Optimized Maintenance Planning for Transmission Power Systems.

Transcription

1 Optimized aintenance Planning for Transmission Power Systems. Improvement of the condition indexing process Name : - Adesh Soemeer Thesis committee: - Prof. dr. Johan Smit - Ing. Peter Oomens (Joulz) - Dr. ir. Dhiradj Djairam - Dr. ir. arjan Popov - Ir. Ravish ehairjan 1

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3 Executive summary Nowadays, one of the major goals of electricity utilities is to optimize the maintenance of the assets which can be achieved, amongst others, by a more effective condition indexing. The maintenance activities and maintenance planning of the grid included in this research are based on the condition indexing process. In this thesis, several process steps included in the condition indexing process will be optimized in order to achieve a more precise condition index of the components. This research is described in seven chapters. Chapter 1: irst, the concept of asset management is introduced. Afterwards, the research problem and objectives of this research are described. urthermore, the scientific challenges, research methodology and scope of research of this thesis are explained. inally, an outline of the chapters for the rest of the thesis is given. Chapter 2: A theoretical description of all the key words used in this research is described. The standard PAS 55 which provides objectivity across many aspects of proper asset management, from lifecycle strategy to everyday maintenance is described. urthermore, asset failures and their risk analysis are described. inally, the condition and the health assessment of components based on previous studies are described based on previous studies. Chapter 3: In order to optimize the condition and health indexing process, the current condition and health indexing process have to be investigated. This chapter will describe the current condition and health indexing process. urthermore, the interpretation of the condition levels and the health index levels are described. Chapter 4: Based on the current model of condition indexing, a number of process steps should be optimized in order to achieve an optimized condition of asset for each component. This leads to an updated model. These process steps and the way they could be optimized are described in this chapter. It is found that the process steps which should be optimized are: the assessment of the visual inspection, the condition function, the interpretation and subdivision of the condition levels and the health index levels, and the determination of the time intervals for the condition indicators. Chapter 5: Using condition function, the condition of asset is determined based on the assessed condition indicators. With the current condition function, the weighting of the condition indicators is excluded. In the updated model, the weighting of the condition indicators will be included in order to determine the condition of asset. In order to determine the weighting of the condition indicators, data is required such as ailure odes Effects Criticality and Analysis (ECA) of components. These data are shown in this chapter. It is determined whether there is a link between the condition indicators and failure modes. There is a link between the condition indicator and the failure mode if the failure mode can be predicted by the condition indicators before its occurrence. The links of the 3

4 condition indicators and failure modes are weighted based on the failure frequency, failure impact and the detection chance. The failure frequency and failure impact are retrieved from the ECA. The detection chance is determined for each link between condition indicator and failure mode. Afterwards, the failure frequency, failure impact and detection chance are categorized. Based on these categorized parameters, risk and criticality matrices are constructed. Based on the criticality of each link, the criticality (weighting) of each condition indicator is determined. As an example, this is performed for the circuit breaker, grounding and control circuit. Chapter 6: A model is designed in order to minimize the subjectivity of the assessment of the visual inspections. The model includes guidelines such as questions, answers and a transformation matrix. urthermore, a strategy is devised for the determination of the condition of the asset by including the weighting of the condition indicator. A new strategy is also determined for the determination of the time intervals for the condition indicators. In this strategy the result and the weighting of the condition indicator are included. inally, the new strategy for the determination of the time intervals is analyzed with the help of a case study. Based on the case study, it can be concluded that a more flexible condition control can be achieved for each component. urthermore, it can be concluded that not for all components there will be savings on the operational expenditures. Chapter 7: In this chapter, the conclusions and recommendations of this research are given and discussed. The recommendations are intended for future research. 4

5 Acknowledgements or the completion of my.sc. graduation research in Electrical Power Engineering, I performed research at the department of High Voltage Technology and anagement. This research describes the optimization of the condition indexing process of electrical components. Writing this research would not be possible without the help of others. I would like to thank my supervisors during this research: Prof. dr. Johan J. Smit, dr.ir. Dhiradj Djairam from the Delft University of Technology, ing. Peter Oomens from Joulz and ir. Ravish ehairjan from the Delft University of Technology and Stedin. I would like to thank Dhiradj Djairam and Ravish ehairjan as my daily supervisors. They gave me very good coaching and also contributed greatly to the completion of my thesis. I want to thank Peter Oomens for arranging the.sc. graduation research at Joulz. I am thankful for the required data he provided me. I am also very thankful for the regular meetings we had every week and for the valuable inputs I acquired from these meetings. I want to acknowledge the aid of Prof. dr. Johan J. Smit for his contributions and discussions during this research. any thanks to dr. ir. arjan Popov for being willing to participate in my final thesis committee and for taking the effort to evaluate my graduation thesis. There are also others who I would like to thank for their contribution during this graduation. I want to thank dr. ir. Sander eijer for giving me information and introducing me to TenneT. I want to thank Ton van de Scheur for sharing his practical experience with me. I want to thank dr. ir. David Tax, who aided me with the complex stochastic concepts. urthermore, I want to thank dr. ir. rank Wester and Wouter van den Akker for sharing information with me. inally, I gratefully acknowledge my friends and families, especially my parents for supporting and motivating me. Without their help and support this thesis would not have been here. Thank you. 5

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7 Table of contents Executive summary... 3 Acknowledgements... 5 Chapter 1. Introduction Overview of asset management Research description Research problem Objectives of the research Scientific challenges Research methodology Scope of research Thesis outline Chapter 2. Introduction to condition and health assessment PAS Asset failures Deterioration ailure risk ailure odes and Effects Analysis (EA) Criticality matrix Bathtub curve Condition assessment Condition indicator easurements Health assessment Comparison of the condition and health assessment aintenance Summary & conclusions Chapter 3. Description of the current condition and health indexing process for the 150 kv grid Transmission networks Health indexing process performed by the TSO Introduction The model including the process steps in order to determine the maintenance Risk analysis aintenance decision Health index levels

8 3.2.6 Possibility of additional maintenance Current maintenance policy Condition indexing process performed by the SP TOR Condition indicator levels and condition levels Summary & conclusions Chapter 4. Approach for the optimization of the process steps included in the condition indexing process Introduction Optimization of the assessment of the visual inspections NEN Assessment of visual inspections for electrical components The general solution Optimization of the condition function Linking of the failure modes and condition indicators (step 1) Determining and categorization of the parameters (step 2) Determining the failure risk of each link (step 3) Criticality matrix (step 4) Strategy for the determination of the condition of asset (step 5) Optimization of the condition levels and the health index levels Updated condition levels Updated health index levels Adjustment of the time intervals of the preventive inspections Summary & conclusions Chapter 5. The weighting of the condition indicators Input data Weighting of the condition indicator for a circuit breaker system The circuit breaker Grounding Control circuit Condition indicators and failure modes of the components Linking of the condition indicators to the failure modes Condition indicator failure mode links for the circuit breaker Condition indicator failure mode links for the remaining components The determination of the detection chance

9 5.4.1 Introduction Determination of the detection chance for each link of the circuit breaker Categorization of the 3 parameters Risk matrix Criticality matrix Weighting of each condition indicator ethodology for determination of the weighting of the condition indicator The determination of the weighting of the condition indicator with the selected method Summary & conclusions Chapter 6. Optimization of the process steps included in the condition indexing process Guidelines for the assessment of the visual inspections Elaboration of the investigated guidelines for a more objective assessment of visual inspections Application of these guidelines for several components Advantages of the new model for assessing the visual inspection Strategy for the determination of the condition of the asset Strategy to determine the condition of the asset Practical example Strategy for the determination of the time intervals Impact of the optimized process steps Case study Summary & conclusions Chapter 7. Conclusions and recommendations Conclusions Recommendations References List of igures List of Tables List of Abbreviations Appendices Appendix A Appendix B Appendix C Appendix D D.1 Circuit breaker D.2 GIS- Circuit breaker

10 Appendix E E.1 Circuit breaker E.2 GIS-Circuit breaker E.3 Grounding E.4 GIS-Grounding E.5 Control circuit Appendix Introduction to the determination of the detection chance Probability of the occurrence of each possible combination ailure probability of each possible combination ormula of the detection chance for each possible combination Appendix G G.1 Circuit breaker G.2 GIS-Circuit breaker G.3 Grounding G.4 GIS-Grounding G.5 Control circuit Appendix H Appendix I Appendix J J.1 The answers and questions J.2 Transformation matrices Appendix K K.1 Circuit breaker K.2 Grounding K.3 Control circuit Appendix L Appendix Appendix N N.1 Scenario 1 and N.2 Scenario 3 and

11 Chapter 1. Introduction Electricity utilities have several goals such as the achievement of reliable performance of their electrical components and cost efficiency in their organization. These goals can be achieved in several ways such as proper maintenance activities and proper maintenance planning. The maintenance of the grid as described in this thesis is based on the condition of the asset (technical condition). In this thesis, the focus will be on the optimization of condition indexing process of components in order to reach the goals mentioned above. Based on the optimized condition indexing process of the components, the maintenance planning can be optimized. An optimized maintenance planning can result in savings on operational expenditures and a more flexible condition control. In section 1.1, an introduction to the asset management is given. Afterwards, the condition indexing process and the maintenance planning are discussed. Section 1.2 describes the research description and objectives of this research. The scientific challenge, research methodology and scope of research are explained in section 1.3, 1.4 and 1.5 respectively. Section 1.6 presents an outline of the remaining chapters for the remainder of the thesis. 1.1 Overview of asset management Electrical power systems consist of transmission and distribution networks for the transport of electrical power from producers to customers. The operation of these networks requires several assets. An asset can be a person, object or other entity with a value that makes monitoring and controlling its usage desirable, in order to achieve the core business objectives. In electrical power systems, we can define asset as the physical component of a manufacturing, production or service facility, which has value, enables services to be provided and has an economic life of greater than one year [1, 22]. Assets can be classified into different types of assets: physical assets, human assets, intellectual assets, financial assets and intangible assets [1]. In this research, assets will be limited to physical assets (components) of a network. Physical assets are assets such as cables, circuit breakers and transformers. This research includes a transmission network with a voltage level of 150 kv. Transmission networks transfers bulk of electrical energy from plant to sub-stations at a voltage level higher than 110 kv. To operate physical assets in a more technically and economically efficient way, asset management is required. In the electrical power business, asset management involves the planning and operation of the electrical power system. Asset management can have different definitions depending on the application. Asset management in the power transmission business can be defined as developing and implementing an integrated set of strategies and decisions as well as managing the relationships with key internal and external parties [1]. Asset management involves of many aspects, for example: life cycle assessment, risk analysis, condition monitoring, maintenance strategies, reliability, financial, legal, regulators etc. Risks have to be taken into account during the implementation of asset management. One of the major risks that are taken into account in asset management is the probability of failure occurrence and its consequences [3]. The goal is to ensure maximum asset value or minimal 11

12 life cycle cost while considering the risk. This can be achieved by proper maintenance planning. Proper maintenance planning can be managed by including several factors such as the condition indexing of components and failure statistics of components. During condition indexing, parameters (condition indicators) indicating the technical condition of the asset are assessed after the execution of preventive inspections (regular maintenance). Based on the assessed condition indicators, the condition of the asset and the health index of a component can be determined. The condition index gives the overall technical state of the asset. The health index gives the state of the asset based on its remaining lifetime. In this thesis, we will make a distinction where maintenance can be divided into extra maintenance and regular maintenance. Regular maintenance involves preventive inspections in order to assess the condition indicators and the required extra maintenance. Extra maintenance is maintenance in order to improve the condition of the asset. Based on the condition of the asset and the health index, the need, amount and planning for extra maintenance can be determined. 1.2 Research description This research involves a part of the 150 kv grid in the Netherlands. The Transmission System Operator (TSO) of this grid is TenneT BV, which is responsible for the operation of the grid. The grid included in this research, is maintained by the Service Provider (SP), Joulz BV. aintenance of this grid is mostly based on the condition of the asset (condition index) and the health index. The condition indexing process is performed by the SP and the health indexing process is performed by the TSO Research problem Currently, the condition of the asset and the health index are determined meeting all of the requirements. However, there are still several shortcomings in condition and health indexing process. As observed from practical experience, there are possibilities to determine the condition of the assets and the health index more effectively. The condition and health indexing process includes a number of process steps. There are several factors in the process steps which can lead to inaccuracy of the determination of the condition of the asset and the health index. The condition and health indexing process includes different process steps such as: assessment of preventive inspection assessment of the condition indicators strategy for determining the condition of the asset (condition function) determination of the time intervals for the condition indicators determination of the remaining lifetime. The factors in the process steps which can lead to inaccuracies in determining the condition and health indexing process are shown in table

13 Table 1.1. actors in the process steps which can lead to inaccuracies in determining the condition and health indexing process are shown. actors The lack of guidelines Description The guidelines that may be absent can be directives for the assessment or determination of process steps such as preventive inspections or condition indicators. Ambiguousness of the interpretation The preventive inspection, condition indicators and condition of asset can be expressed in categories. The interpretation of these categories can be ambiguous. Ageing workforce The assessment or determination of process steps such as preventive inspections or the time interval for these preventive inspections is mostly dependent on workforce. An ageing workforce can lead to a lack of experience in the future. Due to a lack of experience, a less effective condition indexing and suboptimal maintenance planning can be achieved. Using preventive inspections and condition indicators, the condition of the asset and the health index can be determined. With this obtained condition of the asset and the health index, extra maintenance can be planned. If maintenance is performed later than required it can lead to: an increase of asset failures resulting in increased operational expenditures. a late investigation of the condition of the component, which can be deteriorated extremely compared to the last maintenance moment. Performing maintenance earlier than required may also increase the expenditures. Therefore, in order to achieve actual savings on the operational expenditures and a more flexible condition control, optimal maintenance planning is required. The problem definition of this research is schematically shown in figure 1.1. igure 1.1. Lack of guidelines, ambiguousness of interpretations and ageing workforce can lead to a less effective condition indexing, health indexing and regular maintenance planning. Based on the resulting condition of the asset and health index, the extra maintenance can be planned sub-optimally. Suboptimal maintenance planning can result in an increase of the expenditures and a rigid condition control. 13

14 After analyzing the process steps of the condition and health indexing process, a number of shortcomings have been found and investigated at several process steps based on the factors mentioned above. These shortcomings should be minimized or eliminated in order to determine the condition of the asset and health index more effectively. The shortcomings in the process steps are: A number of preventive inspections can be performed by measurements and others can be assessed visually. As observed from practice, the assessment of the visual inspections can be subjective, because of the absence of guidelines. The weighting of the condition indicators are excluded in the determination of the condition of the asset. The condition of the asset is expressed in one out of 4 condition levels. The interpretation of the condition levels is not sufficiently specific. The reason for this is that the condition levels give a wide range of the estimation of the condition of the asset. Different estimations of the condition of the asset can be made by the maintenance planners. This can lead to subjectivity in the planning of the extra maintenance. Assessments of condition indicators are performed repeatedly after a standard time interval. Only when the component is in a critical condition, the time intervals are adjusted based on experience. Even when the condition of the component is not critical, the time interval can be adjusted. There are no guidelines to adjust the time interval for each condition indicator. The health index is expressed in four health index levels. Based on the interpretation of the health index levels, different meanings can be interpreted by the maintenance planners. This can lead to a subjective planning of the extra maintenance Objectives of the research The main purpose of this research is that the condition of the asset and health index of a component should be determined more effectively. This can be achieved by minimizing or eliminating the shortcomings. The approach for the minimization or elimination of the shortcomings is shown here. A model will be developed that will involve concrete questions with standard answers as guidelines, which will minimize the subjectivity of the assessment of the visual inspections. During the visual inspections deviations such as rust and damage are assessed. The questions will be based on the deviations to be focused during the visual inspection. The weighting of the condition indicator can be determined by determining the criticality index of the condition indicators. The criticality index gives the importance of the condition indicator. In this research, the criticality index will be determined based on risk factors such as failure frequency, failure impact and detection chance. A 14

15 strategy will be developed in order to include the weighting of the condition indicators for the determination of the condition of asset. The number of condition levels will be extended in order to achieve a narrower estimation of the condition of the asset. The interpretation of the updated condition levels should give a specific estimation of the condition of the asset. A strategy will be developed in order to determine the maintenance an optimal time interval for each condition indicator. The weighting of the condition indicators will be included in this strategy. The number of health index levels will be extended in order to achieve health index levels which will give each health state more precisely. In this way, an unambiguous interpretation can be achieved for each health index level. Based on the shortcomings in the process steps, the optimization of the condition and health indexing process will be based on quality, objectivity, uniformity and user friendliness. After the optimization, an optimal maintenance planning and thus savings on the operational expenditures and a more flexible condition control can be expected. An overview of the process steps which will be optimized and the parameters on which are focused on in order to achieve the optimizations, are shown in figure 1.2. igure 1.2. The objectives of the research are the achievement of a more effective condition and health indexing of a component. This will lead to optimal maintenance planning, resulting in savings on expenditures and a more flexible condition control. The process steps, condition index levels, visual inspections, condition of asset, time intervals of inspections and the health index levels have to be optimized. In order to achieve the optimizations, the focus will be on quality, objectivity, uniformity and user friendly. 15

16 1.3 Scientific challenges High voltage transmission networks contain many physical assets. Time constraints make it difficult to optimize the condition and health indexing process for the whole population. Based on available ECA data (ailure ode Effect and Criticality Analysis), the relevant components will be selected. Based on the optimizations of the condition and health indexing process for the selected components, can be illustrated how to optimize the condition and health indexing process for the remaining components. Questions which may arise during the optimization are: How can the condition indicators be weighted quantitatively? How can the detection chance be determined for the condition indicator and the related failure modes? How can an optimal maintenance time interval for each condition indicator be determined? 1.4 Research methodology The research can be divided into three main parts. The first part is the theoretical part, the second part is the analysis part and the final part is the case study part. In the theoretical part, a literature review of concepts such as asset management, condition indicators, condition functions, health index levels, PAS 55 and maintenance is performed. The analysis part provides minimization or elimination of the investigated shortcomings of the process steps included in the procedure of the condition and health indexing process. irstly, information such as the current condition and current health indexing process is discussed. Afterwards, the interpretation and subdivision of the condition of asset and health index level will be optimized based on the current interpretations. After interviews with maintenance personnel, questions will be constructed which will help in assessing visual inspections more objectively. urthermore, data such as ECA (ailure ode Effect and Criticality Analysis) of components is collected and analyzed. Subsequently, the weighting of the condition indicators of the selected assets will be determined. This will be achieved by linking the condition indicators to the failure modes included in the ECA. There is a link between condition indicator and failure mode if this particular condition indicator can predict the failure mode before its occurrence. The links will be weighted based on the failure frequency, failure impact and detection chance. Based on the weighted links, the weighting of the condition indicators can be determined. A strategy will be constructed in order to determine the condition of the asset by including the weighting of the condition indicators. inally, a strategy for the determination of the time intervals for the assessment of the condition indicators will be developed. The strategy will be based on the results of the condition indicators and the weighting of the condition indicators. In the case study part, a number of future scenarios will be elaborated in order to verify if savings on operational expenditures and a more flexible condition control are achieved after the application of the new strategy for the determination of the time intervals. 16

17 1.5 Scope of research According to the objectives of this study, the focus will be on several process steps of the condition and health indexing process. These process steps are highlighted in figure 1.3. igure 1.3. Process steps, which will be optimized in this research are highlighted. aintenance and preventive inspections are performed for each asset. Based on the preventive inspections, the condition indicators are assessed. Based on the result of the condition indicators, the condition of the asset and the health index can be determined for the component. The strategy to determine the condition of the asset based on the results of the condition indicators is named condition function. Preventive inspections are performed for each component in order to assess the condition indicators of that component. The assessed condition indicators and the expected remaining lifetime are combined to determine the remaining lifetime. Based on the remaining lifetime of the asset, the health index can be determined. The assessed condition indicators are also used to determine the overall condition of the asset. The condition of the asset and the health index are analyzed considering risk factors (risk analysis) in order to determine the proper maintenance activities. In order to optimize the condition and health indexing process the following process steps will be optimized: - Assessment of the visual inspections. - Condition of asset, by including the weighting of the condition indicators. - The condition index levels, which will be extended. - The health index levels, which will be extended. - Time intervals of the condition indicators. Optimization of the determination of the remaining lifetime and the risk analysis will be excluded in this research. 17

18 1.6 Thesis outline The report is described in seven chapters. Chapter 2 performs a literature review of the key words of the condition and health indexing processes. In this chapter special notice is given to theoretical view of the collected data and information. Also concepts as PAS 55 and maintenance are described. Chapter 3 constitutes an introduction to the current model for condition and health indexing for the 150 kv grid. The interpretation of the condition levels and the health index levels is also shown in this chapter. Chapter 4 deals with approach for the optimization of the assessment of the visual inspection, the determination of the condition of the asset, the subdivision of condition levels and the health index levels, and the determination of the time intervals for the condition indicators. Chapter 5 performs a review of the collected data (ECA). After that the weighting of the condition indicators is determined. This is performed for the circuit breaker, grounding and control circuit. Chapter 6 constitutes a model which is developed to assess the visual inspection more objectively. urthermore, a strategy for the determination of the condition of the asset by including the weighting of the condition indicators is described. Afterwards, a strategy to determine the time interval of the condition indicators is developed. inally, a case study is performed to verify the strategy for the determination of the time intervals. Chapter 7 will give the conclusions and recommendation of this research. 18

19 Chapter 2. Introduction to condition and health assessment A description of all the key topics used throughout this thesis is presented in this chapter. In section 2.1 the standard PASS 55 is described. In section 2.2, asset failures and their analysis methodologies are described. In section 2.3 and 2.4, the condition and health assessment of components are described. urthermore, the difference between the expected remaining lifetime and the remaining lifetime is shown. In section 2.5, a comparison is made between condition and health assessment. In section 2.6, the different maintenance methodologies are described. 2.1 PAS 55 The Transmission System Operator (TSO) has to prove to the regulator (Energiekamer, the regulator of the Netherlands) that the physical assets are performing according to the requirements during its complete lifecycle. This can be proven by fulfilling meeting the requirements of PAS 55. PAS 55 is a standard which specifies the requirements for the management of physical assets and asset systems over their lifecycles. The lifecycle of a physical asset includes the acquiring, owning and ultimately disposing of the physical assets. The TSO has the intention to optimize their condition assessment process, not only for a proper maintenance planning but also for meeting the requirements of PAS 55. PAS 55 provides objectivity across 28 aspects of good asset management, which enables the integration of all stages of the asset lifecycle. The different stages of the asset lifecycle are: the first recognition of a need to design, purchase, construction, commissioning, operation, maintenance and replacement [4, 5]. In this research, the focus will be on the last aspect of the asset lifecycle, maintenance. The maintenance planning will be optimized. This can be achieved by the optimizations of the condition and health indexing process. Optimization of these processes should minimize the occurrence of asset failures. 2.2 Asset failures Asset failure simply means a breakdown or the inability of the component to perform the expected function. Asset failures can be caused due to deterioration of the component or external causes such as interference of animals Deterioration Components are subjected to deterioration as a result of usage and aging. During operation, components undergo electrical, mechanical, thermal and environmental stresses, which lead to a deterioration of the component [6]. urthermore, deterioration also occurs due to components interaction such as chemical reactions. Deterioration occurs over time and can worsen the reliability of the components. Each utility has a certain acceptable range at which the reliability of their components can vary. In order to control if the reliability of the components is within the acceptable limits, preventive maintenance activities can be planned and executed. 19

20 2.2.2 ailure risk ailure risk is a combination of the frequency of the failure and the consequences of the failure. This can be expressed in a formula [7]: ailure risk ailure frequency ailure impact (2.1) These parameters can be determined by a ailure odes and Effects Analysis ailure odes and Effects Analysis (EA) In order to determine the risk of the asset failures of a component, EA is a possibility. EA involves determining the different ways a component might fail (failure modes), and what the consequences might be (failure effects). urthermore, EA determines the probability of the occurrence of each failure mode, as well as the potential severity of consequences. The probability of the occurrence of each failure mode strongly depends on the ageing and degradation state of the asset. Besides the EA, a ailure ode Effect and Criticality Analysis (ECA) is also possible. In this case, the probability or frequency of the occurring failure modes and the consequences of the failure modes are weighted and expressed in a rank. A specific component can be divided into several types of components. ailure modes can differ for the several types of components. ailure modes can be divided in two types of failure modes, namely maintainable failure modes and non-maintainable failure modes. In contrast to maintainable failure modes, non-maintainable failure modes cannot be countered by preventive maintenance [8] Criticality matrix The criticality of a failure mode can be estimated with the help of a criticality matrix which is shown in figure 2.1. The failure frequency and failure impact of the failure mode are categorized. In the criticality matrix, the criticality of the failure mode is given by matching the categorized failure frequency and failure impact. The criticality can be expressed in a category. Based on figure 2.1, the categories are: low, moderate, high and extreme. igure 2.1. Criticality matrix in order to determine the criticality of a failure mode by combining the failure frequency and failure impact. The criticality is categorized: low, moderate, high and extreme. 20

21 2.2.5 Bathtub curve The bathtub curve illustrates the failure rate of a population of components, during its lifetime. The bathtub curve consists of 3 periods [9]: The first period has a decreasing failure rate, known as early failures. The second period has a constant failure rate, known as random failures. The third part has an increasing failure rate, known as wear-out failures. The bathtub curve can vary for each component. In figure 2.2, 4 different bathtub curves are shown. The three periods of each bathtub curve are also shown in figure 2.2. igure different bathtub curves are shown in the figure (blue, green, brown and orange). The 3 periods of each bathtub curve are also shown in the figure, namely decreasing failure rate, constant failure rate and increasing failure rate. 2.3 Condition assessment In power systems, physical assets are included which can fail in its functioning (failures). The frequency and impact of these failures have to be minimized where possible. This can be achieved by preventive maintenance activities. The key for the development of the appropriate maintenance activity is condition assessment (condition indexing). Condition assessment is the process of transferring diagnostics information about an asset in a condition index [10]. The condition index gives the overall technical condition of the asset. The condition of the asset (condition index) provides an indication of the asset failure probability and its accompanied risk [11]. Parameters of the asset indicating diagnostic information of the component are monitored in order to determine the condition of the asset. By monitoring these parameters, significant changes of the diagnostics indicating degradation are noticeable. Based on the degradation, developing failures can be predicted. In order to prevent these failures, maintenance activities can be performed. Traditionally, maintenance activities are designed by expert operators based on analysis of data such as: historical statistics of the occurring failures. In recent years, the operators of high voltage grids are optimizing their maintenance activities by the application of condition indexing. 21

22 2.3.1 Condition indicator The condition of assets can be determined with the help of condition indicators. The condition indicators are assessed by preventive inspections which give the degradation and ageing information of the asset. Preventive inspections are assessed by comparing the measured data with expected data of such a component. The requirements for recording and registration of condition indicators of the assets of the transmission network in the Netherlands are described in the technical maintenance guidelines of TenneT (TOR 2.1) easurements As described before, a number of preventive inspections have to be performed in order to assess the condition indicators. Preventive inspections can be performed through measurements or visual inspections. easurements can be performed in different ways, for example by lab testing, field measures or simulations. easurements can be continuous or periodic. During continuous measurements, data can be collected continuously while the component is electrically energized and in service [12]. During periodic measurements, the data can be collected periodically, depending on when the measurement is executed. During a periodic measurement, the component usually has to be taken out of service. Visual inspections can be performed by observations made by maintenance personnel. These maintenance personnel can also make use of equipments such as cameras and noise detectors. 2.4 Health assessment The health index gives the condition of an asset based on the remaining lifetime of the component. The remaining lifetime of the component is determined based on the assessed condition indicators and historical data such as failure statistics are included. The definition of health index is: the ability to get a reliable and convenient indication of the health of the transmission network components [8]. The procedure of health indexing can differ for each utility. To gain more insight into the health index, the health assessment (health indexing) as performed by TenneT will be described. The health index can be expressed in four levels: good, fair, poor and end of life. As in the condition assessment process, the condition indicators are assessed based on preventive inspections. The assessed condition indicators and the expected remaining lifetime are analyzed with the help of failure statistics in order to determine the remaining lifetime. The expected remaining lifetime is the remaining lifetime based on component data such as the installation year and technical lifetime (determined by the manufacturer) of the component, while the remaining lifetime is based on a combination of the remaining lifetime based on component data and the remaining lifetime based on the condition data of the component. Based on the remaining lifetime, the health index of a component can be determined. The difference between expected remaining lifetime and the remaining lifetime is illustrated in table

23 Table 2.1. Comparison of the expected remaining lifetime and the remaining lifetime. The process steps which are included for the determination of the expected remaining lifetime and the remaining lifetime are shown. Data Process steps Expected remaining lifetime Population data ailure statistics X Component data Year of installation X X Component data Technical lifetime X X Condition data Condition indicators X Remaining lifetime 2.5 Comparison of the condition and health assessment Different process steps are included in the procedure of the condition and health assessment. Several process steps are included in both, the condition and health assessment. Based on the process steps, a comparison of the condition assessment and the health assessment is shown in table 2.2. Table 2.2. Comparison of the condition and health assessment. The process steps which are included in the condition and health assessment process are marked. Process steps Condition assessment Health assessment Preventive inspections X X Condition indicators X X Condition of the asset X ailure statistics X Expected remaining lifetime X Remaining lifetime X Health index X The health index and condition of asset are the input for the risk analysis. Based on the risk analysis, proper maintenance can be determined and planned. 2.6 aintenance The basis for maintenance is to preserve and protect. The aim of maintenance activities is to continuously meet performance, reliability, economic requirements and customer requirements. Different maintenance methodologies can be applied based on the aim of the maintenance. The different maintenance methodologies are shown in figure

24 igure 2.3. Classification of the maintenance methodologies. There are three maintenance methodologies, namely: Corrective aintenance, Condition Based aintenance and Time Based aintenance [14]. In this research, extra maintenance depends on the condition of the asset (Condition Based aintenance). Condition Based aintenance (CB) is a methodology that strives to identify incipient faults before they become critical. CB enables a more tailor-made planning of the preventive maintenance. Beside the maintenance methodology, there are maintenance strategies, namely [1]: Status related maintenance. Risk based maintenance. Reliability centered maintenance. 2.7 Summary & conclusions After studying the theoretical description and the subjects included in this thesis, it can be concluded that many studies are ongoing on condition and health assessment. any companies are transforming to condition and health assessment of their components in order to optimize their maintenance. Besides condition and health assessment, the failure risk or reliability of the component needs to be determined and analyzed in order to determine the maintenance activities. Utilities optimize their maintenance in order to minimize the asset failures. oreover, in this way, they prove to the regulator that the physical assets are performing according to the requirements during its complete lifecycle. Both the condition and health assessment process give the condition of the asset. The condition assessment gives the technical condition of the asset, while the health assessment gives a condition of the asset based on its remaining lifetime. The theoretical description of the condition and the health assessment process becomes clear. This will facilitate the investigation of the current condition and health indexing process at TenneT and Joulz, which will be described in the next chapter. 24

25 Chapter 3. Description of the current condition and health indexing process for the 150 kv grid In order to optimize the condition and health indexing process, the current condition indexing and health indexing process have to be investigated. This chapter will describe the current condition and health indexing process. Section 3.1 provides the transmission networks in the Netherlands. Section 3.2 provides the health indexing process which is performed by the TSO. urthermore, the determination of the maintenance activities is described. The maintenance activities are determined based on the health index and the condition of asset. In order to determine the health index, the assessed condition indicators have to be included. The condition indicators and condition of asset are assessed by the SP. In section 3.3, the condition indexing process which is performed by the SP is described. 3.1 Transmission networks Transmission networks transfers bulk of electrical energy from plant to sub-stations at voltage levels higher than 110 kv. A well planned, properly designed and well maintained transmission network can provide reliable and quality power supply to customers [23]. The transmission lines, underground cables and various components such as transformers, circuit breakers, insulators and arrestors form a major part of a transmission network. The power network in the Netherlands consists also of a transmission network which has grown to an integral network. The first transmission connection was made in 1931 between generation stations in riesland en Groningen [23]. Besides the growing dependency on electricity, the distances of transmission and the demand for transmission capacity also increased. This has led to the construction of an integral high voltage network. TenneT has the responsibility to connect all new initiatives such as new plants and substations to the electricity grid. Newly connected power plants may however not be allowed to endanger the security of supply. TenneT is also required to maintain the proper functioning of the grid. TenneT operates 380 kv, 220 kv, 150 kv and 110 kv grids on a national scale [19]. The total length of the high voltage network is approximately 3400 km [23]. These grids are shown in figure A.1 (appendix A). Depending on the capacity of power plants, power plants are connected to the 110kV, 150 kv, 220 kv and the 380 kv networks. The 150 kv grid This research involves a transmission network, which is part of the 150 kv grid in the Netherlands. The part of the 150 kv grid which is included in this research consists of: The Stedin High Voltage network. The Stedin Cross Border Leases (CBL) grid. 25

26 3.2 Health indexing process performed by the TSO Introduction The TSO determines the health index in order to [7]: support the decision process, for maintenance activities. achieve ranking of the assets based on their remaining lifetime. prove to the regulator (Energiekamer) that the asset management department operates according to requirements, which can be imposed by standards such as PAS The model including the process steps in order to determine the maintenance Currently, the TSO determines the health index on the basis of [17, 19]: failure statistics of a mixed-population. At time of writing, the numbers of failures per subpopulation are relatively low, therefore, no valid analysis can be made based on the failures statistics of the subpopulation. TenneT makes use of failure statistics of mixedpopulation to determine the remaining lifetime of a component. the expected remaining lifetime of the component based on component data such as the technical lifetime (determined by the manufacturer) and the installation year (based on the year of installation of the asset or manufacture). the condition indicators of the components (condition data). The condition indicators of the components are assessed and delivered by the SP to the TSO. Based on these condition indicators, it can be concluded whether the condition of an asset matches the expected condition of that asset. the data availability and quality of the condition indicators. Not all condition indicators can be supplied or are well-known. It can also occur in the future that inspections are not performed on time and thus the available condition information is outdated. Combining the condition indicators (component based data), the expected remaining lifetime (component based data) and failure statistics (mixed-population based data), the remaining lifetime (component based) can be determined. The data availability and quality determine the balance of contribution between expected remaining lifetime and the condition indicators. In the case of high data availability and quality, the condition indicators dominate in the determination of the remaining lifetime. In figure 3.1, the health indexing process (the green and orange arrows) is shown. The condition indexing process (the green and purple arrows) is also shown. urthermore, the level (component or population) on which the process steps are based is shown. The condition of the asset and the health index both form important inputs for the risk analysis. aintenance activities are based on the risk analysis, therefore, there is a reverse loop in the diagram from the risk analysis to the maintenance activities. 26

27 igure 3.1. The health index is based on the remaining lifetime which in turn is based on the expected remaining lifetime and the condition indicators. The condition of asset is based on the results of the condition indicators. The health index, condition index and business values such as financial restrictions are the input for the risk analysis. Based on the risk analysis, the maintenance activities can be determined Risk analysis Together with the analysis of the health index and the condition of the asset of a component, the maintenance planner includes risk factors to make a well-founded decision with respect to the maintenance activities of that component. The risks associated with failure of these assets need to be accounted in the maintenance decisions. Risk can be seen as a combination of the failure frequency and the failure impact. Based on the health index and the condition of asset, an estimation of the number of failure frequency can be achieved. The impact of failure of a component is determined based on business values such as safety, quality of supply, financial, reputation, customers, environment and compliance [7]. The business value financial is based on the financial costs. The financial costs can be divided in OPEX (Operating Expenditures) and CAPEX (Capital Expenditures). OPEX is the operational costs of a fixed asset. CAPEX are incurred when a business spends money to buy acquire fixed assets. Total expenditures (TOTEX) are equal to the sum of CAPEX and OPEX [18]. The decision for extra maintenance depends mostly on the OPEX. The decision to perform replacement of a component depends on the balance between OPEX and CAPEX. In this research, the condition and health indexing process will be improved. This can lead to an optimized maintenance planning of the component. An optimized maintenance planning should minimize the OPEX of a component given a certain period aintenance decision Using the risk analysis, the maintenance activities for the components can be determined. Three different maintenance decisions can be taken: Regular maintenance There will be no extra maintenance until the next regular maintenance. Additional maintenance There will be extra maintenance before the next regular maintenance. Based on practical experience, it will be determined when exactly the extra maintenance will be performed. 27

28 Replacement The component must be replaced. Based on practical experience, it will be determined when exactly the replacement will be performed. The decision to perform additional maintenance on a component is made by the TSO. In some cases when the cost for the additional maintenance is below a threshold value, the SP is allowed to decide whether to perform the additional maintenance. This threshold value is determined by the TSO Health index levels A health index can be categorized in different levels. These levels provide an estimate to what extent the components meet the technical assumptions within a viewing period of three or seven years [17]. The possibility of additional maintenance is also included in the health index. According to TenneT, the current health index levels are shown in table 3.1 [17]. Health index levels Good air Poor End of Life Table 3.1. The health index levels and related interpretation are shown. Interpretation The expected technical condition meets the technical assumptions within a viewing period of seven years, provided the regular maintenance activities are carried. The expected technical condition does not meet the technical assumptions within a viewing period of seven years, but with additional maintenance it can be returned to the good health index. The expected technical condition does not meet the technical assumptions within a viewing period of seven years. The expected technical condition cannot be increased with additional maintenance to the good health index. The expected technical condition no longer meets the technical assumptions within a viewing period of three years. 28

29 The current health index levels are shown in figure 3.2 (RL = Remaining lifetime). igure 3.2. Current health index levels of TenneT based on the remaining lifetime and the possibility of additional maintenance [13]. The remaining lifetime is categorized to standard remaining lifetime intervals, namely: RL > 7 years, 3 < RL < 7 years and RL < 3 years Possibility of additional maintenance As observed from practice, the possibility of additional maintenance can be determined by 3 parameters, namely: Spare parts and tools The spare parts and the tools to perform the maintenance activities should be available. Technical specifications The technical specification to perform the maintenance activities should be available. aintenance personnel aintenance personnel should have the knowledge and experience to perform the maintenance activities Current maintenance policy The maintenance of the high voltage transmission network of TenneT can be divided into [19]: Preventive maintenance Regular maintenance (maintenance coupled with periodic inspections) ollow-up actions from the two above mentioned maintenance activities Corrective maintenance. Those maintenance activities are based on ECA (ailure ode & Effects Analysis, assessed), in which the risks of asset failure at component level are analyzed. In order to reduce such risks, additional maintenance can be performed after regular maintenance. Risk analysis will determine the extent of the additional maintenance. Besides the regular maintenance and preventive maintenance, there are unforeseen activities, named corrective maintenance. Corrective maintenance is applied when unexpected asset failures have occurred. Components causing failures (both large and small failures) are repaired or replaced. Once failures have occurred, it should be considered whether these unexpected failures can occur with other components. This can be achieved by interference studies (Root Cause Analysis) applied to the failed components. 29

30 The maintenance activities are evaluated periodically with the feedback of the preventive maintenance and the regular maintenance. If necessary, the maintenance activities can be adapted after the evaluation of the feedback and failure analysis [19]. 3.3 Condition indexing process performed by the SP Joulz performs the maintenance activities and preventive inspections for a part of the 150 kv grid. The preventive inspections can be performed by measurements or visual observations. The accuracy of the assessment of visual inspections can be influenced based on the experience of the maintenance personnel. During the regular maintenance preventive inspections are performed and if required additional maintenance is performed. Additional maintenance could be e.g. the cleaning of brushes or the refreshment of oil. Each preventive inspection is categorized: good, fair, poor and bad. The condition indicator can be categorized in the condition indicator levels. This can either occur using descriptive qualifications such as good, fair, poor and bad or a numerical score such as 0.5, 1.0, 1.5, etc. In the case, that the condition indicators are expressed as a score, they have to be transformed to the levels: good, fair, poor or bad. The condition indicators are assessed after a basic time interval (BTI), which is given in TOR 2.1 (discussed in section 2.3.1). The condition indicator is assessed, based on the results of the related preventive inspections. The worst level of the preventive inspections will be the condition indicator level. After the assessment of the condition indicators, the results are reported to the TSO. After the assessment of the condition indicators for each component, the overall condition of the asset can be determined with the aid of a condition function. The condition of the asset is expressed in a level, named condition level (not to be confused with condition indicator level). The condition levels are: good, fair, poor and bad. Currently, based on the condition function, the overall condition of the asset is equal to the level of the condition indicator with the worst level, independent of the weighting of the condition indicator. Thus, for example, an asset with 10 condition indicators of which 9 are found to be good, can still be rated poor if the 10 th condition indicator level is poor. The model in figure 3.3 shows the process steps which are performed by the SP (green frame) and the TSO (yellow frame). 30

31 igure 3.3. rames indicating the process steps in the model performed by the SP (green frame) and the TSO (yellow frame). The condition indexing process is performed by the SP. The condition indicators and condition of asset are delivered to the TSO. The TSO performs the health indexing process TOR 2.1 aintenance activities and preventive inspections which are performed for all the assets of the 150 kv grid, are given in the Technical aintenance Directives or TOR 2.1 (Dutch: Technische Onderhoudsrichtlijnen). TOR 2.1 is a document which is published by the TSO, TenneT. TOR 2.1 describes the regular maintenance activities of high voltage networks which are in management or ownership of TenneT. The preventive inspections and maintenance activities given in TOR 2.1 are based on the ECA (ailure ode and Effect Analysis). TenneT expects skill and expertise of the SP to perform the maintenance activities and preventive inspections as efficiently and cost-effective as possible without compromising the quality [20] Condition indicator levels and condition levels According to TOR 2.1, the current interpretation of the levels of the preventive inspection, condition indicator and condition of the asset are [20]: Good: the asset is in a good condition. No additional maintenance or inspection is required until the next regular maintenance. Also at the next regular maintenance activities no special details are expected. air: the asset still has a capable state. No extra maintenance is required until the next regular maintenance. However, a significant deterioration of the asset is noticeable. Poor: the asset has a moderate to poor condition but is still expected to be a few years in function. The condition is so deteriorated that additional maintenance or further research is needed before the next regular maintenance. Bad: the asset has a very poor condition and needs short-term additional maintenance, replacement or further research to be able to fulfill its function. 31

32 3.4 Summary & conclusions In this chapter, the condition and health indexing process for the grid included in this research becomes clear. The condition indexing process and the health indexing process are connected to each other. The SP performs the condition indexing process, while the TSO performs the health indexing process. The SP delivers the assessed condition indicators to the TSO. The TSO includes the assessed condition indicators in the determination of the health index. The condition of asset and the health index are included in the risk analysis in order to determine the maintenance activities and maintenance planning. Based on the problem definition of this research, the focus will be on the condition indexing process, part of the health indexing process and determination of the maintenance planning. Several process steps can be optimized. These process steps have shortcomings such as: the absence of guidelines in order to perform the aspect the existing guidelines (interpretations) can interpreted ambiguously dependency of workforce which can age in the future. the exclusion of weighting factors of several process steps. All the process steps that can be optimized in the condition and health indexing process will be discussed in the next chapter. In order to optimize those process steps, an approach will be devised for each process step. 32

33 Chapter 4. Approach for the optimization of the process steps included in the condition indexing process The shortcomings at several process steps included in the current condition and health indexing process should be minimized or eliminated in order to achieve an optimized condition of asset and health index for each component. The process steps and the related shortcomings are described in section 4.1. The approach of the optimizations of the several process steps are described in section 4.2, 4.3, 4.4 and 4.5. The approach for the optimization of the assessment of the visual inspection is described in section 4.2. The approach is based on an existing methodology, namely NEN Based on this methodology, guidelines such as questions, answers and a transformation matrix are included for the development of a new model. Section 4.3 describes the approach for the optimization of the condition function. The condition function is a strategy which determines the condition of asset based on the results of the condition indicators. The approach is based on the inclusion of the weighting of the condition indicators in the condition function. In section 4.4, the condition levels and the health index levels are updated. After the update of these levels, the interpretation and subdivision of the levels are optimized. Section 4.5 describes the approach for the determination of the time interval of the condition indicators. The approach is based on a strategy which includes the result and weighting of the condition indicators in the determination of the time intervals. 4.1 Introduction The process steps in the condition and health indexing process which can be optimized are: The assessment of the visual inspections. The condition function. The condition levels. The health index levels. Time intervals at which the condition indicators are assessed. Linking the process steps on which will be focused in this research, results in a model which is schematically shown in figure 4.1. This model is named current model. After optimization of the current model, this model will become the updated model. 33

34 igure 4.1. Process steps which will be optimized are shown in the current model. Each asset has a set of preventive inspection, which is performed after a specific time interval. Based on the preventive inspections, the condition indicators are assessed. With the aid of a condition function, the results of the condition indicators can be transformed to the condition of the asset. The condition indicators are also involved to determine the health index. The shortcomings in the process steps of the condition and health indexing process are: Visual inspections: The only guideline for the assessment of the visual inspection is the interpretation of the 4 categories, namely: good, fair, poor and bad. The interpretation of the categories can be ambiguous. As observed from practice, the assessment of the visual inspections can be subjective. The assessment strongly depends on the observation of the maintenance personnel. Due to this subjectivity, the quality of the assessment of the preventive activities can be diminished. Based on the current methodology of the assessment of visual inspections, the maintenance planner cannot always determine the optimal maintenance activity for a component or sub-component. Condition function: The weighting of the condition indicators is excluded in the determination of the condition of the asset. Currently, the overall condition of the asset is equal to the level (result) of the condition indicator with the worst level. Situations could arise where this level does not necessarily reflect the overall condition of the asset. or example, in the case when the worst condition indicator is not critical for the overall functioning of the asset. Condition levels: Even when the condition of the asset is determined accurately, it still can be difficult for the maintenance planner to schedule the required maintenance. This is due to the fact that the interpretation of the condition levels is not sufficiently specific which lead to a wide range of the estimation of the condition of the asset. The interpretation and subdivision of the condition levels do not give each state of the condition of an asset precisely: 34

35 o The poor level gives that the condition of the component can be poor or moderate and that the component requires additional maintenance. It is not clear when the condition is poor and when moderate. o The level bad includes the requirement of both additional maintenance and replacement. Based on this classification, it is not clear when replacement is required. These problems are highlighted in figure 4.2. The current condition levels are plotted in a matrix based on their interpretations. The question marks show the states of an asset that are currently not or insufficiently classified by a condition level. igure 4.2. atrix indicating the current condition levels based on its interpretation, namely the states of the condition and the requirement of additional maintenance. The interpretation of the condition levels can be ambiguous, because of the broad indication of the condition. The question marks show the shortcomings in the current matrix. Health index levels: In the health indexing process, the focus will be only on the optimization of the interpretation and subdivision of the health index levels. In order to perform specific targeted maintenance activities, the health state of the asset should be given precisely with the aid of the interpretation of the health index level. Currently, not all the health index levels give an precise representation of the health of the asset: o The fair level gives two states of the health of an component, namely: Assets with a remaining lifetime between three and seven years with the possibility of additional maintenance. 35

36 Assets with a remaining lifetime less than three years with the possibility of additional maintenance. Therefore, the maintenance planner cannot ascertain which of the two above-mentioned states is applicable when a fair level is obtained. o The good level does not indicate if additional maintenance is possible or not, while it is not obviously that a component with a remaining lifetime greater than seven years do not need additional maintenance. The current health index levels are plotted in a matrix based on their interpretations (see figure 4.3). The levels with shortcomings are shown by question marks. igure 4.3. atrix indicating the health index levels based on the remaining lifetimes and possibility of additional maintenance. The question marks show the levels with limitations in the matrix. air gives two states of the health. It is not clear if addition maintenance is possible for good. RL means remaining lifetime. Time intervals: As prescribed in TOR 2.1, time intervals for performing preventive inspections are constant. In cases where the condition of components is critical, the time intervals are reduced based on experience. Cases also exist where time intervals could be larger than given in TOR 2.1 without detrimental effects. There are no guidelines for adjusting the time interval in consistent and predictable manner. This will pose a problem, since an ageing workforce leads to a decrease in experience. This, in turn, can lead to incorrect adjustments of the time intervals. 36

37 4.2 Optimization of the assessment of the visual inspections In order to achieve a model for a more objective assessment of visual inspections, the assessment of visual inspections for other type of components will be analyzed first. It is interesting to study similar processes for other components, for example, visual inspection of infrastructural components such as bridges and pavements. In order to study the assessment of visual inspection of infrastructural components, the norm NEN 2767 will be analyzed. As observed from practice, this norm is successfully applied NEN 2767 NEN 2767 is a norm that allows to manage buildings properly and efficiently, ensure building and installation quality, test maintenance objectives and minimize financial risks. This norm involves 4 parts, namely [21]: ethodology for measuring the condition of building and installation parts (NEN ): this part describes the method to achieve a uniform rule for the technical condition of building and installation parts. This is based on registration of defects. Defects are circumstances of a construction or installation part where the (technical) condition is at a lower level than the (technical) condition that was intended for delivery. Defect lists for measuring the condition of building and installation parts (NEN ): in this part of the standard, defect lists are recorded. The defect list includes construction or installation parts with the most occurring defects and the importance of each part. Aggregation condition scores to a technical index for measuring the condition of building and installation parts (NEN ): this part contains calculation rules to each building component to achieve a technical index for buildings. The score gives a complete picture of the technical condition and makes comparison possible between buildings. Condition measurement of infrastructure (NEN ): this part is a methodology to assess the condition of infrastructural components in an objective and unambiguous determination. According to NEN 2767, in order to assess the condition of a building or installation part, 3 parameters have to be combined [22]: Importance - degree of influence of a defect in the functioning of a building or installation part. Each element of the components is weighted based on the importance. The weighting factor has 3 levels: o High (Weighting 1.0) o Average (Weighting 0.8) o Low (Weighting 0.5). Intensity - Indicator which shows at which stage a defect is located: early, advanced or late stage. 37

38 Size - Net amount at which a defect manifests itself in relation to the total consideration amount of a building or installation part. The size of a defect is determined by the surface of the defect with respect to the entire surface of the inspected component. The size is decided in the field (by the inspector). The 5 degrees of magnitude are: 1. The defect is occasionally (<2%) 2. The defect occurs locally (2% to 10%) 3. The defect occurs frequently (10% to 30%) 4. The defect occurs significant (30% to 70%) 5. The defect is common for the absence (> 70%). Condition Condition gives the technical condition or state of a building or installation part. The condition score is a result of the defect parameters: size, intensity and importance. The condition score ranges from 1 to 6: 1. Excellent condition (occasional minor defects) 2. Good condition (incidentally beginning aging) 3. Reasonable condition (mostly visible aging, function of components not in danger) 4. oderate condition (function of components occasionally in danger) 5. Poor condition (the aging is irreversible) 6. Very poor condition (technically ready for demolition). With the help of this norm, an approach can be constructed for the optimization of the assessment of the visual inspections for electrical components Assessment of visual inspections for electrical components According to TOR 2.1, there are 60 condition indicators which can be assessed for different electrical components or sub-components. An example of condition indicators with their related units and categories is shown in table 4.1. Table 4.1. Different condition indicators as prescribed in TOR 2.1 and their units or categorized levels. Condition indicators Unit Levels Condition indicator 1 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0 Condition indicator 2 % Good, air, Poor, Bad Condition indicator 3 [µl/l] Condition indicator n PC Good, air, Poor, Bad Of those 60 condition indicators, 52 condition indicators can be measured and 8 condition indicators are determined by visual inspections. The 8 visually inspected condition indicators are shown in table

39 Table 4.2. The visually inspected condition indicators. Of the 8 condition indicators, several are completely based on visual inspections and others are based on a combination of visual inspections and measurements. A number of the 8 condition indicators can be assessed for different components or sub-components. These condition indicators are: Overall condition Condition of additional function Condition function and signaling Leakages. Determining a separate solution for each condition indicator and each component is a timeconsuming process. A general solution including guidelines for the assessment of visual inspections will be developed which will be applicable for all visual inspections given in TOR The general solution Condition indicators Overall condition Condition of additional function Condition function and signaling Leakages Rust of power transformer echanically damage of power transformer Shifts The focus of the genaral solution for the reporting of the visual inspections will be objectivity, quality, uniformity and user friendliness. As observed from practice, a number of standard questions have to be answered during the visual inspections. This coresponds to NEN 2767 were 2 questions have to be answered, namely: What is the size of the defect? What is the intensity of the defect? The questions includes parameters which gives the severity of the defect. These questions will be used as guideline. The answers to these questions should also be standard, therefore, a number of concrete aswers will be provided. The idea is to transform the answers to the categories good, fair, poor or bad by employing a matrix. The questions, concrete aswers and the transformation matrix will be the guidelines for the assessment of visual inspections. The quidelines are shown in a flowchart (see figure 4.4). The new model for the assessment of the visual inspections will be illustrated for the circuit breaker, grounding and control circuit. 39

40 igure 4.4. lowchart indicating the guidelines in order to assess the visual inspections. The guidelines consist of standard questions, concrete answers and a transformation matrix. 4.3 Optimization of the condition function The condition function transforms the results of the condition indicators to the overall condition of the asset. The weighting of the condition indicators is not taken into account in the determination of the condition of the asset. It will be investigated if the weighting should be included in this process. The weighting of each condition indicators will be determined. If the inclusion of the weighting of the condition is shown to be significant, a strategy will be designed in order to include the weighting of the condition indicator to make a narrower estimation of the condition of the asset. These steps that have to be followed in order to optimize the condition function are shown in figure

41 igure 4.5. lowchart including steps to be followed for the determination if the weighting of the condition indicators and optimization of the condition function by including the weighting of the condition indicators. The condition indicators and failure modes of a component are linked to each other. Each link will be weighted based on the parameters, failure frequency, failure impact and detection chance. These 3 parameters will be transformed to a weighting with the help of a risk matrix and a criticality matrix. A strategy will be constructed to include the weighting of the condition indicator in the determination of the condition of the asset. In order to weight each condition indicator, the criticality of the condition indicator will be determined. irst, the condition indicators will be linked to the failure modes. There is a link between the condition indicator and failure mode if the failure mode can be predicted by the condition indicator. After that, the failure risk and criticality for each link between condition indicator failure mode will be determined. The failure risk includes the failure frequency and the failure impact. The criticality index includes two parameters, failure risk and detection chance. The failure risk and criticality index can be determined by a matrix. With the aid of a strategy the criticality (weighting) of each link will be included to determine the overall criticality (weighting) of the condition indicator. The weighting of each condition indicator has to be determined only once and can be entered into TOR 2.1 or onto the inspection sheets. The flowchart of figure 4.5 can be divided in five steps: Step 1: Linking of the condition indicators to the failure modes. Step 2: Determining and categorization of the parameters; failure frequency, failure impact and the detection chance for each link. Step 3: Determining the risk of each link with the aid of a risk matrix. Step 4: Determining the criticality of each link with the help of a criticality matrix and the construction of a methodology for the determining of the overall criticality (weighting) of each condition indicator. 41

42 Step 5: Determining the condition of the asset with the help of a strategy which will include the weighting of the condition indicators. The determination of the weighting of the condition indicators will be performed for the circuit breaker, grounding and control circuit Linking of the failure modes and condition indicators (step 1) or each component the condition indicators and failure modes are listed in table 4.3. The condition indicators are shown in TOR 2.1 and the failure modes can be extracted from a ECA. The condition indicators will be linked to the failure modes based on: the causes of the failure the sub-component related to the failure mode and condition indicators the interpretation of the failure modes and condition indicators. Table 4.3. Example of a table in which failure modes and condition indicators can be linked. X means that there is a link between the condition indicator and the failure mode, and means there is no link between the condition indicator and the failure mode. Linking and CI Condition indicators ailure modes n Weighting CI CI 1 X X?? Weighting CI 1 CI 2 X X X?? Weighting CI 1?????? CI n?????? Weighting CI n An X means that a link between the condition indicator and the failure mode was found. Conversely, a means no link between the condition indicator and the failure mode. or the determination of the criticality index of the condition indicators, only the maintainable failure modes will be included, because the condition indicators only can predict the maintainable failure modes Determining and categorization of the parameters (step 2) After linking the condition indicators to the failure modes, the links can be weighted. 3 parameters are selected to weight the link quantitatively. These parameters are: ailure frequency The number of occurance of a failure mode in a specific period. ailure impact The consequence of a failure mode, which can be determined by business values such as safety and costs. Detection chance The probability that a condition indicator can indentify a specific failure mode. 42

43 igure 4.6. Parameters determining the weighting of a link between condition indicator and failure mode. These parameters can be categorized. ailure frequency and failure impact can be combined to one parameter (failure risk) with the help of a risk matrix. Assuming that the detection chance can vary between 0 and 1 and the failure risk between 1 and 100, the parameters can be categorized equally as shown in table 6. The assumption of the range of the detection chance and the failure risk are made in order to illustrate an example of the categorization. Table 4.4. Example of the categorization of the parameters detection chance and failure risk. The detection chance vary between 0 and 1, and the failure risk between 1 and 100. The parameters are equally categorized. Ranks Detection chance (0 1) ailure risk (1 100) 1 0-0, ,21-0, ,41-0, ,61-0, , Determining the failure risk of each link (step 3) At the third step, the parameters failure frequency and failure impact can be combined. This combination provides the failure risk. The failure risk can be determined with the help of a risk matrix (see figure 4.7). The failure risk is classified in 5 categories, namely: A, B, C, D and E. The shown matrix and categories are an example to illustrate the risk matrix. The failure risk will be used as input for the criticallity matrix. ailure frequency ailure impact A A B B C 2 A B B C C 3 B B C C D 4 B C C D E 5 C C D E E igure 4.7. Example of a risk matrix. Two parameters, failure frequency and failure impact are combined. The risk can be expressed in five categories: A, B, C, D and E. 43

44 4.3.4 Criticality matrix (step 4) Step 4 is divided in two steps, namely step 4a and step 4b. Step 4a The criticality of each linked condition indicator - failure mode will be determined by matching the failure risk and detection chance in the criticality matrix (see figure 4.8). The criticality is classified in 3 categories, low, medium and high. The shown matrix and categories are an example to illustrate the criticality matrix. Detection Chance ailure risk A B C D E 1 Low Low High High High 2 Low Low High High High 3 Low oderate High High High 4 oderate oderate oderate High High 5 oderate oderate oderate High High igure 4.8. Example of a criticality matrix. 2 parameters, failure risk and detection chance are combined. Step 4b A methodology will be designed in order to determine the overall criticality of the condition indicator based on the criticality of each link. Step 1 to step 4 is performed in chapter Strategy for the determination of the condition of asset (step 5) Based on the current approach, the overall condition of the asset is equal to the level of the condition indicator with the worst level. As stated before, this does not necessarily reflect the actual overall condition in reality which may lead to over-expenditure on maintenance costs. A new strategy for a narrower estimation of the condition of an asset will be designed in section

45 4.4 Optimization of the condition levels and the health index levels The condition indicator levels, condition levels and health index levels are shown in table 4.5. Table 4.5. Categorization of the condition indiators, condition of asset and health index. Condition indicator levels Condition levels Health index levels Good Good Good air air air Poor Poor Poor Bad Bad End of Life In the updated model, the condition indicators will be categorized using the levels shown in table 4.5. However, the condition level and the health index levels will be optimized Updated condition levels In order to gain more insight into the states of each condition level, the levels are projected in the bathtub characteristic. The bathtub curve is used, because the failure rate of a component is shown during its lifetime. As observed from practice, the condition level can be expressed in the failure rates. A typical bathtub curve is used to illustrate the expression of the condition levels. Using the interpretations, a projection on the bathtub curve is made. or example, the interpretation of bad indicates that the asset has a very poor condition. It is assumed that the failure rate is high for the bad level. The projection of bad is shown on the curve were the failure rate is high. igure 4.9. Condition levels projected on the bathtub curve. The interpretation of these condition levels has been described in section rom the interpretation of the condition levels (section 3.3.2) and the example of the bathtub curve (figure 4.9), it can be concluded that each level gives an estimation of the condition of the asset that ranges broadly. In order to reach a narrower estimation of the condition of the asset and an unambiguous interpretation of the levels, a new division of the condition levels have to be constructed. The updated condition levels are derived from the constructed matrix in figure 4.2, in which the current condition levels and the associated shortcomings are projected. 45

46 The introduction of the new condition levels are described here. In the updated model, the bad level is divided in two levels. The first level is the bad level which indicates a very poor condition of the asset which requires additional maintenance before regular maintenance. The second level is the unusable level indicating an extremely poor condition of the asset which requires short term replacement. The decision when the replacement will take place is then ultimately based on remaining parameters such as e.g. financial restrictions. A level moderate will be introduced, indicating that the condition of the asset is moderate, but requires additional maintenance. The level poor will give a poor condition of the asset and the requirement of additional maintenance. The new division of the condition levels is shown in table 4.6. Table 4.6. The updated condition levels. The moderate level and unusable level are added. Condition levels Good air oderate Poor Bad Unusable The updated condition levels can also be shown in a matrix (see figure 4.10). igure The updated condition levels are projected in a matrix. The condition levels are based on the state of the condition and the requirement of additional maintenance. 46

47 In order to optimize the interpretation of the condition levels, an indication of the frequency of the expected failures will be included in the interpretation. The failure frequency is given qualitatively. In order to minimize the subjectivity in the determination of the maintenance by different maintenance planners, the failure frequency has to be given quantitatively. Due to the lack of failure data, the failure frequency is not determined quantitatively for each condition level in this research. Interpretation of the updated condition levels are: Good: the asset is in a good condition. No additional maintenance or inspection is required until the next regular maintenance. Also, at the next regular maintenance no obvious deterioration is expected. The occurrence of a failure is deemed improbable. air: the asset still is in a capable state. No extra maintenance is required until the next regular maintenance. At the next regular maintenace, it will be decided if extra maintenace is required. However, a significant deterioration of the asset is noticeable. The occurrence of a failures is deemed remote. oderate: the asset is in a moderate state. The deterioration of the asset is accelarating faster, but still normal usage of the component is expected. Extra maintenance is required at the next regular maintainance. The occurrence of a failures is deemed low. Poor: the asset is in a poor condition but is still expected to function properly for several years. The condition, however, has deteriorated such that additional maintenance or further research is required before the next regular maintenance. The occurrence of a failures is deemed occasional. Bad: the asset is in a very poor condition and requires short-term additional maintenance or further research to be able to fulfill its function. The occurrence of a failures is deemed probable. Unusable: the asset is in an extremely poor condition and needs short-term replacement. The occurrence of a failures is deemed frequent. Based on the projection of the updated condition levels on the bathtub curve, the difference in states of each condition level can be realized. This projection is made, based on the interpretation of the condition levels in order to have a rough indication of the estimation of the condition of the asset. 47

48 4.4.2 Updated health index levels igure Updated condition levels projected on the bathtub curve. Based on the remaining lifetime and the possibility of additional maintenance, the current health index levels are not classified for each health state. Therefore, the interpretation of the health index levels can be ambiguous. Updated health index levels have to be constructed. The updated health index levels have been derived from the constructed matrix shown in figure 4.3. The introduction of the new health index levels are described here. Currently, the good level does not indicate if additional maintenance is possible or not. There will be a level excellent which gives a health state where the remaining lifetime is greater than seven years and additional maintenance is possible. There will be a good level which gives a health state where the remaining lifetime is greater than seven years and additional maintenance is not possible. or example, this could be the case when spare parts are not available anymore. The fair level gives two states of the health of a component as mention in section 4.1. There will be a moderate level for the state indicating a remaining lifetime less than three years and with the possibility of additional maintenance. There will be a fair level indicating a remaining lifetime between three and seven years. Also here, there is possibility of additional maintenance. Updated health index levels are shown in table 4.7. Table 4.7. The updated health index levels. Based on the current health index levels which provides an ambiguous interpretation, the number of health index levels are extended. The excellent level and the moderate level are added. Health index levels Excellent Good air oderate Poor End of Life 48

49 The updated health index levels are shown in a matrix, see figure igure Updated health index levels are shown in a matrix. RL means remaining lifetime. Interpretation of the updated health index levels are shown in table 4.8. Updated health index levels Excellent Good air oderate Poor End of Life Table 4.8. The updated health index levels and related interpretations. 49 Interpretations The expected technical condition meets the technical assumptions within a viewing period of seven years, provided the regular maintenance activities are carried. Additional maintenance is possible. The expected technical condition meets the technical assumptions within a viewing period of seven years, provided the regular maintenance activities are carried. No additional maintenance is possible. The expected technical condition does not meet the technical assumptions within a viewing period of seven years, but with additional maintenance it can be returned to the excellent health index. The expected technical condition no longer meets the technical assumptions within a viewing period of three years, but with additional maintenance it can be returned to the fair health index. The expected technical condition does not satisfy the technical assumptions within a viewing period of seven years. The expected technical condition cannot be increased using maintenance. The expected technical condition no longer meets the technical assumptions within a viewing period of three years. The expected technical condition cannot be increased using additional maintenance.

50 4.5 Adjustment of the time intervals of the preventive inspections As prescribed in TOR 2.1, the time intervals (BTI) for assessing the condition indicators are constant. The BTI are determined by EA analysis. In cases where the condition of components is critical, the time intervals are decreased based on practical experience. There are no guidelines in order to determine an optimal time interval. A strategy will be designed to determine an optimal time interval. The strategy has to be determined once for each BTI. The BTI given in TOR 2.1 are standard, namely: 1, 3, 6, 9 and 12 years. Result and the weighting of the condition indicator In figure 4.9, the condition indicator levels are plotted on the bathtub curve. As shown in figure 4.9, the worse the condition indicator level, the higher the failure rate and the faster the increase of the failure rate. The higher the failure rate, the more important it is to predict these failures, thus more frequently regular maintenance. Therefore, the time interval of the preventive inspection should be based on the result of the condition indicator level. oreover, in a number of cases, the failure frequency could be high, but the impact low or vice versa. That is why also the weighting of the condition indicator is important. Other literature studies also show that the time interval of preventive inspection can be based on the result of condition indicators [33]. Another literature study shows that the time interval of the preventive inspection can be based on the failure risk [26]. The failure risk is included in the weighting of the condition indicator. In this thesis, the time interval of the preventive inspection will be based on a combination of the result of the condition indicators and the weighting of the condition indicators. This will be performed in section Summary & conclusions After investigating the shortcomings of each process step in the condition and health indexing process, an approach has been devised in order to minimize or eliminate the shortcomings of each process step. As discussed earlier the focus will be mostly on the optimization of the condition indexing process. In order to construct the approach for the assessment of visual inspections, the methodology of the assessment of visual inspection for infrastructural components (NEN 2767) is analyzed. Guidelines are investigated which is included in the optimization of the assessment of the visual inspections of the electrical components. These guidelines consist of questions, standard answers and a transformation matrix. In order to optimize the condition function, an approach consisting of 5 steps is constructed. Step 1 to step 4 is performed in chapter 5, while step 5 is performed in chapter 6. urthermore, the condition levels are subdivided in 6 levels and provide a narrower estimation of the condition of the component. The health index levels are also extended to six levels, indicating each health state based on the standard remaining lifetime (viewing periods) and possibility of additional maintenance. Based on the approaches constructed in this chapter, the optimization can be implemented in chapter 6. 50

51 Chapter 5. The weighting of the condition indicators In order to determine the weighting of the condition indicators, data is required such as ECA of components. The ECA of a circuit breaker system will be discussed in section 5.1. In section 5.2, the condition indicators and failure modes of the circuit breaker, grounding and control circuit are shown. The condition indicators are retrieved from TOR 2.1 and the failure modes are retrieved from the ECA. In section 5.3, the link between the condition indicators and the failure modes are determined based on the failure causes, related sub-component, interpretation of the condition indicators and interpretation of the failure modes. In section 5.4, each link is weighted based on 3 parameter, namely: failure frequency, failure impact and detection chance. The failure frequency and failure impact are retrieved from the ECA. Consequently, the detection chance is determined for each link between condition indicator and failure mode. In section 5.5, the parameters failure frequency, failure impact and detection chance are categorized. Based on these categorized parameters, matrices are constructed in section 5.6 and 5.7. The weighting of each link is determined based on these matrices. Based on the weighting of the links, the weighting of the condition indicators is determined. In section 5.8, the results of the weighting of each condition indicator of the circuit breaker, grounding and control circuit are shown. 5.1 Input data In table 5.1, an example of a ECA for a component or system is shown (component 1 and component 2). The score shown in the table can differ for each component. In appendix B the ECA of a circuit breaker system is shown [26]. 51

52 Table 5.1. Example of a ECA of a specific component or system. The scores can differ for each component. The failure impact is based on the the parameters safety, system, environment and cost. Component Sub - component Sub - component a Component Sub - 1 component b Sub - component c Sub - component d Component Sub - 2 component e ailure mode ailure mode 1 ailure mode 2 ailure mode 3 ailure mode 4 ailure mode 4 Scores In this example, the scores of each parameter ranges between 1 and 5. requency Impact % Score Safety System Environment Cost ailure risk The score of the failure frequency is based on the breakdown with respect to the total failure frequency. The parameters included in the failure impact are: Safety Environment System Cost The total score of the failure impact is determined by formula 5.3. The failure risk is determined by formula 2.1 (section ) ailure impact safety environment system cos t (5.3) 52

53 5.2 Weighting of the condition indicator for a circuit breaker system Based on the components prescribed in TOR 2.1, a circuit breaker system can divided in the 3 components, namely: Circuit breaker Grounding Control circuit The circuit breaker The quality of electricity delivery depends on several aspects such as the reliability of the components. The development of networks, increase of power generation, rise in service voltage and the increasing importance of interconnections, results in an increasing importance of the reliability of circuit breakers [27]. The circuit breakers have various and essential duties. The main task of a circuit breaker is to interrupt fault currents and to isolate faulted parts of the system [28]. There are indoor and outdoor circuit breakers. Condition indicators and maintainable failure modes of the indoor and outdoor circuit breaker are equal, therefore, no distinction will be made between them in this research. In high voltage, 3 types of circuit breakers can be categorized according to the extinguishing medium, namely: Air-blast circuit breaker Gas circuit breaker (mostly S 6 ) Oil circuit breaker. These 3 types of circuit breakers can be subdivided in 3 types of circuit breakers based on the operating mechanisms [27]: Hydraulic mechanism Pneumatic mechanism Spring operated mechanism. The available combinations of operating mechanisms and insulating media for circuit breakers managed by TenneT are shown in table 5.2 [7]. Table 5.2. Available combinations of operating mechanisms and insulating media for circuit breakers. Hydraulic Pneumatic Spring Air blast X Gas X X X Oil X X The hydraulic mechanism, pneumatic mechanism and spring mechanism a number of failure modes differ, therefore, the determination of the weighting of the condition indicators have to be performed separately for each mechanism type. 53

54 5.2.1 Grounding The electrical power system consists of overhead lines and underground cables which are required for transfer of the electrical energy on different voltage levels. These conductors must be isolated with respect to the ground [29]. Grounding is a conducting connection, by which an electric circuit or equipment is connected to the earth, or to several conducting body of relatively large extent that serves in place of the earth. Groundings are used for establishing and maintaining the potential of the earth or approximately that potential [30]. Grounding is required, because of the safety in the network for the persons who work or walk nearby and the continuation of the electricity delivery Control circuit The circuit breaker is operated by receiving an external command from an external relay or an operator which actuates the mechanism to change the position of the contacts. The external command is processed by the control circuit to provide the proper action to activate the circuit breaker. The control circuit is the electrical system required to ensure that the circuit breaker responds correctly, safely and reliably to external commands. This includes operating facilities such as operating coils and condition indications [31]. When an external operating command is sent to a circuit breaker, the control circuit is required to determine whether the circuit breaker is ready to perform the required operation. When the circuit breaker is not able to complete the requested operation, the control circuit must prevent the requested operation. The control system is required to monitor critical parameters and provide an alarm to the operator if these are changing such that they may cause a failure in the circuit breaker [31] Condition indicators and failure modes of the components Condition indicators According to the TOR 2.1, the circuit breakers can be classified in 2 type of circuit breaker, namely: All circuit breakers excluding GIS circuitbreakers. Includes engine and manual circuit breakers, as well as for indoor and outdoor use. GIS circuitbreakers. The condition indicators differ for the 2 types of circuit breakers. The weighting of the condition indicatros have to be performed for each type. According to TOR 2.1, the grounding can be classified in two types of grounding: All grounding excluding GIS grounding. Includes engine and manual circuit breakers, as well as for indoor and outdoor use. GIS grounding. The condition indicators differ for the 2 types of grounding. The weighting of the condition indicators have to be performed for each type. 54

55 Based on TOR 2.1, there is one type of control circuit. The condition indicators for the circuit breaker, grouding and control circuit are shown in table 5.3. The condition idicators are abbreviated to a code number. The code number are also shown in table 5.3. Table 5.3. The condition indicators of each component and their code numbers. Condition Indicator Circuit breaker General impression condition shifting CI 1 Condition insulator CI 2 Condition drives CI 3 Condition contacts CI 4 The average motor current CI 5 GIS-Circuit breaker Overall condition CI 6 Condition drives CI 7 The average motor current CI 8 Grounding Condition insulator CI 9 Condition drives CI 10 Condition contacts CI 11 The average motor current CI 12 GIS-Grounding Overall condition CI 13 Condition drives CI 14 The average motor current CI 15 Control circuit Condition functioning and signaling control CI 16 Condition of the interfacing CI 17 Condition of the additional functions to the CI 18 control The outcome of the "Live" test CI 19 Code number ailure modes Based on the ECA included in this research, the failure modes of the circuit breaker are shown in table 5.4. The failure modes are abbreviated to codes, which are also shown in table 5.4 [26]. 55

56 Table 5.4. ailure modes of the circuit breaker and their related code numbers. ailure modes ailure mode code Asynchronous close/open 1 operation Cracked or break insulator 2 lashover 3 Cracked or break insulator 4 Broken indicator didn't show 5 the real gas pressure Decrease of gas pressure 6 Oil Leakage 7 Air pressure Leakage 8 Internal or external hydraulic 9 oil leakage Pneumatic system leakage 10 otor does not work 11 Operating mechanism failure 12 echanical ailure 13 Only maintainable failure modes are included, thus following failure modes are excluded in this research: Animal exists in the main terminal Animal get into the panel. The failure modes for both grounding types are equal. The failure modes of the grounding are shown in table 5.5. The failure modes are abbreviated to codes, which are also shown in table 5.5. Table 5.5. ailure modes of the grounding and their related code numbers. ailure modes ailure mode code Change of ground level 14 Broken grounding cable 15 Loosen / missed grounding 16 joint The failure modes of the control circuit are shown in table 5.6. The failure modes are abbreviated to codes, which are also shown in table 5.6. Table 5.6. ailure modes of the control circuit and their related code numbers. ailure modes ailure mode code Water/ damp in the panel, contact terminal, auxiliary contact 17 Bad wiring control 18 Broken or exfoliated control cable 19 Power supply doesn't work 20 56

57 5.3 Linking of the condition indicators to the failure modes The links between the condition indicators and the failure modes are determined based on the cause of the failure modes, the interpretation of the failure modes, the sub-component related to the failure mode and the interpretation of the condition indicators. This is shown in figure 5.1. The failure causes are retrieved from the general EA of a circuit breaker determined by IEEE [42] (see appendix C) and the ECA of the circuit breaker system. igure 5.1. actors indicating the link between the failure mode and the condition indicator Condition indicator failure mode links for the circuit breaker Using the factors shown in figure 5.1, the link between each condition indicator and failure mode can be determined. The determination of the link between CI 1 and 1 is illustrated in table 5.7. Table 5.7. Determination of the link between CI 1 and 1. General condition shifts (CI 1) - Asynchronous close/open operation ( 1) actors Description Related component Electrical current carrying Causes failure mode Loss of stored interrupting energy of the mechanism due to leaks, slippage and breakage of the contacts and spring release mechanism worn. Interpretation failure mode The contacts close or open asynchronously. Interpretation condition indicator The impression of the condition of the shifter ("open" & "close") and leakage of the drives. Based on the description of the 4 factors in table 5.7, it can be concluded that there is a link between CI 1 and 1. The link for each condition indicator and failure mode for the circuit breaker is shown in table 5.8. In appendix E, the link for each type of circuit breaker is shown separately. The determination of each link is described in appendix D. 57

58 Table 5.8. Link of the condition indicators and failure modes for a circuit breaker CI 1 X X X X X CI 2 X X X CI 3 X X X X X X X X X X CI 4 X X X CI 5 X X X X X Condition indicator failure mode links for the remaining components The link for each condition indicator and failure mode for the remaining components is shown in table 5.9, 5.10, 5.11 and In appendix E, the link for each type of component GIS-circuit breaker is shown separately. The determination of each link is described in appendix D. Table 5.9. Link of the condition indicators and failure modes for an GIS-circuit breaker CI 6 X X X X X X X X X CI 7 X X X X X X X X X X CI 8 X X X X Table Link of the condition indicators and failure modes for the grounding. Grounding (excluding GIS) CI 9 X CI 10 X X X CI 11 X X CI 12 X Table Link of the condition indicators and failure modes for the GIS-grounding. GIS-Grounding CI 13 X X X CI 14 X X X CI 15 X Table Link of the condition indicators and failure modes for the control circuit. Control circuit CI 16 X CI 17 X X CI 18 X CI 19 X X X X 58

59 5.4 The determination of the detection chance Introduction The detection chance gives the probability that a failure can be predicted before its occurrence. The failure mode can be predicted by the assessment of the related condition indicators. The detection chance will have to be determined for each link between condition indicator and failure mode investigated in the previous section. At time of writing, there were no specific formulas available to determine the detection chance. A formula will be constructed in order to determine the detection chance. In this formula, the probability that a failure mode will occur given the value of the condition indicator will be determined. This formula will be applied for each link in order to determine the detection chance. A number of estimations and assumptions have to be made in order to determine the detection chance, because lack of required data is available. The assumptions and formulas in order to determine the detection chance are shown in appendix. Using these formulas the detection chance of each link between condition indicator and failure mode can be determined. In section 5.4.2, the determination of the detection chance for the links of the circuit breaker will be determined Determination of the detection chance for each link of the circuit breaker Different failure modes can occur for circuit breakers. These failure modes are linked to the condition indicators in section 5.3. ailure mode 1 ( 1) is linked to 3 condition indicators. These condition indicators are show in table The description of the codes of the condition indicators is shown in table ailure mode 1 stands for asynchronous close/open operation. The determination of the detection chance for the 3 links is determined in this section. The determination of the detection chance of remaining links of the circuit breaker, grounding and control circuit is shown in appendix G. Table The condition indicators CI 1, CI 3, and CI 5 are related to failure mode 1. Condition indicators 1 CI 1 X CI 3 X CI 5 X Code condition indicator CI 1 CI 3 CI 5 Table The meaning of the condition indicators. 59 Condition indicator General impression condition shifting Condition drives The average motor current As described in appendix.1, a condition indicator can have 2 values, namely 0 (bad) and 1 (good). Based on these values, 8 combinations are possible if 3 condition indicators are linked to a failure mode. In this case, the detection chance can be determined by formula.1. Based on this formula, 2 factors have to be determined, namely the probability of the

60 occurrence of each combination and the probability of the failure occurrence at each combination. As described in appendix.2 and.3, the mutual relation (functional) between the condition indicators and importance of the condition indicators have to be determined in order to determine these 2 factors. The mutual relation between the condition indicators The mutual relation of the different condition indicators is determined based on the requirements of the sub-components related to the condition indicators of each other based on the functioning of the sub-components. The mutual relation can be: related or not related. The mutual relations for the 3 condition indicators are: CI 1 and CI 3: Related, because for the shifting of the contacts, the drives are required. CI 1 and CI 5: Not related, because the shifting of the contacts do not need the average motor current physically to function. CI 3 and CI 5: Related, because the average motor current can be influenced by the functioning of the drives. The probability of the occurrence of each combination can vary between 0,1 and 1, based on the mutual relation. The interpretation of the different values of the probability of the occurrence of each combination is shown in appendix.2. Importance of the condition indicator CI 1, CI 3 and CI 5 As described in appendix.3, the importance of the condition indicators can be expressed in very important and less important, dependent on the number of related failure causes. ailure mode 1 has three possible failure causes. The importance of the condition indicators are: CI 1, very important, because the condition indicator is related to two failure causes. CI 3, very important, because the condition indicator is related to two failure causes. CI 5, less important, because the condition indicator is related to one failure cause. Based on the importance of the failure modes, the failure probability can be determined. The failure probability can be expressed in 5 categories: P1, P2, P3, P4 and P5. The interpretation of these categories is shown in appendix.3. With the help of percentages included in the interpretations and the frequency of the failure mode, the probability of the failure occurrence can be determined. The frequency of failure mode 1 is equal to 4,35 % of the 48 failures [26, 27]. In the ECA involved in this research, the percentage of failure frequency for each failure mode with respect to the total failure frequency is shown. The total number of failure frequency is not given in the ECA. The total number of failure frequency is derived from another source. Based on data of the second international enquiry on high voltage circuit breakers (CIGRE, 2004) [27], the annual number of failures for a circuit breaker is equal to 48 failures per 1000 circuit breakers. The voltage level for these circuit breakers ranges between 100 kv and 200 kv. The data includes circuit breakers installed after January 1983 [27]. 60

61 The determination of the probability of the combination occurrence and the probability of the failure occurrence for each combination is illustrated in table Based on these 2 factors, the detection chance is determined. The detection chance for CI1-1, CI3-1, CI5-1 is shown in table We can see that the detection chance for CI 1 is the highest for 1 compared to CI 3 and CI 5. So the chance to predict 1 is the greatest with CI 1. Table The determination of the probability of the combination occurrence and the probability of the failure occurence for each combination. CI 1 CI 3 CI 5 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence , ,2 P ,2 P ,2 P ,2 P ,2 P ,2 P , Table The detection chance of the links. Link Detection chance CI 1 1 0,73 CI 3 1 0,71 CI 5 1 0, Categorization of the 3 parameters In order to weight the condition indicators, each link between the condition indicator and failure mode have to be weighted by the parameters failure frequency, failure impact and detection chance. In order to weight the links based on these 3 parameters, the parameters have to be categorized and expressed in a rank. The parameters failure frequency and failure impact are retrieved already categorized in the ECA [26]. The detection chance has to be categorized in this research. Each rank has an interpretation. The interpretation should be applicable for all the components. ailure frequency The failure frequency gives the number of occurrences of a failure mode in a specific period. The failure frequency ranks between 1 and 5. The interpretation of the ranks is based on the interpretation in NEN-EN-IEC [32]. 61

62 Table Ranks and interpretation of the failure frequency. Rank Interval ( in % of total) Interpretation 1 < 0,87 Improbable: ailure is unlikely 2 0,87 1,73 Remote: Relatively few failures 3 1,74 6,96 Occasional: Occasional failures 4 10,43 13,04 Probable: Repeated failures 5 > 13,04 requent: ailure is almost inevitable ailure impact The failure impact gives the consequence of a failure mode, which can be determined by business values such as safety and costs. The business values which are taken into account in the failure impact of the ECA included in this research and their categorization range are: Safety (1 to 2), Environment (1 to 2) System (1 to 5) Cost (1 to 4). Based on these business values and formula 5.3, the failure impact can vary between 1 and 80. These ranks are still difficult to use for further analysis, that is why these ranks will be classified in 4 ranks. The interpretation of the ranks and the choice of 4 ranks are based on the interpretation and the categorization in NEN-EN-IEC [32]. The intervals of the new ranks are based on the interpretation of the ranks. The new ranks, the involved intervals and their interpretation are shown in table

63 Table Ranks, intervals and related interpreatation of the failure impact. Rank Interval Interpretation A 1-6 Insignificant: A failure mode which could potentially interrupt the system's functions, but will cause no damage to the system and does not constitute a threat to life, injury or the environment. B 7-9 arginal: A failure mode, which could potentially interrupt the system performance functions without notable damage to the system or threat to injury or the environment. C Critical: A failure mode which could potentially result in the failure of the system's primary functions and therefore causes notable damage to the system, but which does not constitute a serious threat to injury or the environment. D Catastrophic: A failure mode which could potentially result in the failure of the system's primary functions and therefore causes serious damage to the system and its environment and/or personal injury. Detection chance The higher the detection rank, the less probable the detection is [32]. Based on the assumptions and the formula for the determination of the detection chance, the detection chance can range between 0,38 and 1. The detection chance will be categorized in 4 ranks. The interval of each rank will assumed to be equal. Division of the difference between 0,38 and 1, by 4 ranks results in an range of 0,155. The interpretation of the ranks is based on the interpretation shown in NEN-EN-IEC [32]. The ranks of the detection chance, intervals and related interpretation are shown in table Based on the ranks of the 3 parameters, the risk matrix and criticality matrix can be constructed. 63

64 Table Ranks, intervals and interpreatation of the detection chance. Rank Interval Interpretation 1 0,87 1,00 High: High chance that the condition indicator will detect a failure mode. 2 0,70 0,86 oderate: oderate chance that the condition indicator will detect a failure mode. 3 0,55-0,69 Low: Low chance that the condition indicator will detect a failure mode. 4 0,38 0,54 Remote: Remote chance that the condition indicator will detect a failure mode. 5.6 Risk matrix The parameters failure frequency and failure impact will be matched in a risk matrix in order to determine the failure risk. This risk matrix (see table 5.20) is equal to the risk matrix of NEN-EN-IEC [32]. The risk matrix is expressed in 4 categories: Negligible Tolerable Undesirable Intolerable. Table Risk matrix based on the failure impact and failure frequency [32]. ailure impact ailure frequency A B C D 5 Undesirable Intolerable Intolerable Intolerable 4 Tolerable Undesirable Intolerable Intolerable 3 Tolerable Undesirable Undesirable Intolerable 2 Negligible Tolerable Undesirable Undesirable 1 Negligible Negligible Tolerable Tolerable 64

65 5.7 Criticality matrix Based on the failure risk and the detection chance, a criticality matrix can be determined. This criticality matrix (see table 5.21) is based on the description of the risk evaluation in NEN-EN-IEC [32]. The criticality can be expressed in 4 categories: Remote Low edium High. Table Criticality matrix based on the failure risk and the detection chance. Detection chance ailure risk Negligible Tolerable Undesirable Intolerable 1 Low Low High High 2 Low Low High High 3 Remote Remote edium edium 4 Remote Remote edium edium 5.8 Weighting of each condition indicator ethodology for determination of the weighting of the condition indicator In order to determine the weighting of the condition indicator based on the weighting of the links, 2 different methods are analyzed. Based on the analysis, ethod 1 is the best method. ethod 1 The link with the greatest criticality index will be the overall criticality of the condition indicator. rom the remaining methods, one method (ethod 2) will be described in order to illustrate disadvantage of the other methods. ethod 2 In this case, the criticality of each link will be expressed in a score, for example: Remote (1), Low (2), edium (3) and High (4). In order to determine the weighting of a condition indicator the scores of all links will be added. Based on the total score, the weighting of the condition indicator can be determined. The total scores can range between 1 and 40, because the condition indicators included in this research can be linked to minimal 1 and maximal 10 failure modes. 65

66 The total score is assumed to be classified in even ranges: 1 10 = Remote = Low = edium = High. Analysis of the methods by an example The example includes a condition indicator (CI A) which is linked to 3 failure modes, namely 1, 2 and 3. The failure risk and detection chance for the links are: CI A 1 Detection chance = 3 and ailure risk = Tolerable. Based on table 5.21, the criticality is Remote. CI A 2 Detection chance = 3 and ailure risk = Undesirable. Based on table 5.21, the criticality is edium. CI A 1 Detection chance = 2 and ailure risk = Intolerable. Based on table 5.21, the criticality is High. The methods are illustrated in table 5.22 and table Table An example to illustate method 1 to determine the weighting of the condition indicator Weighting CI CI A Remote edium High High Table An example to illustate method 2 to determine the weighting of the condition indicator Weighting CI CI A Remote edium High Remote Evaluation and selection of the method ethod 2: The disadvantage of this method is that in the case that there are relatively less links, the overall criticality (weighting) of the condition indicator can be Remote or Low even though the criticality of all the links are High. In this way, the risk of each failure mode is not taken into account. Even though the condition indicator is linked to 1 failure mode, the condition indicator can be important if the link has a High criticality. ethod 1: In this method the greatest criticality of the links is included. In this way, the risk of each failure mode is taken into account. Therefore, ethod 1 is selected in order to determine the weighting of a condition indicator. 66

67 5.8.2 The determination of the weighting of the condition indicator with the selected method In table 5.24, the detection chance has been categorized, and the failure risk and criticality is determined for a number of links (condition indicator failure mode). The determination of the weighting (criticality) of the other links is shown in appendix H. Table The 3 parameters failure frequency, failure impact and detection chance are shown for a number of links. urthermore, the failure risk and criticality are given. Link Detection chance CI 1 1 0,73 CI 3 1 0,71 CI 5 1 0,69 Rank detection chance Rank failure frequency Rank failure impact Category failure impact Category failure risk C Undesirable C Undesirable C Undesirable Criticality link High High edium The condition indicators can be determined with the aid of method 1. This is shown for one type of the circuit breaker (air-blast and pneumatic) in table The determination of the weighting of the condition indicators of the other types of the circuit breaker, the grounding and the control circuit are shown in appendix I. Table The weighting of each condition indicator for the circuit breaker. Circuit breaker (Air Blast, Pneumatic) Weighting of CI CI 1 High Remote Remote High CI 2 High High High High CI 3 High Remote Low Remote edium High CI 4 Remote edium Remote edium CI 5 edium Low Remote edium edium Based on the weighting of the condition indictors can be concluded that the weighting of the condition indicators is equal for respectively the different types of circuit breakers and GIScircuit breaker. The criticalities of the condition indicators are shown in table 5.26 for each component. 67

68 Table The weighting of each condition indicator of the circuit breaker, grounding and control circuit. Condition Indicator Circuit breaker General impression condition shifting High Condition insulator High Condition drives High Condition contacts edium The average motor current edium GIS-Circuit breaker Overall condition High Condition drives edium The average motor current High Grounding Condition insulator Remote Condition drives Remote Condition contacts Remote The average motor current Low GIS-Grounding Overall condition Low Condition drives Remote The average motor current Low Control circuit Condition functioning and signaling control Remote Condition of the interfacing Remote Condition of the additional functions to the Remote control The outcome of the "Live" test Remote Weighting of the CI 5.9 Summary & conclusions After the collection of data, the determination of the weighting of the condition indicators can be performed. Based on the 3 parameters, failure frequency, failure impact and detection chance, the weighting of each condition indicator is determined. After determination of the weighting of the condition indicators of the circuit breaker, grounding and control circuit, it can be concluded that the weighting of the condition indicators differ from each other. Therefore, the inclusion of the weighting of the condition indicators in order to determine the condition of the asset is required. The condition indicators are the same for the different types of circuit breakers. The weighting of the condition indicators is determined for each type of circuit breaker. The weighting of the condition indicators is equal for the different type of circuit breakers. In the next chapter, a strategy will be designed in order to include the weighting of the condition indicators in the determination of the condition of the asset. 68

69 Chapter 6. Optimization of the process steps included in the condition indexing process In section 6.1, a model is developed for the assessment of the visual inspections. This model includes guidelines such as question, answers and a transformation matrix in order to minimize the subjective of the assessment of the visual inspections. Afterwards, a strategy including the weighting of the condition indicators is constructed for the determination of the condition of the asset in section 6.2. This strategy will be based on the current strategy. The current strategy is based on the weakest part of the component. In section 6.3, a strategy is constructed in order to determine the time intervals for the condition indicators. The result and weighting of the condition indicator will be included in this new strategy. In section 6.4, the impact of the process steps which are optimized is described. Based on the impact of the process steps, it can be analyzed if the maintenance can be planned more optimally. inally, a case study is performed in section 6.5 in order to assess the news strategy for the determination of the timer intervals. Based on the case study, it can be analyzed if savings on operational expenditures and a more flexible condition control can be achieved. 6.1 Guidelines for the assessment of the visual inspections Elaboration of the investigated guidelines for a more objective assessment of visual inspections As shown in the flowchart in figure 4.4 (section 4.2.3), a number of questions have to be answered during the visual inspections. As observed from practice, the questions which have to be aswered during the preventive inspection are standard. There are 9 standard questions based on the visual inspections of the circuit breaker, grounding and control circuit. The questions are based on parameters such as size or intensity, which gives the severity of the defect. The 9 standard questions and related answers are shown in table 6.1. Several examples of deviations or sub-components for which these questions have to be answered are shown in appendix J. Interpretations of the answers are also shown in appendix J. 69

70 Table standard questions and the related answers. The answers are categorized. The lay-out of the anwers and questions on the inspection sheet is also shown in the table. Questions Answers Rank Lay-out in practice What is the size of the deviation? What is the severity of the deviation? Nothing Small edium Large Nothing Less oderate any Nothing 0 Small 1 edium 2 Large 3. Nothing 0 Less 1 oderate 2 any 3. Size deviation Severity deviation Is the sub-component in position? Yes No Sub-component in position Yes No What is the intensity of the defect? Nothing Beginning Advanced End Nothing 0 Beginning 1 Advanced 2 End 3. Intensity deviation How smooth is the rotation or motion of the structures? Very rough Rough Smooth Very rough 1 Rough 2 Smooth 3. Smoothness subcomponent Is the sub-component firmed correctly? Yes No Sub-component firmed Yes No Do yo feel the heat feel? Yes No Heat component felt Yes No Is the sub-component available? Yes No Sub-component available Yes No Is the checklist satisfied? Yes No Checklist subcomponent satisfied Yes No 70

71 Transformation matrix Based on the results of the questions of each visual inspection, a transformation can be made to one of the 4 categories. At each visual inspection usually 2 questions are asked, therefore, a matrix can be handy for the application of the transformation. The categories in the matrix are determined on basis of interviews of maintenance personnel. An example of a matrix is shown in table 6.2. This matrix can be applied for a visual inspection such as rust. The other matrices for damage, pollution, wiring, etc. are shown in appendix J. Table 6.2. atrix for the transformation of the parameters size and intensity. Size Intensity 0 Good air air Poor 2 - air Poor Bad 3 - Poor Bad Bad Application of these guidelines for several components With the aid of several examples (components), the designed model will be illustrated. In appendix K, the inspection sheet for these components is shown. A general example of the inspection sheet of a component is shown in table

72 Table 6.3. The method of reporting visual inspections for a components is shown for the current model and new model. Current New Sub component A Good air Poor Bad Report Transformation - condition indicator X - Deviation 1 Size deviation 1 Severity deviation Good air Poor Bad Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad - Sub-component 1 Checklist sub-component 1 satisfied Yes No Sub component B - condition indicator Y - Deviation 2 - Sub-component 2 Size deviation 2 Intensity deviation Sub-component 2 firmed Yes No Good air Poor Bad Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad 72

73 6.1.3 Advantages of the new model for assessing the visual inspection The advantages of the new model for the assessment of the visual inspections are: The assessment of the visual inspection will be uniform for each specific visual inspection. The same questions, answers and transformation matrix will be used for each specific inspection. Different maintenance personnel are performing the visual inspections, hereby, there can be always a possibility of subjectivity. However, using guidelines the subjectivity can be minimized. With the new model, the subjectivity during the assessment of the visual inspection is minimized. The guidelines in this model are the question, answers and the transformation matrix. The maintenance personnel are all required to answer the same questions. Based on the transformation matrix, the transformation to the categories such as good, fair, poor and bad will be independent of the maintenance personnel. Subjectivity during the transformation is minimized in this way. The new model does not require more time compared to the current model, because the questions that have to be answered in the new model are already answered in mind of the maintenance personnel. On the contrary it will take less time, because they do not have to transform the answers to a category. Based on the answers to the questions, the maintenance planner can determine the maintenance activity precisely in several cases. or example, when the intensity of rust on a component is at a beginning stage, a minor maintenance activity can be sufficient. Conversely, when the intensity of rust on the same component is at a final stage, this would require a major maintenance activity. Based on the current model (only the category good, fair, poor or bad), it is not clear at what stage the intensity of rust is. 6.2 Strategy for the determination of the condition of the asset Strategy to determine the condition of the asset In order to determine the condition of the asset, there should be possibility to transform the condition indicators levels to the condition level. The interpretation of the condition indicator levels and the condition levels includes aspects such as: the requirements of extra maintenance (yes or no) noticing of deterioration (deterioration or no deterioration) the interval for the next extra maintenance (earlier or much earlier). Based on these aspects, the condition indicator levels and condition levels can be classified. Based on the classification, the relation between the condition indicator levels and condition levels can be investigated. 73

74 The classification of the condition indicator levels and the condition levels, and the transformation of the condition indicator levels to the condition levels are shown in figure 6.1. igure 6.1. Classification of the condition indicator levels and the condition levels. The transformation from the condition indicator level to the condition level is also shown. It is assumed that the transformation will be based on the criticality of the condition indicator. As indicated in figure 6.1, a number of condition indicator levels can directly be transformed into a condition level. A number of transformations are assumed to be based on the weighting (criticality) of the condition indicator. This is shown in figure 6.2. igure 6.2. The transformation of the condition indicator level to the condition level based on the weighting of the condition indicator is shown in a diagram. New strategy Based on the transformation in figure 6.2, a strategy for selecting the appropriate condition indicator level has to be selected. or the achievement of a proper reliability of a component, the weakest part of the component should be involved. Based on this, the worst 74

75 condition indicator level will be selected in the new strategy. After that, the worst level will be matched to the weighting of that condition indicator in order to transform the condition indicator level to the condition level. The transformation is performed with the diagram in figure 6.2. In the case, several condition indicators with the same worst level are available, the worst level with the highest weighting will be selected. or example, when the worst result is bad and the result of 2 condition indicators are bad (the weightings are respectively high to low), the condition indicator with the weighting high will be selected in order to determine the condition of the asset. In figure 6.3, the range of the estimation of the condition of the asset with respect to the actual condition of the component is shown for the current and new strategy (option). igure 6.3. Projection of the range of the estimation of the condition of the asset for the current and new strategy. Based on the ranges indicating the estimations of the condition of the asset it can be concluded that the range in which the estimation of the condition of the asset can be inaccurate is smaller for the new strategy (option). rom this it is found that a narrower estimation of the condition of the asset can be determined with the new strategy. Evaluation of the transformation model In the new strategy that has been described throughout the previous sections, the weighting of the condition indicators is included in the transformation. Based on the weighting, 16 situations are possible in the new transformation model. Compared to the current transformation model, the new transformation model differs for 8 situations. This contributes to a change of 50 % of the transformation. The transformation of the good level and fair level remains the same in the new transformation model. Based on the condition of asset, analyses will be performed in order to determine the extra maintenance. or the good level and fair level no extra maintenance is required, that is why it does not matter that these levels do not change in the new strategy. It can be concluded that the 50 % of change is effective Practical example With the help of an example, the new strategy for the determination of the condition of the asset will be illustrated. Table 6.4 gives the results of the condition indicators of a component. As described before, the worst condition indicator level will be selected. In this case, the worst condition level is bad. After that, the weighting of that condition indicator will be matched in the diagram (see figure 6.2). Based on the diagram, bad (high weighting) is related to unusable. The condition of the asset is unusable. Once the weighting of each condition indicator is determined and given on the inspection form, only the diagram has to be included in order to determine the condition of the asset. The determination of the asset 75

76 is uniform for each component, therefore, the strategy of determination can be programmed leading to a more user friendly application of this method. Table 6.4. An example illustrating the determination of the condition of the asset. Component Condition indicator Weighting of the condition indicator CI 1 Low air CI 2 High Bad CI 3 Low Poor CI 4 Remote Good CI 5 edium air Result of the condition indicator Condition of asset Unusable 6. 3 Strategy for the determination of the time intervals A strategy is required to determine an optimal time interval of the condition indicators. In this way, the proper moment of the required maintenance can be determined. or condition indicators with a basic time interval (BTI), the determination of the time intervals could be shown in the matrix of table 6.5. In the matrix the 4 results of the condition indicators are plotted against the 4 weightings of the condition indicators. Based on the matrix, there are 16 options. It will be assumed that the BTI will be stated centrally of the matrix. The central of the matrix is: poor-remote and fair-high. urthermore, it will be assumed that the adjustment of the BTI will be performed with an even time interval for each cell compared to the previous cell. The minimum BTI in TOR 2.1 is 1 year, therefore, the minimum time interval in the matrix will be assumed to be 1 year. The difference between the BTI and 1 year is subtracted and evenly divided by the number of cells from the first cell (1 year) to the cell of BTI. There are 7 cells, so the difference will be divided by 7. This will result in a time interval, TI. BTI 1 TI (6.1) 7 TI is expressed in years. As shown in table 6.5, the BTI is adjusted with a plurality of ± TI based on the result and weighting of the condition indicator. Table 6.5. Time intervals for condition indicators with a time interval equal to BTI. These time intervals are based on the result and weighting of the condition indicator. These intervals are expressed in years. Condition indicator Weighting Result High edium Low Remote Good BTI + 4. TI BTI + 5. TI BTI + 6. TI BTI + 7. TI air BTI BTI + TI BTI + 2. TI BTI + 3. TI Poor BTI 3. TI BTI 2. TI BTI TI BTI Bad 1 BTI 6. TI BTI 5. TI BTI 4. TI 76

77 This model is illustrated for 2 time intervals, namely 3 years and 6 years. The matrices of these time intervals are shown in appendix L. In the interpretation of the condition levels, it is given if extra maintenance is required before the next regular maintenance or not. The time intervals for the extra maintenance are not given, but are determined based on experience. The time interval for the extra maintenance will also be adjusted with the matrix in table 6.5. This will result in a combination of regular and extra maintenance. The time interval for extra maintenance will be determined, therefore, it does not have to be given in the interpretation of the condition levels that extra maintenance is required before the next regular maintenance. Based on the strategy for the determination of the time interval, it can be expected that the time interval for each condition indicator can differ from each other. As prescribed in TOR 2.1, the BTI of several components can also differ for the various condition indicators. In these cases, usually 2 different time intervals are applied for a component. In this research, the time intervals will also be classified in a maximum of 2 groups for each component: In the first group, the condition indicator with the lowest time interval is involved. After that, the difference between the next lowest time interval will be matched with the difference between the other time intervals. If the difference between the next lowest time interval is the lowest difference, it will be selected in the first group. If the difference between the next lowest time interval is not the lowest difference, the next lowest time interval will be the second group. The remaining condition indicator will also be included in the second group. If the second lowest time interval differs less than 1 year compared to the lowest time interval, the second lowest time interval will be involved in the first group. In this case the time interval for the second group will be the third lowest time interval. If the other time intervals also differ less than 1 year from the lowest time interval, these time intervals will also be included in the first group. The application of this strategy for the determination of time intervals can be illustrated for the GIS-circuit breaker in table 6.6. The condition indicators are classified in 2 groups. 2,4 years is the lowest time interval and will be the time interval for the first group. The second lowest time interval is 6 years. The difference between the lowest and second lowest time interval is 3,6 years. The difference between the time interval of CI 6 and CI 8 is 1,2 years. 3,6 years is not lower than 1,2 years, so the second lowest time interval will not included in the first group. The time interval of the second time interval will be the second lowest time interval (6 years). CI 6 and CI 8 will be included in the second group. Table 6.6. Example illustrating the determination of the time intervals for the GIS-circuit breaker. GIS- Circuit breaker Condition indicator Weighting of the CI Basic time interval (years) Result of the CI Adjusted time interval (years) CI 6 High 6 air 6 CI 7 edium 12 Bad 2,4 CI 8 High 12 Poor 7,2 77

78 User friendly and uniformity Once the matrices for each BTI are constructed, the time interval for the condition indicators can be determined after each inspection (regular maintenance). By creating guidelines (strategy), the adjustment of the time intervals will be uniform. Because of the uniformity, the determination of the time intervals can be programmed, leading to a more user friendly method Impact of the optimized process steps After the optimization of all the process steps, the impact of this optimization can be analyzed. By using a more objective assessment of visual inspections, the data quality of the assessment of the condition indicators can be improved. rom this improved data quality of the assessed condition indicators, the health index and condition of asset can be determined more precisely. urthermore, based on the improved data quality of the assessed condition indicators an optimal time intervals of the condition indicators can be determined. Based on the updated condition index levels and the inclusion of the weighting of the condition indicators, a narrower estimation of the condition of the asset can be achieved. urthermore, an optimal time interval of the condition indicators can be determined based on the weighting of the condition indicators. Based on an optimal time interval of the condition indicators and a narrower estimation of the condition of the asset, the extra maintenance planning can be determined more optimally. Based on an optimal time interval of the condition indicators, the regular maintenance planning can be determined more optimally. The impact of the optimizations of the process steps is illustrated in figure 6.4. igure 6.4. The impact of the process steps which are optimized is shown. Based on the optimized aspect included in the condition indexing process, the regular and extra maintenance planning can be determined optimally. 78

79 6. 5 Case study Determining the optimal time interval for the condition indicators and the extra maintenance can lead to optimized maintenance planning. Based on the optimized maintenance planning, savings on expenditures and a more flexible condition control can be achieved. In order to confirm if these objectives can be achieved by the application of the new strategy for the determination of the time intervals, a case study will be performed. In the case study, 4 future scenarios will be constructed. These scenarios are numbered from 1 to 4. 2 grounding components are involved, namely Grounding A en Grounding B. Scenario 1: Grounding A is included and the current strategy for the determination of the time intervals is performed. Scenario 2: Grounding A is included and the new strategy for the determination of the time intervals is performed. Scenario 3: Grounding B is included and the current strategy for the determination of the time intervals is performed. Scenario 4: Grounding B is included and the new strategy for the determination of the time intervals is performed. The total period for each scenario will be 30 years. The results of the condition indicators are fictitious, because the lack of maintenance data. The initial results of the condition indicators will be given as the results after regular maintenance 1. The purpose of the case study is illustrated in figure 6.5. The adjustment of the time intervals will be performed for the condition indicators and the extra maintenance. It will be assumed that the failure frequency during the period of 30 years will be equal for respectively scenario 1 and scenario 2, and scenario 3 and scenario 4. igure 6.5. The purpose of the case study is presented in the diagram. The case study will prove if savings on expenditures and a more flexible condition control can be achieved. In scenario 1 and 3 the current strategy for the determination of the time interval is performed, while the new strategy is performed at scenario 2 and 4. 79

80 Scenario 1 The result of the condition indicators as assumed after the regular maintenance 1 are shown in table 6.7. Table 6.7. The results of the condition indicators and condition of the asset of Grouniding A. Condition indicator Result of the CI Time intervals (years) CI 9 Good 6 CI 10 air 6 CI 11 air 6 CI 12 Poor 6 Condition of asset Poor Scenario 2 The same component (Component A) of scenario 1 is included scenario 2, so the result of the condition indicators after regular maintenance 1 are equal to the results assumed in scenario 1 (table 6.7). The time intervals for regular maintenance 2 are adjusted in table 6.8. Table 6.8. The results of the condition indicators and condition of the asset of Grouniding A. The time interval for the condition indicators are determined, based on the new strategy. Condition indicator Weighting of the CI Result of the CI Time intervals (years) Time interval in practice (years) CI 9 Remote Good 10,9 CI 10 Remote air 8,1 8,1 CI 11 Remote air 8,1 CI 12 Low Poor 5,3 5,3 Condition of asset oderate or scenario 1, the results of the condition indicators after each regular maintenance activity are shown in table N.1 (appendix N.1). The time interval for each condition indicator after each regular maintenance activity is 6 years. The results of the condition indicators after each regular maintenance activity in scenario 2 can determined based on the results of the condition indicators of scenario 1. This is shown in figure 6.6. In figure 6.6, first the results of the condition indicators as given in table N.1 are plotted on a timeline for each condition indicator for scenario 1. After that, the time intervals for scenario 2 based on the initial condition (regular maintenance 1) are determined and plotted on a timeline. Based on the timeline of scenario 1, the condition at the determined time intervals in scenario 2 is matched. This process is repeated for every regular maintenance activity. 80

81 igure 6.6. The results of the condition indicators of scenario 1 are plotted on a time line. The time intervals for each condition indicator are adjusted for the scenario 2 for regular maintenance 2. The results of the condition indicators are matched with the results in scenario 1. This process is repeated for each regular maintenance activity. The number gives the number of the regular maintenance. gives the extra maintenance. In figure N.1 (appendix N.1), all the condition indicators, related time intervals and related results is shown on one timeline for respectively scenario 1 and scenario 2. In scenario 1, each condition indicator in scenario 1 is assessed 6 times, while each condition indicator is assessed 5 times in scenario 2. In scenario 2 there are more regular maintenance moments due to the fact that the condition indicators are assessed at different time intervals. Savings on expenditures The expenditures for both scenarios will be determined (over 30 years). Based on the expenditures of the scenarios, it can be explored if savings on expenditures is achieved at scenario 2 compared to scenario 1. In order to determine the expenditures of each scenario, the price tag (appendix ) for the maintenance of the grounding have to be analyzed. The maintenance costs can be divided in: Labor costs Transport costs aterial costs (spare parts and tools). Based on a number of assumptions the costs for each regular maintenance and extra maintenance are determined. The assumptions are derived from the price tag, which is illustrated in appendix [46]. 81

82 The assumptions for the costs are determined here. In the costs the surcharges are included. Labor costs: In the labor costs the, costs for the execution and the preparations are included. The labor costs are 64 per hour. The inspection of 4 condition indicators requires 10 hours. The labor cost is 640. The inspection of 3 condition indicators requires 10 hours. The labor cost is 640. The inspection of 2 condition indicators requires 10 hours. The labor cost is 640. The inspection of 1 condition indicator requires 5 hours. The labor cost is 320. Transport costs: The transport costs are 0,20 per kilometer. The distance to the installed component is 100 km. The transport cost for the inspection of all 4 condition indicators is 80. The transport cost for the inspection of 3 condition indicators is 80. The transport cost for the inspection of 2 condition indicators is 80. The transport cost for the inspection 1 condition indicator is 40. Spare parts: The spare parts include the material and the required tools to perform the inspection. The costs for spare parts for each regular maintenance activity of each condition indicator are equal. The costs for spare parts for each regular maintenance activity of each condition indicator are 70. Extra maintenance: The extra maintenance costs are included the labor, transport and material costs. The extra maintenance cost for each condition indicator is equal. The costs for extra maintenance 1 are equal to The costs for extra maintenance 2 are equal to The costs for extra maintenance 3 are equal to The costs for extra maintenance 4 are equal to Discount rate: The discount rate on expenditures is 7 % per year. When all condition indicators are performed during the regular maintenance, the total costs are In table 6.9, the expenditures for scenario 1 and 2 are shown. 82

83 Table 6.9. The expenditures of each scenario during a period of 30 years. Regular gives the number of the regular maintenance. Extra gives the number of the extra maintenance. # After number of years (years) t 0 0 Regular 1 t 1 4 Extra 1 t 2 6 Regular 2 t 3 12 Regular 3 t 4 16 Extra 2 t 5 18 Regular 4 t 6 24 Regular 5 t 7 28 Extra 3+4 t 8 30 Regular 6 Scenario 1 Scenario 2 Activity Expenditures Activity ( ) # After number of years (years) 83 Expenditures ( ) 1000 t 0 0 Regular t 1 5,3 Regular 2a and Extra t 2 8,1 Regular 2b 1460 t 3 12,7 Regular 3a 810 t 5 16,2 Regular 3b 1985 t 6 20,1 Regular 4a and Extra t 7 24,3 Regular 4b 2320 t 8 27,5 Regular 5a t 9 30 Regular 5a and Extra Total ( ) Savings (%) -13 In appendix N.2, scenario 3 and scenario 4 are elaborated on the same way. The same assumptions are made for the expenditures. It can be concluded that the application of the new strategy for the determination of the time intervals in scenario 2 provides extra expenditures. This can be, due to the fact that the component did not is a newly installed component (condition of asset is moderate) or in a good condition. In scenario 4 savings on expenditures are achieved compared to scenario 3.

84 The component included in these scenarios is in a good condition (condition of asset is good). Condition control Due to the fact that the time intervals are shorter when the component is getting critical, a more flexible condition control can be achieved. As can be seen in scenario 2, the regular maintenance is performed frequently (increase controllability) in the case the component becomes critical (deterioration of the condition of the component). As determined in scenario 4, less regular maintenance is performed when the component is in a good condition. Different ages of components The transmission grid contains components with different ages. When the new strategy for the determination of the time intervals will be applied in practice, it is expected that for a number of components there can be savings on the expenditures, but for other components there can be an increase of the expenditures. Based on the failure statistics, it can be concluded that the failure rates can differ for different ages. When the condition of the component after application of the new strategy for the determination of the time intervals is at a state from which is expected that the failure rates will increase continuously, it can be expected that the expenditures will increase with respect to the current strategy. The increase of expenditures is expected, due to the fact that the condition indexing process will be performed over a shorter time intervals. It is expected that newly installed components or components in a good condition will lead to savings of the expenditures when the new strategy will be applied. The TSO should determine if there will be an overall savings or extra cost for the complete grid after the application of the new strategy. This, in order to prove if the new strategy will be cost efficient. In case of extra expenditures, there can be savings of the expenditures after a certain period of the application of the new strategy. After a certain period the older components or the components which are bad will already have been replaced. 6.6 Summary & conclusions After several interviews with maintenance personnel, a new model for the assessment of the visual inspections was constructed. This new model is based on a number of guidelines such as standardized questions and answers. These guidelines can minimize the subjectivity of the assessment of the visual inspections. Afterwards, a strategy was developed in order to make a narrower estimation of the condition of the asset by including the weighting of the condition indicators. urthermore, a strategy was developed to adjust the time intervals for each condition indicator based on the weighting of the condition indicators and the result of the condition indicators. Consequently, the impact of all the process steps which are optimized was analyzed. Based on the impact of the process steps, it could be concluded that the maintenance can be planned more optimally. Application of the new strategy for the determination of the time intervals on a specific case illustrates that extra expenditures can be incurred for several components with respect to the current strategy, while on several other components savings can be achieved based on the condition of the component. urthermore, a more flexible condition control is achieved when the new strategy for the determination of the time intervals is applied. 84

85 Chapter 7. Conclusions and recommendations In this chapter, the conclusions and recommendations of this research are given and discussed. The recommendations are intended for future research. In the first section, the conclusions are described. The conclusion will be divided in two parts. The first part will describe condition indexing process and the second part will describe the health indexing process. In the last section, the recommendations are given. 7.1 Conclusions In order to optimize the maintenance planning, the condition and health indexing process has been optimized. Condition indexing process A model has been developed which includes more guidelines. This model will minimize the subjectivity of the assessment of the visual inspections. The guidelines consist of standard questions, the answers for each of these questions and transformation matrices. Based on these questions, the maintenance personnel have to focus on the same parameters during the assessment of the visual inspections. The answers of these questions limit the maintenance personnel within a certain range during the assessment. With the help of a transformation matrix the transformation to the 4 categories, good, fair, poor and bad, will be independent of the maintenance personnel (section 6.1). Because of the minimization of the subjectivity in the new model, the quality of the assessment of the visual inspections will be increased. Despite the fact that in several cases more questions have to be answered, this model is expected to be user-friendly as observed from interviews with maintenance personnel. By introducing more guidelines in the model, the assessment of the visual inspections becomes uniform. urthermore, based on the answers of the questions, the maintenance planner can determine the maintenance activity precisely in several cases (section 6.1). With the help of the failure frequency, failure impact and detection chance, a quantitative estimation can be made for the criticality (weighting) of each condition indicator. urthermore, an approach for the determination of the detection chance has been elaborated (section 5.4). The weighting factors have different values for every condition indicators. Because of the difference in the weighting of the condition indicators, the weighting of the condition indicators should be taken into account in order to make a narrower estimation of the condition of the asset (section 5.8). A strategy has been designed to determine the condition of the asset by including the weighting of the condition 85

86 indicators. Based on this strategy, a narrower estimation of the condition of the asset can be achieved. In the strategy specified at the previous point, the condition indicator levels have to be transformed into a condition level. This transformation is based on the weighting of the condition indicators. Currently, the transformation is performed without including the weighting of the condition indicators. Using the weighting of the condition indicators, 16 transformations are possible. In the new transformation methodology, the transformation to good and fair is equal to the current transformation methodology. The transformation to good and fair consist of 8 transformations. Based on the new transformation methodology, 50% of the transformations to the condition levels will differ compared to the current transformation methodology. Only in the same 50% of the transformations (transformation to poor and bad) which is mentioned before, there will be requirement of additional maintenance, thus the change is relatively effective in practice (section 6.2). By extending the condition levels from 4 to 6 levels, each specific state of the condition can be given. In this way, a narrower condition estimation can be determined for each component. The specific states of the condition based on the current interpretation of the condition levels are good, moderate, poor and very poor. By updating the interpretation of the condition levels, an indication of the expected failure frequency can be made. urthermore, an unambiguous interpretation of the condition levels is achieved (section 4.4). The weighting of the condition indicators can be used not only for the determination of the condition of the asset, but also for the determination of the time intervals of the condition indicators and the extra maintenance. After each regular maintenance activity, the time intervals of each condition indicator can be determined based on a strategy which includes the result and weighting of the condition indicators. The previously mentioned strategy will be the guideline for the determination of the time intervals of the condition indicators and the extra maintenance. Based on this strategy, an optimal time interval can be determined. In this way, the proper moment when the maintenance is required can be determined. urthermore, the adjustment of the time intervals becomes uniform and less dependent of experienced personnel based on this strategy (section 6.3). As illustrated in the case study, the new strategy for the determination of the time intervals for the condition indicators and extra maintenance results in a more flexible condition control. However, it can be concluded that extra cost can be incurred for several components by applying the new strategy for the determination of the time intervals, while expenditures can be saved for other components (section 6.5). The possibility of extra cost or savings is dependent on the condition of the asset at time of the implementation of the new strategy. 86

87 Health indexing process Each health state of a component is classified to the given viewing periods (remaining lifetime) and the possibility of additional maintenance. By extending the health index levels from 4 to 6 levels, each health state of a component can be given precisely. The interpretation of the updated health index levels is unambiguous. The maintenance planner can determine from the interpretation of each health index level if additional maintenance is possible or not and to which viewing period the remaining lifetime is classified. The subjectivity of the planning of the maintenance will be minimized in this way (section 4.4). 7.2 Recommendations The recommendations which come forth from this research are divided in 2 parts. irst, there are recommendations for further study. Secondly, there are recommendations for the data collection and data analyses. urther study: The result of a condition indicator is determined based on the worst result of the preventive inspections involved for that condition indicator. The weighting factor of the preventive inspections is not taken into account in the assessment of the condition indicators. The preventive inspections can be weighted and if required, included in the assessment of the condition indicators. In this research, the weighting of the condition indicators was determined for the circuit breaker, grounding and control circuit. The determination of the weighting factors of the condition indicators must be performed for the other components in the grid as well. The location of a component in a network can make the component less or more critical. A methodology has to be determined in order to involve the criticality based on the location of a component in the weighting of the condition indicators. A study has to be performed in order to determine the failure frequencies which are given in the condition levels quantitatively. There are basic time intervals (BTI) for the assessment of condition indicators, namely: 1, 3, 6, 9 and 12 years. Using a strategy, these BTI are adjusted with a time interval ( TI). TI is determined based on assumptions. A methodology should be developed to determine TI for each BTI more precisely. In the case study was assumed that the failure frequency during respectively scenario 1 and scenario 2, and scenario 3 and scenario 4 are equal. Due to a more flexible condition control at scenario 2 and scenario 4, more failures can be prevented. In this way, the failure frequency can be decreased. A study can be 87

88 performed which will take the prevention of the failures into account in order to determine the expenditures of each scenario more precisely. It was assumed that the regular maintenance can be performed in maximum 2 time intervals for each component. Dependent on the activities during the inspection for the several condition indicators, it should be considered for the different type of components if it is economically efficient to perform the regular maintenance in 2 time intervals or not. or example, for several components the component has to be opened during the inspections. After that, a number of sub-components (condition indicators) in the component are inspected. It can take a lot of time to open such a component. It is economically not efficient to open such a component and assess only 1 condition indicator, while 3 condition indicators have to be assessed. It should be determined if the application of the new strategy for the determination of the time intervals for the complete grid will result in savings or extra expenditures. In the case that extra costs will be incurred, it should be determined if savings will achieved after a certain period of application of the new strategy. It can be considered if the additional costs which are incurred for a component in the last period of the lifetime of the component due to frequent inspections outweigh an early replacement. Data collection and data analyses: The link between the occurring failures and condition indicators has to be investigated. The weighting of the condition indicators can be determined more precisely in this way. The results of the condition indicators and the related occurring failures have to be analyzed in order to determine the detection chance of each condition indicator more precisely. The failure statistics have to be analyzed in order to confirm if the occurring failures meets the failures frequency as expected from the condition of the asset. 88

89 References [1] Asset anagement of Electrical Infrastructures Prof. dr. Johan J. Smit. [2] You cannot manage what you cannot measure: information systems based asset management perspective Abrar Haider, Andy Koronios, Gerald Quirchmayr. [3] aintenance strategies Adoghe Uwakhonye Anthony, [4] Whole life management of physical assets, BSI PAS 55:2008 The Institute of Asset anagement. [5] Asset anagement, Specification for the optimized management of physical assets PAS 55-1:2008. [6] Condition assessment of power system equipment, the impact of ageing and deterioration Athanasios Krontiris and Gerd Balzer, [7] High voltage asset performance modeling Evert J. de Haan, [8] aintaining systems with dependent failure modes and resource constraints aoyin Chen, Cong Xu and Donghua Zhou, IEEE, [9] [10] Information strategy for decision support in maintaining high voltage infrastructure Benjamin Quak, IEEE, [11] Asset condition assessment by health index benchmarking Sasa iletic, CIRED, [12] Guide for transformer maintenance Cigre, Working Group A2.34, [13] Asset management in practice TenneT TSO rank Wester. [14] aintenance and Replacement Strategies Dr. George Anders, Dr. Lina Bertling, Dr. Gerard Cliteur, Dr. John Endrenyi, Dr. Andrew Jardine and Dr. Wenyuan Li, IEEE, [15] Condition health indices and probabilities of failure Thor Hjartarson, Kinectrics. [16] vices/administration transmission_grid/maintenance_strategy.aspx. [17] Kwaliteits- en capaciteitsdocument 2011 (Elektriciteit 150kV-netten) Stedin, [18] [19] Kwaliteits- en Capaciteitsplan deel1 TenneT. [20] Technische Onderhoudsrichtlijnen (TOR 2.1) TenneT. [21] [22] [23] Challenges of Asset anagement in Power Transmission Network Lutfiye Allahmanli, Gopi Chattopadhyay and Gary Edwards, IEEE, [24] Steady-state voltage profile and reactive power balance for EHC AC cable systems in the Randstad 380 project P.N. Gockel, [25] NEN Condition assessment methodology, [26] Circuit Breaker aintenance ethod Optimization Indera Arifianto and Yokeu Wibisana, Proceedings of the 2010 International Conference on Condition onitoring and Diagnosis, 2010, Japan. [27] inal report of the second international enquiry on high voltage circuit breaker failures and defects in service Working Group 06 (Reliability of HV circuit-breakers) of Study Committee 13 (Switching Equipment), CIGRE, [28] Transients in power systems Lou van der Sluis,

90 [29] Power system grounding arjan Popov, [30] Recommended practice for powering and grounding electronic equipment Thomas. Gruzs and Christopher J. elhorn, IEEE, [31] ailure survey on circuit breaker control systems, Cigre, Harley Wilson, ark Blundell, Jens Burger, Thomas Haas, Peter Högg, Antoni Hyrczak, Hiroki Ito, Thierry Jung, Thomas Küng, Eva Pagán, Gunvantray Patel, Stanislaw Pokora and René Smeets [32] NEN-EN-IEC 60812, Analysis techniques for system reliability Procedure for failure mode and effects analysis (EA), [33] Condition assessment of transmission network infrastructure J.J. Smit and E. Gulski, IEEE, [34] Condition monitoring on power systems rancisco Poza, Perfecto arino, Santiago Otero and Vicente Pastoriza, 19 th International Conference on Systems Engineering, IEEE, [35] A Novel Approach to Predictive Condition onitoring and Knowledge anagement in Power Systems urad Akhrarov and Peter illett. [36] Specificatie berichtenverkeer conditie indicatoren Wim Wolferink, [37] aintenance and Replacement Strategies IEEE PES G, [38] Determination of transformer health condition using artificial neural networks Ahmed E. B. Abu-Elanien,.. A. Salama and alak Ibrahim, IEEE, [39] Entropy Weight Health index ethod of Power Transformer Condition Assessment Yan Zhou, Lin a and Jian Yang and Cong Xia, IEEE, [40] Kwaliteits- en Capaciteitsdocument Elektriciteit Stedin. [41] Asset anagement Challenges in Ageing Power Systems D.. Allan, IEEE, [42] On Distribution Asset anagement: Development of Replacement Strategies iroslav Begovic, Joshua Perkel, Nigel Hampton and Rick Hartlein, IEEE, [43] [44] Presentation: Is de NEN 2767 een instrument om de installatie verantwoordelijkheid te kunnen gebruiken/toepassen? Joulz, [45] Guide for the selection of monitoring for circuit breakers IEEE, [46] Productblad deel tariefblad template v1.0 Joulz,

91 List of igures igure 1.1. Lack of guidelines, ambiguousness of interpretations and ageing workforce can lead to a less effective condition indexing, health indexing and regular maintenance planning. Based on the resulting condition of the asset and health index, the extra maintenance can be planned suboptimally. Suboptimal maintenance planning can result in an increase of the expenditures and a rigid condition control igure 1.2. The objectives of the research are the achievement of a more effective condition and health indexing of a component. This will lead to optimal maintenance planning, resulting in savings on expenditures and a more flexible condition control. The process steps, condition index levels, visual inspections, condition of asset, time intervals of inspections and the health index levels have to be optimized. In order to achieve the optimizations, the focus will be on quality, objectivity, uniformity and user friendly igure 1.3. Process steps, which will be optimized in this research are highlighted. aintenance and preventive inspections are performed for each asset. Based on the preventive inspections, the condition indicators are assessed. Based on the result of the condition indicators, the condition of the asset and the health index can be determined for the component. The strategy to determine the condition of the asset based on the results of the condition indicators is named condition function igure 2.1. Criticality matrix in order to determine the criticality of a failure mode by combining the failure frequency and failure impact. The criticality is categorized: low, moderate, high and extreme igure different bathtub curves are shown in the figure (blue, green, brown and orange). The 3 periods of each bathtub curve are also shown in the figure, namely decreasing failure rate, constant failure rate and increasing failure rate igure 2.3. Classification of the maintenance methodologies. There are three maintenance methodologies, namely: Corrective aintenance, Condition Based aintenance and Time Based aintenance [14] igure 3.1. The health index is based on the remaining lifetime which in turn is based on the expected remaining lifetime and the condition indicators. The condition of asset is based on the results of the condition indicators. The health index, condition index and business values such as financial restrictions are the input for the risk analysis. Based on the risk analysis, the maintenance activities can be determined igure 3.2. Current health index levels of TenneT based on the remaining lifetime and the possibility of additional maintenance [13]. The remaining lifetime is categorized to standard remaining lifetime intervals, namely: RL > 7 years, 3 < RL < 7 years and RL < 3 years igure 3.3. rames indicating the process steps in the model performed by the SP (green frame) and the TSO (yellow frame). The condition indexing process is performed by the SP. The condition indicators and condition of asset are delivered to the TSO. The TSO performs the health indexing process igure 4.1. Process steps which will be optimized are shown in the current model. Each asset has a set of preventive inspection, which is performed after a specific time interval. Based on the preventive inspections, the condition indicators are assessed. With the aid of a condition function, the results of the condition indicators can be transformed to the condition of the asset. The condition indicators are also involved to determine the health index igure 4.2. atrix indicating the current condition levels based on its interpretation, namely the states of the condition and the requirement of additional maintenance. The interpretation of the condition levels can be ambiguous, because of the broad indication of the condition. The question marks show the shortcomings in the current matrix igure 4.3. atrix indicating the health index levels based on the remaining lifetimes and possibility of additional maintenance. The question marks show the levels with limitations in the 91

92 matrix. air gives two states of the health. It is not clear if addition maintenance is possible for good. RL means remaining lifetime igure 4.4. lowchart indicating the guidelines in order to assess the visual inspections. The guidelines consist of standard questions, concrete answers and a transformation matrix igure 4.5. lowchart including steps to be followed for the determination if the weighting of the condition indicators and optimization of the condition function by including the weighting of the condition indicators. The condition indicators and failure modes of a component are linked to each other. Each link will be weighted based on the parameters, failure frequency, failure impact and detection chance. These 3 parameters will be transformed to a weighting with the help of a risk matrix and a criticality matrix. A strategy will be constructed to include the weighting of the condition indicator in the determination of the condition of the asset igure 4.6. Parameters determining the weighting of a link between condition indicator and failure mode igure 4.7. Example of a risk matrix. Two parameters, failure frequency and failure impact are combined. The risk can be expressed in five categories: A, B, C, D and E igure 4.8. Example of a criticality matrix. 2 parameters, failure risk and detection chance are combined igure 4.9. Condition levels projected on the bathtub curve igure The updated condition levels are projected in a matrix. The condition levels are based on the state of the condition and the requirement of additional maintenance igure Updated condition levels projected on the bathtub curve igure Updated health index levels are shown in a matrix. RL means remaining lifetime igure 5.1. actors indicating the link between the failure mode and the condition indicator igure 6.1. Classification of the condition indicator levels and the condition levels. The transformation from the condition indicator level to the condition level is also shown. It is assumed that the transformation will be based on the criticality of the condition indicator igure 6.2. The transformation of the condition indicator level to the condition level based on the weighting of the condition indicator is shown in a diagram igure 6.3. Projection of the range of the estimation of the condition of the asset for the current and new strategy igure 6.4. The impact of the process steps which are optimized is shown. Based on the optimized aspect included in the condition indexing process, the regular and extra maintenance planning can be determined optimally igure 6.5. The purpose of the case study is presented in the diagram. The case study will prove if savings on expenditures and a more flexible condition control can be achieved. In scenario 1 and 3 the current strategy for the determination of the time interval is performed, while the new strategy is performed at scenario 2 and igure 6.6. The results of the condition indicators of scenario 1 are plotted on a time line. The time intervals for each condition indicator are adjusted for the scenario 2 for regular maintenance 2. The results of the condition indicators are matched with the results in scenario 1. This process is repeated for each regular maintenance activity. The number gives the number of the regular maintenance. gives the extra maintenance igure A.1. The Dutch high voltage network, 2007 [23] igure.1. The classification of the possible combinations when 3 condition indicators are related to a failure mode and their related probability. Third CI indicates the third condition indicator which is not involved in the first group, 2 condition indicators which are both good or bad igure.2. The classification of the possible combinations when two condition indicators are related to a failure mode and their related probability igure.3. Parameters included in the formula for the determination of the detection chance is shown in the diagram

93 igure N.1. The 2 scenarios illustrating the regular maintenance and extra maintenance including the time intervals. Regular indicates the number of the regular maintenance. Extra indicates the number of the extra maintenance. Regular 1 is given as the current case (0 years) igure N.2. The results of the condition indicators of scenario 3 are plotted on a time line. The time intervals for each condition indicator are determined for the scenario 4 for regular maintenance 4. The results of the condition indicators are matched with the results in scenario 3. This process is repeated for each regular maintenance activity. The number indicates the number of the regular maintenance. indicates the extra maintenance

94 94

95 List of Tables Table 1.1. actors in the process steps which can lead to inaccuracies in determining the condition and health indexing process are shown Table 2.1. Comparison of the expected remaining lifetime and the remaining lifetime. The process steps which are included for the determination of the expected remaining lifetime and the remaining lifetime are shown Table 2.2. Comparison of the condition and health assessment. The process steps which are included in the condition and health assessment process are marked Table 3.1. The health index levels and related interpretation are shown Table 4.1. Different condition indicators as prescribed in TOR 2.1 and their units or categorized levels Table 4.2. The visually inspected condition indicators Table 4.3. Example of a table in which failure modes and condition indicators can be linked. X means that there is a link between the condition indicator and the failure mode, and means there is no link between the condition indicator and the failure mode Table 4.4. Example of the categorization of the parameters detection chance and failure risk. The detection chance vary between 0 and 1, and the failure risk between 1 and 100. The parameters are equally categorized Table 4.5. Categorization of the condition indiators, condition of asset and health index Table 4.6. The updated condition levels. The moderate level and unusable level are added Table 4.7. The updated health index levels. Based on the current health index levels which provides an ambiguous interpretation, the number of health index levels are extended. The excellent level and the moderate level are added Table 4.8. The updated health index levels and related interpretations Table 5.1. Example of a ECA of a specific component or system. The scores can differ for each component. The failure impact is based on the the parameters safety, system, environment and cost Table 5.2. Available combinations of operating mechanisms and insulating media for circuit breakers Table 5.3. The condition indicators of each component and their code numbers Table 5.4. ailure modes of the circuit breaker and their related code numbers Table 5.5. ailure modes of the grounding and their related code numbers Table 5.6. ailure modes of the control circuit and their related code numbers Table 5.7. Determination of the link between CI 1 and Table 5.8. Link of the condition indicators and failure modes for a circuit breaker Table 5.9. Link of the condition indicators and failure modes for an GIS-circuit breaker Table Link of the condition indicators and failure modes for the grounding Table Link of the condition indicators and failure modes for the GIS-grounding Table Link of the condition indicators and failure modes for the control circuit Table The condition indicators CI 1, CI 3, and CI 5 are related to failure mode Table The meaning of the condition indicators Table The determination of the probability of the combination occurrence and the probability of the failure occurence for each combination Table The detection chance of the links Table Ranks and interpretation of the failure frequency Table Ranks, intervals and related interpreatation of the failure impact Table Ranks, intervals and interpreatation of the detection chance Table Risk matrix based on the failure impact and failure frequency [32] Table Criticality matrix based on the failure risk and the detection chance

96 Table An example to illustate method 1 to determine the weighting of the condition indicator Table An example to illustate method 2 to determine the weighting of the condition indicator Table The 3 parameters failure frequency, failure impact and detection chance are shown for a number of links. urthermore, the failure risk and criticality are given Table The weighting of each condition indicator for the circuit breaker Table The weighting of each condition indicator of the circuit breaker, grounding and control circuit Table standard questions and the related answers. The answers are categorized. The lay-out of the anwers and questions on the inspection sheet is also shown in the table Table 6.2. atrix for the transformation of the parameters size and intensity Table 6.3. The method of reporting visual inspections for a components is shown for the current model and new model Table 6.4. An example illustrating the determination of the condition of the asset Table 6.5. Time intervals for condition indicators with a time interval equal to BTI. These time intervals are based on the result and weighting of the condition indicator. These intervals are expressed in years Table 6.6. Example illustrating the determination of the time intervals for the GIS-circuit breaker.77 Table 6.7. The results of the condition indicators and condition of the asset of Grouniding A Table 6.8. The results of the condition indicators and condition of the asset of Grouniding A. The time interval for the condition indicators are determined, based on the new strategy Table 6.9. The expenditures of each scenario during a period of 30 years. Regular gives the number of the regular maintenance. Extra gives the number of the extra maintenance Table 1. Abbreviations of concepts Table B.1. ECA of a circuit breaker system Table E.1. General EA of a circuitbreaker [42] Table D.1. Link of the condition indicators and failure modes of an air-circuit breaker Table D.2. Link of the condition indicators and failure modes of a gas-circuit breaker Table D.3. Link of the condition indicators and failure modes of a gas-circuit breaker Table D.4. Link of the condition indicators and failure modes of an oil-circuit breaker Table D.5. Link of the condition indicators and failure modes of a gas-circuit breaker Table D.6. Link of the condition indicators and failure modes of an oil-circuit breaker Table D.7. Link of the condition indicators and failure modes of an air-gis-circuit breaker Table D.8. Link of the condition indicators and failure modes of a gas-gis-circuit breaker Table D.9. Link of the condition indicators and failure modes of a gas-gis-circuit breaker Table D.10. Link of the condition indicators and failure modes of an oil-gis-circuit breaker Table D.11. Link of the condition indicators and failure modes of a gas-gis-circuit breaker Table D.12. Link of the condition indicators and failure modes of an oil-gis-circuit breaker Table.1. Possible combinations when two condition indicators are linked to a failure mode Table.2. Possible combinations when three condition indicators are linked to a failure mode Table.3. Indications for the determination of the probability of the failure occurence for each combination when 2 condition indicators are related to a failure mode Table.4. The determination of the probability of the failure occurence for each combination when 3 condition indicators are related to a failure mode Table G.1. The condition indicator which is related to 2, 3 and Table G.2. The detection chance of the links CI 2-2, CI 2-3 and CI Table G.3. The condition indicators which are related to the Table G.4. The determination of the failure probability for each combination based on CI 1, CI 3 and CI Table G.5. The detection chance of the links CI 1 8, CI 3 8 and CI Table G.6. The condition indicator which is related to

97 Table H.7. The detection chance of the link CI Table G.8. The condition indicators which are related to Table G.9. The determination of the failure probability for each combination based on CI 3, CI 4 and CI Table G.10. The detection chance of the links CI 3 11, CI 4 11 and CI Table G.11. The condition indicators which are related to Table G.12. The determination of the failure probability for each combination based on CI 3, CI 4 and CI Table G.13. The detection chance of the links CI 3 12, CI 4 12 and CI Table G.14. The condition indicators which are related to Table G.15. The determination of the failure probability for each combination based on CI 1, CI 3 and CI Table G.16. The detection chance of the links CI 1 13, CI 3 13 and CI Table G.17. The condition indicators which are related to Table G.18. The determination of the failure probability for each combination based on CI 1 and CI Table G.19. The detection chance of the links CI 1 5 and CI Table G.20. The condition indicators which are related to Table G.21. The determination of the failure probability for each combination based on CI 1 and CI Table G.22. The detection chance of the links CI 1 6 and CI Table G.23. The condition indicators which are related to Table G.24. The determination of the failure probability for each combination based on CI 3, CI 4 and CI Table G.25. The detection chance of the links CI 1 7, CI 3 7 and CI Table G.26. The condition indicator which is related to Table G.27. The detection chance of the link CI Table G.28. The condition indicators which are related to Table G.29. The determination of the failure probability for each combination based on CI 7 and CI Table G.30. The detection chance of the links CI 7 1 and CI Table G.31. The condition indicators which are related to the 2, 3 and Table G.32. The detection chance of the links CI 6 2, CI 6 3 and CI Table G.33. The condition indicators which are related to Table G.34. The determination of the failure probability for each combination based on CI 6, CI 7 and CI Table G.35. The detection chance of the links CI 6 8, CI 7 8 and CI Table G.36. The condition indicators which are related to Table G.37. The determination of the failure probability for each combination based on CI 6 and CI Table G.38. The detection chance of the links CI 6 10 and CI Table G.39. The condition indicators which is related to Table G.40. The detection chance of the link CI Table G.41. The condition indicators which are related to Table G.42. The determination of the failure probability for each combination based on CI 7 and CI Table G.43. The detection chance of the links CI 7 12 and CI Table G.44. The condition indicator which is related to Table G.45. The detection chance of the link CI Table G.46. The condition indicators which are related to Table G.47. The determination of the failure probability for each combination CI 6 and CI Table G.48. The detection chance of the links CI 6 5 and CI

98 Table G.49. The condition indicators which are related to Table G.50. The determination of the failure probability for each combination based on CI 6 and CI Table G.51. The detection chance of the links CI 6 6 and CI Table G.52. The condition indicators which are related to Table G.53. The determination of the failure probability for each combination based on CI 1 and CI Table G.54. The detection chance of the links CI 6 7 and CI Table G.55. The condition indicators which are related to Table G.56. The determination of the failure probability for each combination based on CI 6, CI 7 and CI Table G.57. The detection chance of the links CI 6 9, CI 7 9 and CI Table G.58. The condition indicators which are related to Table G.59. The determination of the failure probability for each combination based on CI 10, CI 11 and CI Table G.60. The detection chance of the links CI 10 14, CI and CI Table G.61. The condition indicators which are related to Table H.62. The determination of the failure probability for each combination based on CI 9 and CI Table G.63. The detection chance of the links CI 9 15 and CI Table G.64. The condition indicators which are related to Table G.65. The determination of the failure probability for each combination based on CI 10 and CI Table G.66. The detection chance of the links CI and CI Table G.67. The condition indicators which are related to Table G.68. The determination of the failure probability for each combination based on CI 13, CI 14 and CI Table G.69. The detection chance of the links CI 13 14, CI and CI Table G.70. The condition indicators which are related to Table G.71. The determination of the failure probability for each combination based on CI 13 and CI Table G.72. The detection chance of the links CI and CI Table G.73. The condition indicators which are related to Table G.74. The determination of the failure probability for each combination based on CI 13 and CI Table G.75. The detection chance of the links CI and CI Table G.76. The condition indicators which are related to Table G.77. The determination of the failure probability for each combination based on CI 16 and CI Table G.78. The detection chance of the links CI and CI Table G.79. The condition indicators which are related to Table G.80. The determination of the failure probability for each combination based on CI 17 and CI Table G.81. The detection chance of the links and Table G.82. The condition indicators which are related to Table G.83. The determination of the failure probability for each combination based on CI 17 and CI Table G.84. The detection chance of the links CI and CI Table G.85. The condition indicators which are related to Table G.86. The determination of the failure probability for each combination based on CI 18 and CI Table G.87. The detection chance of the links CI and CI

99 Table H.1. The 3 parameters and criticality of each link Table I.1. The weghting of each condition indicator for the circuit breaker, grounding and control circuit Table J.1. Reporting of the size Table J.2. Reporting of the intensity Table J.3. Reporting of the smoothness Table J.4. Reporting of the severity Table J.5. Reporting the position Table J.6. Reporting of the firmness Table J.7. Reporting of the heating Table J.8. Reporting of the availability Table J.9. Reporting of the satisfaction of the checklist Table J.10. Reporting of the combination of the size and intensity Table J.11. Reporting of the combination of the size and severity Table J.12. Reporting of the combination of the smoothness and postion Table J.13. Reporting of the combination of the firmness and availibility Table J.14. Reporting of the combination of the firmness and availibility Table J.15. atrix for the transformation of the parameters size and intensity Table J.16. atrix for the transformation of the parameters size and severity Table J.17. atrix for the transformation of the parameters smooth and position Table J.18. atrix for the transformation of the parameters firm and available Table J.19. atrix for the transformation of the parameters size and available Table K.1. The method of reporting visual inspection for the circuit breakers Table K.2. The method of reporting visual inspection for the grounding Table K.3. The method of reporting visual inspection for the control circuit Table L.1. Time intervals for condition indicators with a basic time interval of 3 years. These time intervals are based on the result and weighting of the condition indicator Table L.2. Time intervals for condition indicators with a basic time interval of 6 years. These time intervals are based on the result and weighting of the condition indicator Table.1. Breakdown of the expenditures of maintenance Table N.1. The results of the condition indicators after the regular maintenance for scenario Table N.2. The results of the condition indicators and condition of the asset of a grouniding component Table N.3. The results of the condition indicators and condition of the asset of a grouniding component. The time interval for the condition indicators are determined, based on the new model Table N.4. The results of the condition indicators after the regular maintenance for scenario Table N.5. The expenditures of each scenario during a period of 30 years. Regular indicates the number of the regular maintenance. Extra indicated the number of the extra maintenance

100 BTI CI HV SP TI TOR 2.1 TSO List of Abbreviations Table 1. Abbreviations of concepts. Basic time interval Condition indicator High Voltage ailure mode Service Provider Delta time interval Technical aintenance Directives Transmission System Operator 100

101 Appendix A Appendices The high voltage grid in the Netherlands is shown in figure A.1. igure A.1. The Dutch high voltage network, 2007 [23]. 101

102 Appendix B ECA of a circuit breaker system is shown in table B.1 [26]. No. Sub system Sub sub system Interrupter 1 Electrical current carrying 2 Electrical insulation 3 Arch Extinguisher edium Interrupter accessores (if present) ain (bushing) terminal Insulator of interrupter chamber Insulator of supporting chamber Table B.1. ECA of a circuit breaker system. requency Impact ailure mode % Score Safety System Environment Cost Weight Asynchronous close/open operation Animal exists in the main terminal (clamp) Cracked or break insulator lashover Cracked or break insulator Broken indicator didn't show the real gas pressure Decrease of gas pressure Oil leakage Air pressure

103 4 Operating echanism 5 Control / Auxiliary Circuit 6 echanical Structure 7 Grounding System Control Panel Terminal& wiring control leakage Internal or external hydraulic oil leakage Pneumatic system leakage otor doesn't work Operating mechanism failure Water/ damp in the panel, contact terminal, auxiliary contact Animal get into the panel Bad wiring control Broken or exfoliated control cable Power supply doesn't work echanical ailure Change of ground level Broken grounding cable Loosen / missed grounding joint 103

104 Appendix C EA of a circuit breaker system is shown in table C.1 [42]. Table E.1. General EA of a circuitbreaker [42]. ailure mode ailure cause onitoring options ails to open on command Open or shorted trip coil onitor trip coil continuity or impedance Inappropriate or inadequate lubrication of trip latch or trip mechanism Loss of stored interrupting energy due to leaks, slippage, and breakage Control circuit failure Circuit breaker operation blocked echanism linkage failure between operating mechanism and interrupters Trip latch surface wear, deteriorated bearings, or deformation of trip latch flat surfaces 104 onitor trip coil energy consumed or current and voltage drop during time for circuit breaker to operate, or monitor the time for the circuit breaker to operate onitor stored energy pressure or position of stored energy springs onitor control circuit continuity and dc voltage at circuit breaker and controls. onitor trip coil current and auxiliary contact timing (on noncurrent carrying contact). Periodic insulation testing Self-monitoring and self-alarming of monitoring scheme. Trending of monitored parameters onitor primary current interruption during change of state of operating mechanism. onitor timing sequence between operating mechanism and interruption onitor trip coil energy (current and voltage drop) and time for circuit breaker to operate

105 Opens but fails to remain open echanism cabinet below required temperature External circuit failure, including wiring, battery, and protection devices echanism failure, loss of hold open. energy (e.g., loss of air pressure on air blast circuit breaker requiring air pressure to hold contacts open) ailure of anti-pumping scheme 105 onitor mechanism temperature or mechanism heater current and ambient temperature onitor station battery voltage at circuit breaker, continuity of trip circuitry, self-monitoring of electronic primary and backup protection devices onitor mechanism position and auxiliary contacts with respect to current flow and opening signal onitor number of operations over time period. onitor X and Y relay timing Opens but fails to interrupt Oil contamination Oil dielectric Low gas pressure or density (air or S6 ) Gas pressure or density as appropriate for ambient temperature Loss of vacuum Periodic vacuum integrity over potential test Insufficient contact onitor contact travel opening Arc chute failure Visual inspection, partial discharge monitor, and heater or temperature monitor Puffer failure. echanical failure isapplication or other situation onitor mechanism position and auxiliary contacts with respect to current flow and opening signal onitor system fault level and

106 Opens but fails to maintain open Contact insulation beyond circuit breaker capability Loss of vacuum echanism does not travel complete distance Loss of gas pressure Too many operations in a time period Dielectric stress exceeds the circuit Breaker capability 106 conditions, especially during short circuit interruption and breaker operation. Periodic review of system fault levels Power system disturbance recorder (including oscillographs and digital fault recorders) Periodic vacuum-integrity over potential test ull travel indication Gas pressure monitor onitor number of operations over time period onitor system voltage conditions, especially during short circuit interruption and breaker operation Lightning. Opens without command Trip latch not secure Change in current over time or change in speed to trip Stray current in trip circuit (such as from transients, caused by switching surges on adjacent wiring) Ground on trip circuit Self-protective feature of some circuit breakers (some air blast breakers) Loss of voltage on under voltage trip onitor current in trip coil onitor trip circuit for grounds onitor trend in operating stored Energy onitor voltage in under voltage trip supply circuit ails to close on command Defective close coil or solenoid onitor close coil circuit for possible increase in close current, or monitor closing time

107 Closes but fails to conduct current Closes without command ails to conduct continuous or momentary current (while already closed) Loss of stored energy Inappropriate lubrication Control circuit failure Contacts burnt away (electrically eroded) echanical linkage to contacts broken Loss of overtravel preventing full contact closing Stray current in close circuit (such as from transients caused by switching surges on adjacent wiring) Ground on close circuit Pilot valve not secure Spring release mechanism worn Vibration of circuit breaker High-resistance contacts Ablation of contacts 107 onitor spring position, air pressure onitor timing between main contacts and close coil current onitor control circuit onitor close coil current and auxiliary contact timing (on noncurrentcarrying contact) Power system disturbance recorder (including oscillographs and digital fault recorders) in primary current circuit onitor primary current start during change of state of operating mechanism onitor contact travel and over travel Current in close coil onitor close circuit for grounds Air pressure leaving pilot valve; monitor air compressor run time and pressure ovement of release mechanism Improper application or vibration isolation Infrared monitoring of contact temperature Infrared monitoring of contact temperature

108 Broken or missing contacts; parts in current carrying circuit; bolted joints, sliding, rolling, or moving main contacts; spring failure Loss of over travel and contact closing force 108 Infrared monitoring of contact temperature Infrared monitoring of contact and connection and temperature ails to provide insulation Loss of dielectric medium Gas pressure or density luid level Loss of dielectric integrity of oil Periodic test of oil condition Loss of vacuum Periodic vacuum-integrity over potential test oisture in S6 gas onitor S6 density and moisture Loss of compressed air dielectric onitor compressed air water content and temperature or relative humidity of compressed air Trend periodic insulation resistance and dielectric tests Damaged interrupter from external acts Excessive accumulated interrupted amperes Wear-generated particles in interrupter ails to provide insulation to ground Wildlife contact. Lightning strike. echanical damage to insulation. Water infiltration. Contaminated bushings. lashover caused by system transient event Station security monitor onitor interrupted amperes and breaker operations onitor partial discharge? Power system disturbance recorder (including oscillographs and digital

109 fault recorders) Excessive temperatures of insulating materials onitor ambient air or component temperature(s) ails to provide insulation between phases Wildlife contact. ails to provide insulation across the interrupter. external ails to provide insulation across the interrupter. internal Lightning strike. Ionization of surrounding insulating Partial discharge monitoring air caused by unusual service conditions Water infiltration Partial discharge monitoring oreign material Partial discharge monitoring Wildlife contact. Lightning strike. Water infiltration. Ionization of air during over duty fault System monitoring Excessive voltage applied to breaker System monitoring Dirt or pollution. Deterioration of interrupter exterior onitor partial discharge activity surfaces caused by partial discharge lashover of OPEN interrupter caused Power system disturbance recorder by system transient event Ionization of surrounding insulating Partial discharge monitoring air caused by unusual service conditions Loss of dielectric density Gas density Loss of dielectric integrity of oil Periodic test of oil condition Loss of vacuum Periodic vacuum integrity over potential test 109

110 ails to contain insulating medium ails to indicate condition or position ails to provide for safety in operation Excessive voltage applied to breaker ailure of seals, gaskets, corrosion, erosion, and porcelain rupture disk ailure of insulation gas density switch Stuck, broken, or defective indicator Auxiliary contacts, linkage, or wiring Overpressure of porcelain interrupter. Defects in porcelain Overpressure of pneumatic or hydraulic fluids, spring charging system ailure of interlocks Loss of gas and need to isolate Improper filling or adding liquid versus gas dielectric medium System monitoring onitor insulating medium level (liquids), density (S6), or pressure (air blast) onitor gas density variation as ambient temperature cycles to ensure density variation is appropriate. onitor indication with signal to open and close circuit, primary current, control circuit current, and stored energy charging system operation Auxiliary contacts, linkage, or wiring Pressure relief valve monitoring onitor circuit breaker stored energy device condition remotely onitor indication with signal to open and close circuit, primary current, control circuit current, and stored energy charging system operation onitor gas pressure/ density onitor gas pressure/ density 110

111 Appendix D The available links are shown for the circuit breaker and the GIS-circuit breaker. D.1 Circuit breaker Table D.1. Link of the condition indicators and failure modes of an air-circuit breaker. Circuit breaker (excluding GIS) Air-blast (Pneumatic) CI 1 X X X CI 2 X X X CI 3 X X X X X X CI 4 X X X CI 5 X X X X Table D.2. Link of the condition indicators and failure modes of a gas-circuit breaker. Circuit breaker (excluding GIS) Gas (Pneumatic) CI 1 X X X X CI 2 X X X CI 3 X X X X X X X CI 4 X X X CI 5 X X X Table D.3. Link of the condition indicators and failure modes of a gas-circuit breaker. Circuit breaker (excluding GIS) Gas (Hydraulic) CI 1 X X X X CI 2 X X X CI 3 X X X X X X CI 4 X X X CI 5 X X X 111

112 Table D.4. Link of the condition indicators and failure modes of an oil-circuit breaker. Circuit breaker (excluding GIS) Oil (Hydraulic) CI 1 X X X CI 2 X X X CI 3 X X X X X X CI 4 X X X CI 5 X X X X Table D.5. Link of the condition indicators and failure modes of a gas-circuit breaker. Circuit breaker (excluding GIS) Gas (Spring) CI 1 X X X X CI 2 X X X CI 3 X X X X X X CI 4 X X X CI 5 X X X Table D.6. Link of the condition indicators and failure modes of an oil-circuit breaker. Circuit breaker (excluding GIS) Oil (Spring) CI 1 X X X CI 2 X X X CI 3 X X X X X CI 4 X X X CI 5 X X X X 112

113 113 D.2 GIS- Circuit breaker Table D.7. Link of the condition indicators and failure modes of an air-gis-circuit breaker. GIS-Circuit breaker Air-blast (Pneumatic) CI 6 X X X X X CI 7 X X X X X X CI 8 X X X Table D.8. Link of the condition indicators and failure modes of a gas-gis-circuit breaker. GIS-Circuit breaker Gas (Pneumatic) CI 6 X X X X X X CI 7 X X X X X X X CI 8 X X Table D.9. Link of the condition indicators and failure modes of a gas-gis-circuit breaker. GIS-Circuit breaker Gas (Hydraulic) CI 6 X X X X X CI 7 X X X X X X CI 8 X X

114 114 Table D.10. Link of the condition indicators and failure modes of an oil-gis-circuit breaker. GIS-Circuit breaker Oil (Hydraulic) CI 6 X X X X X CI 7 X X X X X X CI 8 X X X Table D.11. Link of the condition indicators and failure modes of a gas-gis-circuit breaker. GIS-Circuit breaker Gas (Spring) CI 6 X X X X X CI 7 X X X X X X CI 8 X X Table D.12. Link of the condition indicators and failure modes of an oil-gis-circuit breaker. GIS-Circuit breaker Oil (Spring) CI 6 X X X X CI 7 X X X X X CI 8 X X

115 Appendix E E.1 Circuit breaker Explanation of each available link: CI 1-5 General condition shifts - Broken indicator didn't show the real gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 1-6 General condition shifts - decrease of gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 1-7 General condition shifts - oil leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 1-8 General condition shifts - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 1-13 General condition shifts - mechanical failure. The failure mode is based on the sub component: mechanical structure. Causes of this failure mode can be: contacts burnt away (electrically eroded), inappropriate lubrication, trip latch surface wear, deteriorated bearings and mechanism linkage failure between operating mechanism and interrupters. CI 2 2 Condition insulator - cracked or break insulator. The failure mode is based on the sub component: electrical Insulation. Causes of this failure mode can be: damaged interrupter from external acts, mechanical damage to insulation, contaminated bushings, ionization of air during over duty fault, excessive voltage applied to breaker, dirt or pollution, overpressure of porcelain interrupter and defects in porcelain. CI 2 3 Condition insulator flashover. The failure mode is based on the sub component: electrical Insulation. Cause of this failure mode can be: flashover caused by system transient event. CI 2 4 Condition insulator - cracked or break insulator. The failure mode is based on the sub component: electrical Insulation. Causes of this failure mode can be: damaged interrupter from external acts, mechanical damage to insulation, contaminated bushings, excessive voltage applied to breaker, dirt or pollution, overpressure of porcelain interrupter and defects in porcelain. 115

116 CI 3 1 Condition drives - asynchronous close/open operation. The failure mode is based on the sub component: electrical current carrying. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. CI 3-5 Condition drives - broken indicator didn't show the real gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 3-6 Condition drives - decrease of gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, corrosion, erosion and porcelain rupture disk. CI 3-7 Condition drives - oil leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 3-8 Condition drives - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 3-9 Condition drives - internal or external hydraulic oil leakage. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and slippage. CI 3-10 Condition drives - pneumatic system leakage. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and breakage. CI 3-11 Condition drives - motor doesn't work. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and breakage. CI 3-12 Condition drives - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. 116

117 Causes of this failure mode can be: failure of seals, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and slippage. CI 3-13 Condition drives - mechanical failure. The failure mode is based on the sub component: mechanical structure. Causes of this failure mode can be: contacts burnt away (electrically eroded), inappropriate lubrication, trip latch surface wear, deteriorated bearings, and deformation of trip latch flat surfaces and mechanism linkage failure between operating mechanism and interrupters. CI 4-11 Condition contacts - motor doesn't work. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: contacts burnt away (electrically eroded) and inappropriate lubrication. CI 4-12 Condition contacts - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: contacts burnt away and inappropriate lubrication. CI 4-13 Condition contacts - mechanical failure. The failure mode is based on the sub component: mechanical structure. Causes of this failure mode can be: contacts burnt away (electrically eroded), inappropriate lubrication, trip latch surface wear, deteriorated bearings, and deformation of trip latch flat surfaces and mechanism linkage failure between operating mechanism and interrupters. CI 5 1 The average motor current - asynchronous close/open operation. The failure mode is based on the sub component: electrical current carrying. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. CI 5 7 The average motor current - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, spring release mechanism worn, loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 5 8 The average motor current - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, spring release mechanism worn, loss of gas and need to isolate, failure of seals and porcelain rupture disk. CI 5-11 The average motor current - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. 117

118 CI 5-12 The average motor current - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and spring release mechanism worn. E.2 GIS-Circuit breaker Explanation of each available link: CI 6 2 Overall condition - cracked or break insulator. The failure mode is based on the sub component: electrical Insulation. Causes of this failure mode can be: damaged interrupter from external acts, mechanical damage to insulation, contaminated bushings, ionization of air during over duty fault, excessive voltage applied to breaker, dirt or pollution, overpressure of porcelain interrupter and defects in porcelain. CI 6 3 Overall condition flashover. The failure mode is based on the sub component: electrical Insulation. Cause of this failure mode can be: flashover caused by system transient event. CI 6 4 Overall condition - cracked or break insulator. The failure mode is based on the sub component: electrical Insulation. Causes of this failure mode can be: damaged interrupter from external acts, mechanical damage to insulation, contaminated bushings, excessive voltage applied to breaker, dirt or pollution, overpressure of porcelain interrupter and defects in porcelain. CI 6-5 Overall condition - broken indicator didn't show the real gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 6-6 Overall condition - decrease of gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 6-7 Overall condition - oil Leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 6-8 Overall condition - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk. CI 6-9 Overall condition - internal or external hydraulic oil leakage. The failure mode is based on the sub component: operating mechanism. 118

119 Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and slippage. CI 6-10 Overall condition - pneumatic system leakage. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and breakage. CI 7 1 Condition drives - asynchronous close/open operation. The failure mode is based on the sub component: electrical current carrying. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. CI 7-5 Condition drives - broken indicator didn't show the real gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 7-6 Condition drives - decrease of gas pressure. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, corrosion, erosion and porcelain rupture disk. CI 7-7 Condition drives - oil Leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 7-8 Condition drives - air pressure Leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 7-9 Condition drives - internal or external hydraulic oil leakage. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and slippage. CI 7-10 Condition drives - pneumatic system leakage. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, corrosion, erosion, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and breakage. 119

120 CI 7-11 Condition drives - motor doesn't work. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, gaskets, corrosion, erosion, and porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and breakage. CI 7-12 Condition drives - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: failure of seals, porcelain rupture disk, loss of stored energy, self-protective feature of some circuit breakers, loss of stored interrupting energy due to leaks and slippage. CI 7-13 Condition drives - mechanical failure. The failure mode is based on the sub component: mechanical structure. Causes of this failure mode can be: contacts burnt away (electrically eroded), inappropriate lubrication, trip latch surface wear, deteriorated bearings, and deformation of trip latch flat surfaces and mechanism linkage failure between operating mechanism and interrupters. CI 8 1 The average motor current - asynchronous close/open operation. The failure mode is based on the sub component: electrical current carrying. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. CI 8 7 The average motor current - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, spring release mechanism worn, loss of gas and need to isolate, failure of seals, gaskets, corrosion, erosion and porcelain rupture disk. CI 8 8 The average motor current - air pressure leakage. The failure mode is based on the sub component: arch extinguisher medium. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, spring release mechanism worn, loss of gas and need to isolate, failure of seals and porcelain rupture disk. CI 8-11 The average motor current - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and breakage, and spring release mechanism worn. CI 8-12 The average motor current - operating mechanism failure. The failure mode is based on the sub component: operating mechanism. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, slippage and spring release mechanism worn. 120

121 E.3 Grounding Explanation of each available link: CI 9-15 Condition insulator - broken grounding cable. The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: mechanical damage to insulation, excessive voltage applied to breaker and dirt or pollution. CI Condition drives - change of ground level. The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: contacts burnt away, inappropriate lubrication, surface wear, deteriorated bearings, and deformation of trip latch flat surfaces. CI Condition drives - broken grounding cable. The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: inappropriate lubrication, surface wear, deteriorated bearings, and deformation of trip latch flat surfaces. CI Condition drives - loosen / missed grounding joint. The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: contacts burnt away and inappropriate lubrication. CI Condition contacts - change of ground level. The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: contacts burnt away. CI Condition contacts - loosen / missed grounding joint. The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: contacts burnt away, inappropriate lubrication, trip latch surface wear and deteriorated bearings. CI The average motor current - change of ground level. The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, breakage, loss of gas and need to isolate, failure of seals and corrosion. E.4 GIS-Grounding Explanation of each available link: CI Overall condition - change of ground level The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: contacts burnt away, inappropriate lubrication, surface wear, deteriorated bearings, and deformation of trip latch flat surfaces. CI Overall condition - broken grounding cable The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: mechanical damage to insulation, excessive voltage applied to breaker and dirt or pollution. 121

122 CI Overall condition - loosen / missed grounding joint The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: contacts burnt away, inappropriate lubrication, trip latch surface wear and deteriorated bearings. CI Condition drives - change of ground level The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: contacts burnt away, inappropriate lubrication, surface wear, deteriorated bearings, and deformation of trip latch flat surfaces. CI Condition drives - broken grounding cable The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: inappropriate lubrication, surface wear, deteriorated bearings, and deformation of trip latch flat surfaces. CI Condition drives - loosen / missed grounding joint The failure mode is based on the sub component, grounding system. Causes of this failure mode can be: contacts burnt away and inappropriate lubrication. CI The average motor current - change of ground level The failure mode is based on the sub component, mechanical structure. Causes of this failure mode can be: loss of stored interrupting energy due to leaks, breakage, loss of gas and need to isolate, failure of seals and corrosion. E.5 Control circuit Explanation of each available link: CI Condition functioning and signaling control - water/ damp in the panel, contact terminal, auxiliary contact. The failure mode is based on the sub component, control panel. Causes of this failure mode can be: dirt in the control circuit or mechanical structures. CI Condition of the interfacing - bad wiring control. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities of the heating cabinet, relay or bundling wires. CI Condition of the interfacing - broken or exfoliated control cable. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities of the heating cabinet, relay or bundling wires. CI Condition additional funtion to the control - Power supply doesn't work. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities with the backup battery. 122

123 CI Outcome live test - Water/ damp in the panel, contact terminal, auxiliary contact. The failure mode is based on the sub component, control panel. Causes of this failure mode can be: dirt in the control circuit or mechanical structures. CI Outcome live test - bad wiring control. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities of the heating cabinet, relay or bundling wires. CI Outcome live test - broken or exfoliated control cable. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities of the heating cabinet, relay or bundling wires. CI Outcome live test - power supply doesn't work. The failure mode is based on the sub component, terminal and wiring control. Causes of this failure mode can be: irregularities with the backup battery. 123

124 Appendix.1 Introduction to the determination of the detection chance Boolean The determination of the detection chance will be based on the condition indicator which is in a bad condition, therefore, it will be assumed that the result of the condition indicator can be good or bad. The parameter boolean will be used in order to indicate good and bad. According to stochastical theory, a boolean is a data type with only 2 possible values, true (1) and false (0), in order to represent the value of logical expression. The true value indicates that the result of the condition indicator is good and the false value indicates that the result of the condition indicator is bad. Possible combinations As indicated in the previous section, failure modes are linked to 1, 2 or 3 condition indicators for the components involved in this research. Based on the results of the condition indicators which can be true (1) or false (0) and the number of related condition indicators, different combinations can occur for each failure mode. or 1 condition indicator, there could be 2 combinations: the condition indicator is 0 or 1. When 2 condition indicators are linked to a failure mode, 4 combinations are possible. The combinations are shown in table.1. Table.1. Possible combinations when two condition indicators are linked to a failure mode. CI A CI B When 3 condition indicators are linked to a failure mode, 8 combinations are possible. The combinations are shown in table.2. Table.2. Possible combinations when three condition indicators are linked to a failure mode. CI A CI B CI C

125 ormula The formula for the determination of the detection chance can be illustrated with an example. In the example, the failure mode ( A) is related to 3condition indicators (CI A, CI B and CI C). The detection chance for condition indicator A and failure mode A or the probability that failure mode A occurs when the condition indicator A (CI A) is bad, is determined in formula.1. Detection chance A CI A = P ( A CI A = 0) = P ( A CI A = 0, CI B = 0, CI C = 0). P (CI A = 0, CI B = 0, CI C = 0) + P ( A CI A = 0, CI B = 1, CI C = 0). P (CI A = 0, CI B = 1, CI C = 0) + P ( A CI A = 0, CI B = 1, CI C = 1). P (CI A = 0, CI B = 1, CI C = 1) + P ( A CI A = 0, CI B = 0, CI C = 1). P (CI A = 0, CI B = 0, CI C = 1). (.1) In this formula: P ( A CI A, CI B, CI C), indicates the probability of the occurrence of failure mode A, for a specific combination based on the results of CI A, CI B and CI C. P (CI A, CI B, CI C), indicates the probability of the occurrence of a specific combination based on the result of CI A, CI B and CI C. Basic definitions The formula illustrated above is based on a number of basic formulas in the probability theory. These basic formulas are: Definition of condition probability P( X x Y y) P (X=x Y=y) = P( Y y) (.2) Definition of marginal distribution P (Y=y) = ( X x, Y y) (.3) x Based on formula.1, 2 parameters have to be determined for each possible combination, namely: the probability of the occurrence of each possible combination the failure probability for each failure mode at each combination..2 Probability of the occurrence of each possible combination As given in formula (.1), the probability of the occurrence of each possible combination has to be determined. The probability of the occurrence of each possible combination will be based on the mutual relation of the condition indicators related to each failure mode. The relation between the condition indicators will be based on the function of the subcomponent included in the condition indicator. The condition indicators are related when the indicated sub-components need each other to function. The mutual requirement of the sub-components is determined on basis of the theoretical view of the functioning of a component. 125

126 It will be assumed that the probability that the condition indicators which are related to each other having an equal condition (both 0 or both 1) is greater than condition indicators which are not related to each other. Based on this assumption, the possible combinations will be classified in categories. or each category there will be a standard probability. The standard probability will be determined based on stochastic theory. The classification of the combinations will be performed for the cases where: 3 condition indicators are related to a failure mode 2 condition indicators are related to a failure mode. 3 condition indicators related to a failure mode In this case, the combinations can be classified in 2 groups: All condition indicators are good or bad (2 combinations) 2 condition indicators are good or bad (6 combinations) The classification of the possible combinations based on the mutual relations is shown in figure.1. irst, all the possible combinations (8) are divided into the 2 groups. Afterwards, the combinations are divided based on their mutual relations. or each resulting category a standard probability is assigned. The standard probabilities are probability A, probability B, probability C and probability D. igure.1. The classification of the possible combinations when 3 condition indicators are related to a failure mode and their related probability. Third CI indicates the third condition indicator which is not involved in the first group, 2 condition indicators which are both good or bad. The standard probabilities and their interpretations are: Probability A = 0,10. When there are 2 condition indicators with equal condition (both 0 or 1), which are not related to each other. The third condition indicator (unequal condition) is related to the2 or one of the 2 condition indicators which has 126

127 an equal condition or the third condition indicator (equal condition) is not related to the 2 or one of the 2 condition indicators (equal condition). Probability B = 0,15. When there are 2 condition indicators with equal condition (both 0 or 1), which are not related to each other. The third condition indicator (unequal) is not related to the 2 condition indicators (equal condition). Probability C = 0,20. When there are 2 condition indicators with equal condition (both 0 or 1), which are related to each other. The third condition indicator (unequal condition) is related to the 2 condition indicators (equal condition) or if the third condition indicator (equal condition) is related to the 2 condition indicators (equal condition). Probability D = 0,25. When there are 2 condition indicators with equal condition (both 0 or 1), which are related to each other. The third condition indicator (unequal condition) is not related to the other 2 condition indicators (equal condition) or the third condition indicator (equal condition) is related to the other 2 condition indicators (equal condition). The maximum probability can be 0,25 when three condition indicators are related to a failure mode, based on the probability theory. 2 condition indicators related to a failure mode In this case, the possible combinations can be classified in two groups: All condition indicators are bad or good (2 combinations possible) 1 condition indicator is bad or good (2 combinations possible). The classification of the combination based on the mutual relations is shown in figure.2. The standard probabilities are probability D and probability E. igure.2. The classification of the possible combinations when two condition indicators are related to a failure mode and their related probability. The standard probabilities and their interpretations are: Probability E = 0,25. When there are 2 condition indicators (equal condition), which are not related to each other or when there are 2 condition indicators (unequal condition), which are related to each other. 127

128 Probability = 0,50. When there are 2 condition indicators (equal condition), which are related to each other or when there are 2 condition indicators (unequal condition), which are not related to each other. The maximum probability in this case can be 0,5 (Probability E), based on the probability theory..3 ailure probability of each possible combination Another parameter which has to be determined for the determination of the detection chance is the failure probability. The failure probability is dependent on the failure frequency. Based on the failure probability, the probability of the failure occurrence can be determined. In order to determine the probability of the failure occurrence, a number of assumptions have to be made. Assumptions or the possible combination where all condition indicator are bad or good, can be assumed: When all the condition indicators that monitor a certain failure mode, indicate a bad level, the maximum frequency of the failure mode will occur. The probability of failure occurrence will be 1 in this case. When all the condition indicators that monitor a failure mode, indicate a good level, a minimum frequency of the failure mode will occur. The failure frequency will be 1 % of the total failure frequency. or the other combinations, estimations have to be made based on the theoretical view in order to determine the probability of the failure occurrence. The estimations will be based on the number failure frequency with respect to the total failure frequency which will be expressed in percentages. Due to the fact that each condition indicator is linked to minimal 1 failure mode, all condition indicators are important. The condition indicators are not even important. A distinction will be made between less important and very important condition indicators in this research. The importance can be determined, based on the failure causes of the failure mode. The number of causes of a failure mode related to a condition indicator will determine the importance of the condition indicator. The failure probability for each combination can be categorized based on the importance of the condition indicators. The percentages of the failures with respect to the total number of failures which can occur are given in the interpretation of each rank. Based on mathematics, the number of the percentages does not will influence the results of the detection chance when the difference between the percentages is equal. The percentages 50 %, 60 %, 70 %, 80 %, 90 % and 100 % are selected for determination of the probability of the failure occurrence. 100 % of the failure occurs when all the condition indicators are bad. 128

129 The ranks are: P1, 1 condition indicator is bad and the condition indicator is less important. 50 % of the total failure frequency occurs. P2, 1 condition indicator is bad and the condition indicator is very important. 60 % of the total failure frequency occurs. P3, 2 condition indicators are bad. Both condition indicators are less important. 70 % of the total failure frequency occurs. P4, 2 condition indicators are bad. One condition indicator is less important, the other is very important. 80 % of the total failure frequency occurs. P5, 2 condition indicators are bad. Both condition indicators are very important. 90 % of the total failure frequency occurs. The determination of the probability of the failure occurrence can be illustrated with the help of table.3 for the case that 2 condition indicators are linked to a failure mode and table.4 for the case that 3 condition indicators are linked to a failure mode. Table.3. Indications for the determination of the probability of the failure occurence for each combination when 2 condition indicators are related to a failure mode. CI A CI B Probability combination occurrence ailure probability category Percentage of failures (%) ailure frequency 0 0 E or Total frequency Probability of the failure occurrence 0 1 E or P1 or P2 50 or 60 X X / Total frequency 1 0 E or P1 or P2 50 or 60 Y Y / Total frequency 1 1 E or - 1 Z Z / Total frequency 1 129

130 Table.4. The determination of the probability of the failure occurence for each combination when 3 condition indicators are related to a failure mode. CI A CI B CI C Probability combination occurrence ailure probability category A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P A, B, C or D P1, P2, P3, P4 or P5 Percentage of failures (%) ailure frequency 100 Total frequency 50, 60, 70, T 80 or 90 50, 60, 70, 80 or 90 50, 60, 70, 80 or 90 50, 60, 70, 80 or 90 50, 60, 70, 80 or 90 50, 60, 70, 80 or 90.4 ormula of the detection chance for each possible combination U V W X Y Probability of the failure occurrence 1 T / Total frequency U / Total frequency V / Total frequency W / Total frequency X / Total frequency Y / Total frequency 1 Z Z / Total frequency The parameters included in the formula of the detection chance are shown in figure.3. igure.3. Parameters included in the formula for the determination of the detection chance is shown in the diagram. 130

131 As described before, the failure mode can be linked to 1, 2 or 3 condition indicators. or each case the formulas of the detection chance will be indicated. 1 condition indicator linked to the failure mode. In this case, the detection chance is equal to 1 based on the assumptions made in the previous section. 2 condition indicators linked to the failure mode. Each failure mode will be viewed separately. The detection chance for each link can be determined as followed: Detection chance A CI A = P ( A CI A = 0) = P ( A CI A = 0, CI B = 0). P (CI A = 0, CI B = 0) + P ( A CI A = 0, CI B = 1). P (CI A = 0, CI B = 1). (.4) Detection chance A CI B = P ( A CI B = 0) = P ( A CI A = 0, CI B = 0). P (CI A = 0, CI B = 0) + P ( A CI A = 1, CI B = 0). P (CI A = 1, CI B = 0). (.5) Three condition indicators linked to the failure mode. Each failure mode will be viewed separately. The detection chance for each link can be determined as followed: Detection chance A CI A = P ( A CI A = 0) = P ( A CI A = 0, CI B = 0, CI C = 0). P (CI A = 0, CI B = 0, CI C = 0) + P ( A CI A = 0, CI B = 1, CI C = 0). P (CI A = 0, CI B = 1, CI C = 0) + P ( A CI A = 0, CI B = 1, CI C = 1). P (CI A = 0, CI B = 1, CI C = 1) + P ( A CI A = 0, CI B = 0, CI C = 1). P (CI A = 0, CI B = 0, CI C = 1). (.6) Detection chance A CI B = P ( A CI B = 0) = P ( A CI A = 0, CI B = 0, CI C = 0). P (CI A = 0, CI B = 0, CI C = 0) + P ( A CI A = 1, CI B = 0, CI C = 0). P (CI A = 1, CI B = 0, CI C = 0) + P ( A CI A = 1, CI B = 0, CI C = 1). P (CI A = 1, CI B = 0, CI C = 1) + P ( A CI A = 0, CI B = 0, CI C = 1). P (CI A = 0, CI B = 0, CI C = 1). (.7) Detection chance A CI C = P ( A CI C = 0) = P ( A CI A = 0, CI B = 0, CI C = 0). P (CI A = 0, CI B = 0, CI C = 0) + P ( A CI A = 0, CI B = 1, CI C = 0). P (CI A = 0, CI B = 1, CI C = 0) + P ( A CI A = 1, CI B = 1, CI C = 0). P (CI A = 1, CI B = 1, CI C = 0) + P ( A CI A = 1, CI B = 0, CI C = 0). P (CI A = 1, CI B = 0, CI C = 0). (.8) Based on the formulas in this section, the detection chance can finally be determined for the links of the 3 components, namely: circuit breaker, grounding and control circuit. 131

132 Appendix G Determination of the detection chance of each link for the circuit breaker is described. G.1 Circuit breaker ailure mode 2, 3 and 4 The frequency of failure mode 2 is 0,87 % of the total number of failures. The frequency of failure mode 3 is 3,48 % of the total number of failures. The frequency of failure mode 4 is 0,87 % of the total number of failures. Table G.1. The condition indicator which is related to 2, 3 and 4. Condition indicators CI 2 X X X Table G.2. The detection chance of the links CI 2-2, CI 2-3 and CI 2-4. Link Detection chance CI CI CI ailure mode 8 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.3. The condition indicators which are related to the 8. Condition indicators 8 CI 1 X CI 3 X CI 5 X Table G.4. The determination of the failure probability for each combination based on CI 1, CI 3 and CI 5. CI 1 CI 3 CI 5 Probability combination occurrence 132 ailure probability ailure frequency Probability of the failure occurrence , ,2 P ,2 P ,2 P ,2 P ,2 P ,2 P , Table G.5. The detection chance of the links CI 1 8, CI 3 8 and CI 5 8. Link Detection chance CI 1 8 0,67 CI 3 8 0,69 CI 5 8 0,73

133 ailure mode 10 The frequency of this failure mode is 13,04 % of the total number of failures. Table G.6. The condition indicator which is related to 10. Condition indicators 10 CI 3 X Table H.7. The detection chance of the link CI Link Detection chance CI ailure mode 11 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.8. The condition indicators which are related to 11. Condition indicators 11 CI 3 X CI 4 X CI 5 X Table G.9. The determination of the failure probability for each combination based on CI 3, CI 4 and CI 5. CI 3 CI 4 CI 5 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence , ,1 P ,1 P ,25 P ,1 P ,25 P ,1 P , Table G.10. The detection chance of the links CI 3 11, CI 4 11 and CI Link Detection chance CI ,55 CI ,47 CI ,56 133

134 ailure mode 12 The frequency of this failure mode is 13,04 % of the total number of failures. Table G.11. The condition indicators which are related to 12. Condition indicators 12 CI 3 X CI 4 X CI 5 X Table G.12. The determination of the failure probability for each combination based on CI 3, CI 4 and CI 5. CI 3 CI 4 CI 5 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence , ,1 P ,1 P ,25 P ,1 P ,25 P ,1 P , Table G.13. The detection chance of the links CI 3 12, CI 4 12 and CI Link Detection chance CI ,56 CI ,44 CI ,5 ailure mode 13 The frequency of this failure mode is 3,48 % of the total number of failures. Table G.14. The condition indicators which are related to 13. Condition indicators 13 CI 1 X CI 3 X CI 4 X 134

135 Table G.15. The determination of the failure probability for each combination based on CI 1, CI 3 and CI 4. CI 1 CI 3 CI 4 Probability combination occurrence 135 ailure probability ailure frequency Probability of the failure occurrence , ,25 P ,1 P ,1 P ,1 P ,1 P ,25 P , Table G.16. The detection chance of the links CI 1 13, CI 3 13 and CI Link Detection chance CI ,50 CI ,52 CI ,54 ailure mode 5 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.17. The condition indicators which are related to 5. Condition indicators 5 CI 1 X CI 3 X Table G.18. The determination of the failure probability for each combination based on CI 1 and CI 3. CI 1 CI 3 ailure probability Probability combination occurrence ailure frequency Probability of the failure occurrence 0 0-0, P1 0, P2 0, , Table G.19. The detection chance of the links CI 1 5 and CI 3 5. Link Detection chance CI 1 5 0,63 CI 3 5 0,65 ailure mode 6 The frequency of this failure mode is 10,43 % of the total number of failures. Table G.20. The condition indicators which are related to 6. Condition indicators 6 CI 1 X CI 3 X

136 Table G.21. The determination of the failure probability for each combination based on CI 1 and CI 3. CI 1 CI 3 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0, ,25 P ,25 P , Table G.22. The detection chance of the links CI 1 6 and CI 3 6. Link Detection chance CI 1 6 0,63 CI 3 6 0,68 ailure mode 7 The frequency of this failure mode is 4,35 % of the total number of failures. Table G.23. The condition indicators which are related to 7. Condition indicators 7 CI 1 X CI 3 X CI 5 X Table G.24. The determination of the failure probability for each combination based on CI 3, CI 4 and CI 5. CI 3 CI 4 CI 5 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence ,25-2, ,2 P4 1,67 0, ,2 P5 1,87 0, ,2 P2 1,25 0, ,2 P1 1,04 0, ,2 P4 1,67 0, ,2 P3 1,46 0, ,25-0,02 0,01 Table G.25. The detection chance of the links CI 1 7, CI 3 7 and CI 5 7. Link Detection chance CI 1 7 0,71 CI 3 7 0,67 CI 5 7 0,61 136

137 ailure mode 9 The frequency of this failure mode is 11,30 % of the total number of failures. G.2 GIS-Circuit breaker Table G.26. The condition indicator which is related to 9. Condition indicators 9 CI 3 X Table G.27. The detection chance of the link CI 3 9. Link Detection chance CI ailure mode 1 The frequency of this failure mode is 4,35 % of the total number of failures. Table G.28. The condition indicators which are related to 1. Condition indicators 1 CI 7 X CI 8 X Table G.29. The determination of the failure probability for each combination based on CI 7 and CI 8. CI 7 CI 8 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0,5-2, ,25 P1 1,46 0, ,25 P2 1,67 0, ,5-0,028 0,01 Table G.30. The detection chance of the links CI 7 1 and CI 8 1. Link Detection chance CI 7 1 0,68 CI 8 1 0,70 ailure mode 2, 3 and 4 The frequency of failure mode 2 is 0,87 % of the total number of failures. The frequency of failure mode 3 is 3,48 % of the total number of failures. The frequency of failure mode 4 is 0,87 % of the total number of failures. Table G.31. The condition indicators which are related to the 2, 3 and 4. Condition indicators CI 6 X X X 137

138 Table G.32. The detection chance of the links CI 6 2, CI 6 3 and CI 6 4. Link Detection chance CI CI CI ailure mode 8 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.33. The condition indicators which are related to 8. Condition indicators 8 CI 6 X CI 7 X CI 8 X Table G.34. The determination of the failure probability for each combination based on CI 6, CI 7 and CI 8. CI 6 CI 7 CI 8 Probability combination occurrence Percentage (%) ailure frequency Probability of the failure occurrence ,2-1, ,2 P3 0,87 0, ,1 P4 1,00 0, ,2 P2 0,75 0, ,1 P4 1,00 0, ,1 P4 1,00 0, ,2 P5 1,12 0, ,2-0,01 0,01 Table G.35. The detection chance of the links CI 6 8, CI 7 8 and CI 8 8. Link Detection chance CI 6 8 0,54 CI 7 8 0,50 CI 8 8 0,58 ailure mode 10 The frequency of this failure mode is 13,04 % of the total number of failures. Table G.36. The condition indicators which are related to 10. Condition indicators 10 CI 6 X CI 7 X 138

139 Table G.37. The determination of the failure probability for each combination based on CI 6 and CI 7. CI 6 CI 7 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0,5-6, ,25 P2 5,00 0, ,25 P1 3,75 0, ,5-0,06 0,01 Table G.38. The detection chance of the links CI 6 10 and CI Link Detection chance CI ,65 CI ,65 ailure mode 11 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.39. The condition indicators which is related to 11. Condition indicators 11 CI 7 X Table G.40. The detection chance of the link CI Link Detection chance CI ailure mode 12 The frequency of this failure mode is 13,04 % of the total number of failures. Table G.41. The condition indicators which are related to 12. Condition indicators 12 CI 7 X CI 8 X Table G.42. The determination of the failure probability for each combination based on CI 7 and CI 8. CI 7 CI 8 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0,25-6, ,5 P1 3,75 0, ,5 P2 4,38 0, ,25-0,06 0,01 Table G.43. The detection chance of the links CI 7 12 and CI Link Detection chance CI ,55 CI ,50 139

140 ailure mode 13 The frequency of this failure mode is 3,48 % of the total number of failures. Table G.44. The condition indicator which is related to 13. Condition indicators 13 CI 7 X Table G.45. The detection chance of the link CI Link Detection chance CI ailure mode 5 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.46. The condition indicators which are related to 5. Condition indicators 5 CI 6 X CI 7 X Table G.47. The determination of the failure probability for each combination CI 6 and CI 7. CI 6 CI 7 Percentage (%) Probability combination occurrence ailure frequency Probability of the failure occurrence 0 0 0,5-1, ,25 P2 0,87 0, ,25 P1 0,62 0, ,5-0,01 0,01 Table G.48. The detection chance of the links CI 6 5 and CI 7 5. Link Detection chance CI 6 5 0,68 CI 7 5 0,63 ailure mode 6 The frequency of this failure mode is 10,43 % of the total number of failures. Table G.49. The condition indicators which are related to 6. Condition indicators 6 CI 6 X CI 7 X 140

141 Table G.50. The determination of the failure probability for each combination based on CI 6 and CI 7. CI 6 CI 7 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0,5-5, ,25 P2 3,50 0, ,25 P1 2,50 0, ,5-0,05 0,01 Table G.51. The detection chance of the links CI 6 6 and CI 7 6. Link Detection chance CI 6 6 0,68 CI 7 6 0,63 ailure mode 7 The frequency of this failure mode is 4,35 % of the total number of failures. Table G.52. The condition indicators which are related to 7. Condition indicators 7 CI 6 X CI 7 X Table G.53. The determination of the failure probability for each combination based on CI 1 and CI 3. CI 1 CI 3 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence 0 0 0,5-2, ,25 P2 1,4616 0, ,25 P1 1,2528 0, ,5-0, ,01 Table G.54. The detection chance of the links CI 6 7 and CI 7 7. Link Detection chance CI 6 7 0,55 CI 7 7 0,50 ailure mode 9 The frequency of this failure mode is 11,30 % of the total number of failures. Table G.55. The condition indicators which are related to 9. Condition indicators 9 CI 6 X CI 7 X CI 8 X 141

142 Table G.56. The determination of the failure probability for each combination based on CI 6, CI 7 and CI 8. CI 6 CI 7 CI 8 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence ,2-5, ,2 P3 3,79 0, ,1 P2 3,25 0, ,2 P5 4,88 0, ,1 P4 4,33 0, ,1 P1 2,71 0, ,2 P3 3,79 0, ,2-0,05 0,01 G.3 Grounding Table G.57. The detection chance of the links CI 6 9, CI 7 9 and CI 8 9. Link Detection chance CI 6 9 0,58 CI 7 9 0,47 CI 8 9 0,60 ailure mode 14 The frequency of this failure mode is 0,87 % of the total number of failures. Table G.58. The condition indicators which are related to 14. Condition indicators 14 CI 10 X CI 11 X CI 12 X Table G.59. The determination of the failure probability for each combination based on CI 10, CI 11 and CI 12. CI 10 CI 11 CI 12 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P P P P P

143 Table G.60. The detection chance of the links CI 10 14, CI and CI Link Detection chance CI CI CI ailure mode 15 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.61. The condition indicators which are related to 15. Condition indicators 15 CI 9 X CI 10 X Table H.62. The determination of the failure probability for each combination based on CI 9 and CI 10. CI 9 CI 10 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.63. The detection chance of the links CI 9 15 and CI Link Detection chance CI CI ailure mode 16 The frequency of this failure mode is 1,74 % of the total number of failures. Table G.64. The condition indicators which are related to 16. Condition indicators 16 CI 10 X CI 11 X Table G.65. The determination of the failure probability for each combination based on CI 10 and CI 11. CI 10 CI 11 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P

144 G.4 GIS-Grounding Table G.66. The detection chance of the links CI and CI Link Detection chance CI CI ailure mode 14 The frequency of this failure mode is 0,87 % of the total number of failures. Table G.67. The condition indicators which are related to 14. Condition indicators 14 CI 13 X CI 14 X CI 15 X Table G.68. The determination of the failure probability for each combination based on CI 13, CI 14 and CI 15. CI 13 CI 14 CI 15 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P P P P P Table G.69. The detection chance of the links CI 13 14, CI and CI Link Detection chance CI CI CI ailure mode 15 The frequency of this failure mode is 2,61 % of the total number of failures. Table G.70. The condition indicators which are related to 15. Condition indicators 15 CI 13 X CI 14 X 144

145 Table G.71. The determination of the failure probability for each combination based on CI 13 and CI 14. CI 13 CI 14 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.72. The detection chance of the links CI and CI Link Detection chance CI CI ailure mode 16 The frequency of this failure mode is 1,74 % of the total number of failures. Table G.73. The condition indicators which are related to 16. Condition indicators 16 CI 13 X CI 14 X Table G.74. The determination of the failure probability for each combination based on CI 13 and CI 14. CI 13 CI 14 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P G.5 Control circuit Table G.75. The detection chance of the links CI and CI Link Detection chance CI CI ailure mode 17 The frequency of this failure mode is 3,48 % of the total number of failures. Table G.76. The condition indicators which are related to 17. Condition indicators 17 CI 16 X CI 19 X 145

146 Table G.77. The determination of the failure probability for each combination based on CI 16 and CI 19. CI 16 CI 19 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.78. The detection chance of the links CI and CI Link Detection chance CI CI ailure mode 18 The frequency of this failure mode is 5.22 % of the total number of failures. Table G.79. The condition indicators which are related to 18. Condition indicators 18 CI 17 X CI 19 X Table G.80. The determination of the failure probability for each combination based on CI 17 and CI 19. CI 17 CI 19 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.81. The detection chance of the links and Link Detection chance CI CI ailure mode 19 The frequency of this failure mode is 6,96 % of the total number of failures. Table G.82. The condition indicators which are related to 19. Condition indicators 19 CI 17 X CI 19 X 146

147 Table G.83. The determination of the failure probability for each combination based on CI 17 and CI 19. CI 17 CI 19 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.84. The detection chance of the links CI and CI Link Detection chance CI CI ailure mode 20 The frequency of this failure mode is 2.61 % of the total number of failures. Table G.85. The condition indicators which are related to 20. Condition indicators 20 CI 18 X CI 19 X Table G.86. The determination of the failure probability for each combination based on CI 18 and CI 19. CI 18 CI 19 Probability combination occurrence ailure probability ailure frequency Probability of the failure occurrence P P Table G.87. The detection chance of the links CI and CI Link Detection chance CI CI

148 Appendix H The criticality of each link, based on the 3 parameters failure frequency, failure impact and detection chance is shown in table H.1. Link Detection chance CI 1 1 0,73 CI 3 1 0,71 CI 5 1 0,69 CI 2 2 1,00 CI 2 3 1,00 CI 2 4 1,00 CI 1 8 0,67 CI 3 8 0,69 CI 5 8 0,73 CI ,00 CI ,55 CI ,47 CI ,56 CI ,56 CI ,44 CI ,50 CI ,51 CI ,52 CI ,54 Table H.1. The 3 parameters and criticality of each link. Rank detection chance Rank failure frequency Rank failure impact Category failure impact Category failure risk C Undesirable C Undesirable C Undesirable D Undesirable D Intolerable D Undesirable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable B Undesirable B Undesirable B Undesirable A Tolerable A Tolerable A Tolerable Criticality link High High edium High High High Remote Remote Low Low Remote Remote Remote edium edium edium Remote Remote Remote 148

149 CI 1 5 0,63 CI 3 5 0,65 CI 1 6 0,63 CI 3 6 0,68 CI 1 7 0,71 CI 3 7 0,67 CI 5 7 0,61 CI 3 9 1,00 CI 7 1 0,68 CI 8 1 0,70 CI 6 2 1,00 CI 6 3 1,00 CI 6 4 1,00 CI 6 8 0,54 CI 7 8 0,50 CI 8 8 0,58 CI ,65 CI ,65 CI ,00 CI ,55 CI ,50 CI ,00 CI 6 5 0, A Tolerable A Tolerable C Intolerable C Intolerable D Intolerable D Intolerable D Intolerable B Undesirable C Undesirable C Undesirable D Undesirable D Intolerable D Undesirable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable B Undesirable B Undesirable A Tolerable A Tolerable Remote Remote edium edium edium edium edium High edium High High High High Remote Remote Remote Remote Remote Low edium edium Low Remote 149

150 CI 7 5 0,63 CI 6 6 0,68 CI 7 6 0,63 CI 6 7 0,55 CI 7 7 0,50 CI 6 9 0,58 CI 7 9 0,47 CI 8 9 0,60 CI ,42 CI ,40 CI ,49 CI ,60 CI ,50 CI ,55 CI ,50 CI ,73 CI ,67 CI ,75 CI 13 0, A Tolerable C Intolerable C Intolerable D Intolerable D Intolerable B Undesirable B Undesirable B Undesirable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable A Tolerable 150 Remote edium edium edium edium edium edium edium Remote Remote Remote Remote Remote Remote Remote Low Remote Low Remote

151 15 CI ,63 CI ,65 CI ,65 CI ,73 CI ,65 CI ,55 CI ,55 CI ,55 CI ,55 CI ,55 CI , A Tolerable A Tolerable A Tolerable A Neglectable A Neglectable A Neglectable A Neglectable A Tolerable A Tolerable A Tolerable A Tolerable Remote Remote Remote Low Remote Remote Remote Remote Remote Remote Remote 151

152 Appendix I The criticality of each link and condition indicator is shown in table I.1. Table I.1. The weghting of each condition indicator for the circuit breaker, grounding and control circuit. Circuit breaker (Air Blast, Pneumatic) 152 Weighting of CI CI 1 High Remote Remote High CI 2 High High High High CI 3 High Remote Low Remote edium High CI 4 Remote edium Remote edium CI 5 edium Low Remote edium edium Circuit breaker (Gas, Pneumatic) CI 1 High Remote edium Remote High CI 2 High High High High CI 3 High Remote edium Low Remote edium Remote High CI 4 Remote edium Remote edium CI 5 edium Remote edium edium Circuit breaker (Gas, Hydraulic and Spring)

153 CI 1 High Remote edium Remote High CI 2 High High High High CI 3 High Remote edium Remote edium Remote High CI 4 Remote edium Remote edium CI 5 edium Remote edium edium Circuit breaker (Oil, Hydraulic) CI 1 High edium Remote High CI 2 High High High High CI 3 High edium Remote High Remote edium Remote High CI 4 Remote edium Remote edium CI 5 edium edium Remote edium edium Circuit breaker (Oil, Spring) CI 1 High edium Remote High CI 2 High High High High CI 3 High edium Remote Remote edium Remote High CI 4 Remote edium Remote edium 153

154 CI 5 edium edium Remote edium edium GIS-Circuit breaker (Air Blast, Pneumatic) CI 6 High High High Remote Low High CI 7 edium Remote Remote Low edium Low edium CI 8 High Remote edium High GIS-Circuit breaker (Gas, Pneumatic) CI 6 High High High Remote edium Remote High CI 7 edium Remote edium Remote Low edium Low edium 1 12 CI 8 High edium High GIS-Circuit breaker (Gas, Hydraulic) CI 6 High High High Remote edium High CI 7 Remote edium Low edium Low edium 1 12 CI 8 High edium High GIS-Circuit breaker (Oil, Hydraulic) CI 6 High High High edium edium High CI 7 edium edium edium Low edium Low edium

155 CI 8 High edium edium High Grounding 15 CI 9 Remote Remote CI 10 Remote Remote Remote Remote CI 11 Remote Remote Remote 14 CI 12 Low Low GIS-Grounding CI 13 Low Remote Remote Low CI 14 Remote Remote Remote Remote 14 CI 15 Low Low Control circuit 17 CI 16 Remote Remote CI 17 Remote Remote Remote 20 CI 18 Remote Remote CI 19 Remote Remote Remote Remote Remote 155

156 Appendix J J.1 The answers and questions The questions and anwers are: 1. What is the size of the defect? Size indicates an amount or volume of which the corresponding defect manifests in relation to the total amount of consideration. The parameter size can be expressed in 3 categories. Categorization of the size: Nothing The defect does not occur. Small The defect occurs regularly. edium The defect is significant. Large The defect predominates. Several examples of defects for which this question have to be answered are: Rust Spelling Penetration Damage Leakage Cracking Corrosion Pollution Dehydration Wear Distortion Discoloration Coating Aeration The pollution on the insects grille and oil will be checked. In order to keep the reporting of the defect user friendly, the parameters will be expressed in a score. Score for the categories of the size: Nothing 0 Small 1 edium 2 Large 3. In practice the answer can be given on this way: Table J.1. Reporting of the size. Size deviation

157 2. What is the intensity of the defect? Intensity indicates at what stage of degradation the defect is located. The parameter intensity can be expressed in 3 categories. Categorization of the intensity [25]: Nothing The defect does not occur. Beginning - The defect is barely perceptible. Advanced - The defect is clearly visible. End - The defect is very clearly visible, the defect can hardly increase. Several examples of defects for which this question have to be answered are: Penetration Caused by flashover. Damage of the silver coat. Rust Cracking Corrosion Wear Dehydration Distortion Discoloration Coating. In order to keep the reporting of the defects user friendly, the parameters will be expressed in a score. Score for the categories of the intensity: Nothing 0 Beginning 1 Advanced 2 End 3. In practice the answer can be given on this way: Table J.2. Reporting of the intensity. Intensity deviation How smooth is the rotation or motion of the structures? Categorization of the smootness can be: Very rough - The rotation is very rough. Rough - The rotation can be rough. Smooth - The rotation is very smooth. Several examples of structures for which this question have to be answered are: Rotating parts There must be enough grease for the structures. Transmission Structures such as gear must rotate smoothly. 157

158 In order to keep the reporting of the defects user friendly, the parameters will be expressed in a score. Score for the categories of the size: Smooth 1 Rough 2 Very rough 3. In practice the answer can be given on this way: Table J.3. Reporting of the smoothness. Smoothness sub-component What is the severity of the defect? Categorization of the severity can be: Nothing The defect does not occur. Less The severity of the defect is not serious. oderate The severity of the defect is moderate. any The severity of the defect is very serious. Several examples of defects for which this question have to be answered are: Pearls These pearls are released as result of the flashovers. Damage Leakage Pollution. In order to keep the reporting of the defects user friendly, the parameters will be expressed in a score. Score for the categories of the size: Nothing 0 Less 1 oderate 2 any 3. In practice the answer can be given on this way: 5. Is the sub-component in position? Categorization of the position can be: Table J.4. Reporting of the severity. Severity deviation Yes The sub-component is the correct position. No The sub-component is not in the correct position. 158

159 Several examples of sub-components or activities for which this question have to be answered are: Dead points This is available when the contacts are closed. The contacts bend inwards. Status indication or four positions (in, out, fault and separation) the position will be checked. Rotating parts Transmission In order to rotate the structures smoothly, the structures have to be positioned correctly. In and out position auxiliary contacts. Needle on the waltz When the needle is correctly in position, partly is will fit tight on the waltz en partly it will have no contact with the waltz. The needle can be checked manual to feel the pressure, in order to check if the needle is fit tight on the waltz. Walk contacts. In practice the answer can be given on this way: 6. How is the sub-component firmed? Categorization of the firmness can be: Table J.5. Reporting the position. Sub-component in position Yes No Yes The sub-component is firmed correctly No The sub-component is not firmed correctly. Some examples of sub-components or activities for which this question have to be answered are: langes and bolts ootplates Kit edges This gives an indication if the sub-component such as bolts and plates are firmed correctly. Earthing Wiring. In practice the answer can be given on this way: Table J.6. Reporting of the firmness. Sub-component firmed Yes No 159

160 7. How does the heat feels? Categorization of the heat can be: Yes The heater works correctly. No The heater does not work correctly. Some examples of activities for which this question have to be answered are: Cabinet heating In practice the answer can be given on this way: Table J.7. Reporting of the heating. Heat component felt Yes No 8. Is the sub-component available? Categorization of the availability can be: Yes The sub-component is available. No The sub-component is not available. Several examples of activities for which this question have to be answered are: Earthing Aeration The availability of grains will be checked. The grains attract moisture. In practice the answer can be given on this way: Table J.8. Reporting of the availability. Sub-component available Yes No 9. Is the checklist satisfied? Categorization of the satisfaction can be: Yes The checklist is satisfied. No The checklist is not satisfied or not completely satisfied. Several examples of activities for which this question have to be answered are: Locks. 160

161 In practice the answer can be given on this way: Table J.9. Reporting of the satisfaction of the checklist. Checklist subcomponent Satisfied Yes No or some preventive activities 2 questions have to be answered. Based on the combination of questions, the preventive activities can be categorized: 1) Size and intensity: Penetration Rust Cracking Corrosion Wear Dehydration Distortion Discoloration Coating. In practice the answer can be given on this way: 2) Size and severity: Table J.10. Reporting of the combination of the size and intensity. Size deviation Intensity deviation Damage Leakage Pollution. In practice the answer can be given on this way: 3) Smoothness and position: Table J.11. Reporting of the combination of the size and severity. Size deviation Severity deviation Rotating parts Transmission. In practice the answer can be given on this way: 161

162 Table J.12. Reporting of the combination of the smoothness and postion. 4) irmness and availibility: Smoothness subcomponent Sub-component in position Yes No Earthing. In practice the answer can be given on this way: 5) Size and availibility: Table J.13. Reporting of the combination of the firmness and availibility. Sub-component firmed Yes No Sub-component available Yes No Aeration. In practice the answer can be given on this way: J.2 Transformation matrices Table J.14. Reporting of the combination of the firmness and availibility. Size deviation Sub-component available Yes No Based on the possible combinations of questions, the matrices are constructed. Table J.15. atrix for the transformation of the parameters size and intensity. Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Table J.16. atrix for the transformation of the parameters size and severity. Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad 162

163 Table J.17. atrix for the transformation of the parameters smooth and position. Smooth Position Yes Good air Poor No Poor Bad Bad Table J.18. atrix for the transformation of the parameters firm and available. Available Yes No irm Yes Good Bad No Bad Bad Table J.19. atrix for the transformation of the parameters size and available. Size Available Yes Good Good air Poor No air air Bad Bad 163

164 Appendix K Inspection sheet for the circuit breaker, grounding and control circuit which illustrates the current model and the new model. K.1 Circuit breaker Old Table K.1. The method of reporting visual inspection for the circuit breakers. Corona caps: Corona caps: Size damage - damage New Good air Poor Bad Report Transformation Severity damage Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Insulators: Insulators: - condition insulator Good air Poor Bad - condition insulator - damage - flanges and bolts Size damage Severity damage langes and bolts firmed Yes No Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad 164

165 - feetplates eet plates firmed Yes Good No Bad Yes No - kit edges / cementing Kit edges/ cementing firmed Yes Good No Bad Yes No echanical drives: echanical drives: - condition drives - condition drives - General impression of condition shifting - rotating parts - transmission - General impression of condition shifting Smoothness Rotating parts in rotating parts position Yes No Smoothness Transmission in transmission position Yes No Good air Poor Bad Smooth Position Yes Good air Poor No Poor Bad Bad Smooth Position Yes Good air Poor No Poor Bad Bad Yes Good No Bad - lock Checklist lock satisfied Yes No Secundair circuits: - earthing - secondary wiring - heating Earthing firmed Yes No Earthing available Yes No Secondary wiring firmed Yes No Heat heating felt Yes No Available Yes No irm Yes Good Bad Yes Bad Bad Yes Good No Bad Yes Good No Bad 165

166 - earation Dirt size Silicagel available Yes No 166 Size Available Yes Good Good air Poor No air air Bad Bad Auxiliary contacts: Auxiliary contacts: - in- en out position In position Yes Good No Bad Yes No - contact needle op waltz Contact needle in position Yes Good No Bad Yes No ain contacts: ain contacts: - conditie contacten - conditie contacten Good air Poor Bad - penetration - pearls - discoloration - dead points in / out Size Intensity penetration penetration Severity pearls Size Intensity discoloration discoloration Dead points in position (in/out) Yes No Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad 0 Good 1 air 2 Poor 3 Bad Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad

167 - spelling contacts Size spelling contacts 0 Good 1 air 2 Poor 3 Bad Status indication: Status indication: - Position compared to real position Position Yes No Yes Good No Bad 167

168 K.2 Grounding Old Table K.2. The method of reporting visual inspection for the grounding. Good air Poor Bad 168 Report New Transformation Insulators: Insulators: - condition insulator Good air Poor Bad - condition insulator - Damage Size damage Severity damage Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad - flanges and bolts langes and bolts firmed Yes No - feetplates eet plates firmed Yes Good No Bad Yes No - kit edges / cementing Kit edges / cementing firmed Yes Good No Bad Yes No echanische aandrijving: echanische aandrijving: - condition drives - condition drives - rotating parts Smoothness Rotating parts in rotating parts position Yes No Good air Poor Bad Smooth Position Yes Good air Poor No Poor Bad Bad

169 - transmission Smoothness Transmission in transmission position Yes No 169 Smooth Position Yes Good air Poor No Poor Bad Bad Yes Good No Bad - lock Checklist lock satisfied Yes No Secundair circuits: - leakage / brush seals - earthing - secondary wiring - heating - earation Size leakage Severity leakage Earthing firmed Yes No Earthing available Yes No Secondary wiring firmed Yes No Heat heating felt Yes No Size dirt Silicagel available Yes No Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Available Yes No irm Yes Good Bad No Bad Bad Yes Good No Bad Yes Good No Bad Size Available Yes Good Good air Poor No air air Bad Bad Auxiliary contacts: Auxiliary contacts: - in- en out position In position (in/out) Yes No Yes Good No Bad

170 Contact needle in position Yes Good No Bad - contact needle op waltz Yes No ain contacts: ain contacts: - condition contacts - condition contacts - penetration - pearls - discoloration - dead points in / out - walk contacts Size Intensity penetration penetration Severity pearls Size Intensity discoloration discolaration Dead points in position (in/out) Yes No Walk contacts in position Yes No Good air Poor Bad Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad 0 Good 1 air 2 Poor 3 Bad Size Intensity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad Yes Good No Bad K.3 Control circuit The new method of reporting the results of the visual inspections for the control circuit is shown in table K

171 Table K.3. The method of reporting visual inspection for the control circuit. Old New Good air Poor Bad Report Transformation "Live" test damage protection relay wiring earth joints pollution heating cabinet "Live" test Size damage Severity damage Wiring firmed Yes No Earth joints firmed Yes No Earth joints available Yes No Size pollution Severity pollution Heat heating felt Yes No Good air Poor Bad Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad Available Yes No irm Yes Good Bad No Bad Bad Size Severity 0 Good Good air Poor 2 - air Poor Bad 3 - Poor Bad Bad Yes Good No Bad 171

172 Appendix L The determination of the time intervals can be illustrated for the basic time intervals: 3 years and 6 years. Three years or condition indicators with a time interval of 3 years, the time intervals could be vary between 1 year and 5,1 years. Table L.1. Time intervals for condition indicators with a basic time interval of 3 years. These time intervals are based on the result and weighting of the condition indicator. Condition indicator Weighting Result High edium Low Remote Good 4,2 years 4,5 years 4,8 years 5,1 years air 3 years 3,3 years 3,6 years 3,9 years Poor 2,1 years 2,4 years 2,7 years 3 years Bad 1 year 1,2 years 1,5 years 1,8 years Six years or condition indicators with a time interval of 6 years, the time intervals could be vary between 1 years and 10,9 years. Table L.2. Time intervals for condition indicators with a basic time interval of 6 years. These time intervals are based on the result and weighting of the condition indicator. Condition indicator Weighting Result High edium Low Remote Good 8,8 years 9,5 years 10,2 years 10,9 years air 6 years 6,7 years 7,4 years 8,1 years Poor 3,9 years 4,6 years 5,3 years 6 years Bad 1 years 1,8 years 2,5 years 3,2 years 172

173 Appendix Price tag Template v1.0 Table.1. Breakdown of the expenditures of maintenance. PO INS.28 Type activity Standard maintenance activities Activity TB-AA- 01 Regular maintenance grounding (rail and field) Number of components 1 (3-poles) Year 2011 Version 1.0 Omschrijving Number Amount Intern personnel Preparation 4.50 hours Surcharges % Execution 5.00 hours Surcharges % Subtotal intern personnel 10 hours aterial aterial net Surcharges % 2.33 Subtotal aterial Tools Tools Surcharges % Subtotal tools Total included surcharges

174 Appendix N N.1 Scenario 1 and 2 Scenario 1 Table N.1. The results of the condition indicators after the regular maintenance for scenario 1. Regular maintenance Condition levels each condition indicators 1 CI 9 = Good CI 10 = air CI 11 = air CI 12 = Poor 2 CI 9 = Good CI 10 = air CI 11 = air CI 12 = air 3 CI 9 = air CI 10 = fair CI 11 = Poor CI 12 = air 4 CI 9 = air CI 10 = air CI 11 = air CI 12 = air 5 CI 9 = Poor CI 10 = Poor CI 11 = air CI 12 = air 6 CI 9 = air CI 10 = fair CI 11 =Poor CI 12 = Poor Condition of asset oderate air oderate air oderate oderate In figures N.1, all the condition indicators, related time intervals and related results is shown on 1 timeline for respectively scenario 1 and scenario

175 igure N.1. The 2 scenarios illustrating the regular maintenance and extra maintenance including the time intervals. Regular indicates the number of the regular maintenance. Extra indicates the number of the extra maintenance. Regular 1 is given as the current case (0 years). The beginning of each arrow in figure N.1 shows when the condition indicator is assessed as last and the end of the arrow shows when the next maintenance of that condition indicator is performed. Extra means extra maintenance. Regular means the number of the regular maintenance. or example, regular 2a means the first part of the second regular maintenance. N.2 Scenario 3 and 4 Scenario 3 A grounding component (Grounding B) is included in this scenario. The result of the condition indicators as assumed after the regular maintenance 1 are shown in table N.2. The results of the after the other regular maintenance are shown in table N.4. Table N.2. The results of the condition indicators and condition of the asset of a grouniding component. Condition indicator Result Time intervals (years) CI 9 Good 6 CI 10 Good 6 CI 11 Good 6 CI 12 Good Condition of asset Good Scenario 4 The new strategy will be applied to the results of the grounding B included in scenario 3. The result of the condition indicators after the regular maintenance 1 are equal to the results assumed in scenario 1 (table N.2). The time intervals are determined in table N.3.

176 Table N.3. The results of the condition indicators and condition of the asset of a grouniding component. The time interval for the condition indicators are determined, based on the new model. Condition indicator Weigthing Result Time intervals (years) CI 9 Remote Good 10,9 CI 10 Remote Good 10,9 CI 11 Remote Good 10,9 CI 12 Low Good 10,2 Time interval in practice (years) Condition of asset 10,2 Good Table N.4. The results of the condition indicators after the regular maintenance for scenario 3. Regular maintenance Condition levels each condition indicators 1 CI 9 = Good CI 10 = Good CI 11 = Good CI 12 = Good 2 CI 9 = Good CI 10 = Good CI 11 = Good CI 12 = Good 3 CI 9 = Good CI 10 = Good CI 11 = air CI 12 = air 4 CI 9 = Good CI 10 = air CI 11 = air CI 12 = Poor 5 CI 9 = air CI 10 = air CI 11 = air CI 12 = air 6 CI 9 = air CI 10 = Poor CI 11 =Poor CI 12 = air As descibed at scenario 2, the results of the condition indicators are determined for scenario 4 based on scenario 3 (see figure N.2). 176

177 igure N.2. The results of the condition indicators of scenario 3 are plotted on a time line. The time intervals for each condition indicator are determined for the scenario 4 for regular maintenance 4. The results of the condition indicators are matched with the results in scenario 3. This process is repeated for each regular maintenance activity. The number indicates the number of the regular maintenance. indicates the extra maintenance. In scenario 3, each condition indicator is assessed 6 times, while each condition indicator is assessed 4 times in scenario 4. The expenditures of scenario 3 and scenario 4 are shown in table N.5. Based on scenario 3, there are 15 % savings of the expenditures in scenario