Methods for Using Operational Experience for Optimizing O&M of Offshore Wind Farms MSc thesis

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1 Methods for Using Operational Experience for Optimizing O&M of Offshore Wind Farms MSc thesis T.S. Obdam MAY 2007

2 Acknowledgement/Preface This report is written in the context of my Master thesis for the Master program Sustainable Energy Technology at the Technical University of Eindhoven. The thesis investigates the possibility of using the already collected and stored operational data from (offshore) wind farms in order to make accurate estimates of the failure rates of the different wind turbine components. I would like to thank my supervisor dr. Peter Eecen (ECN) for his valuable advice and guiding during the course of my project, ir. Edwin Bot (ECN) for performing the FluxFarm simulations, Arno van der Werff (ECN) for implementing the drive train loads in the database, ir. Luc Rademakers and dr. Gerard van Bussel (TU Delft) for their valuable feedback and all other people who have contributed to this study. Abstract The accurate determination of the probability of a wind turbine failure is essential for estimating the cost of operation and maintenance (O&M) of offshore wind farms in an accurate manner. Unlike in other branches of industry, operational data of wind farms is seldom used for optimizing O&M. In this thesis it is investigated whether the operational data from mechanical load measurements, maintenance reports and SCADA systems can be used for optimizing O&M of offshore wind farms. In the first part of this thesis mechanical load measurements are used to investigate whether fatigue damage (and thus possibly life time and failure rate) differs significantly between the turbines in an offshore wind farm. The calculation has been performed for blades, tower and three major drive train components. In the second part of this thesis it is investigated to what extent the information on the (paper and often hand-written) maintenance reports can be used in order to make accurate predictions of the failure rates of the different wind turbine components. In the third and final part it is investigated whether the information from the turbine s SCADA system can be used in order to get information about the health of the turbine in an offshore wind farm and whether these data can be used for early fault detection. 2 MSc thesis

3 Contents List of symbols 6 1. Introduction Introduction Novelty of the work Structure of report EWTW site description General Layout Measurements Meteorological Turbine data Mechanical loads Loading of components Theory on fatigue S-N curve Miner s rule Complex loading Blades Tower Drive train Drive train model Moments and forces Main bearing Gearbox bearing Gearbox Conclusions Relation between fatigue loading and wind conditions Blades Blade root flapwise bending Blade root edgewise bending Tower Tower bottom for-aft bending Tower bottom side bending Main bearing Gearbox bearing Gearbox Conclusions Fatigue damage in an offshore wind farm Wind farm description Wind climate Calculation of wake effects by FluxFarm Calculation of fatigue damage Blades Tower Main bearing Gearbox bearing Gearbox Results of the calculation Blades Tower 59 MSc thesis 3

4 5.5.3 Main bearing Gearbox bearing Gearbox Implementation in O&M cost estimator Conclusions Analysis maintenance reports Introduction Theory on reliability engineering Reliability analysis under constant failure rate The assumption of a constant failure rate Conclusions Generic reliability figures Database for failure collection Definition part Registration part Overview available data Results analysis Distribution failures Failure rates Implementation in the O&M cost estimator Conclusions Analysis SCADA data Introduction Overview available data Signal selection Normalization Failure detection Implementation in the O&M cost estimator Conclusions Discussion and recommendations 89 References 91 Appendix A Location of objects at EWTW 93 Appendix B Turbulence using nacelle anemometry 95 B.1 Fourier analysis nacelle anemometry 95 B.2 Correction nacelle anemometry 96 B.2.1 Wind speed 96 B.2.2 Turbulence 96 Appendix C Adaptation for the calculation of tilt and yaw moments 98 C.1 Normalization to eliminate drift effects 98 C.2 Translation blade root moments to rotor centre 101 C.3 Summary 103 Appendix D Tilt moment in wake conditions 104 Appendix E Differences between single and triple wake 106 E.1 Wind speed deficit 106 E.2 Power deficit 107 E.3 Horizontal wind shear 108 E.4 Vertical turbulence 108 E.5 Conclusions 110 Appendix F Comparison FluxFarm simulations with measurements 111 F.1 Wind speed deficit MSc thesis

5 F.2 Turbulence intensity 112 Appendix G Breakdown turbine 114 Appendix H List of SCADA signals 117 Appendix I Normalization SCADA data 119 I.1 Temperature generator I.2 Temperature generator I.3 Temperature generator cooling air 121 I.4 Temperature generator bearing A 122 I.5 Temperature generator bearing B 123 I.6 Temperature gearbox 124 I.7 Temperature gearbox bearing 125 I.8 Temperature main bearing 126 Appendix J SCADA data gearbox temperature 127 MSc thesis 5

6 List of symbols Symbol Description Unit U free-stream Free-stream wind speed m/s U local Local wind speed m/s U nacelle Nacelle wind speed m/s I free-stream Free-stream turbulence intensity - I local Local turbulence intensity - I nacelle Nacelle turbulence intensity - σ free-stream Free-stream turbulence m/s σ local Local turbulence m/s σ nacelle Nacelle turbulence m/s τ w Wind speed deficit - M in In-plane blade root bending moment knm M out Out-of-plane blade root bending moment knm M flap Flapwise blade root bending moment knm M edge Edgewise blade root bending moment knm M for-aft For-aft tower bottom bending moment knm M side Side tower bottom bending moment knm M NS North-South tower bottom bending moment knm M EW East-West tower bottom bending moment knm l T Length tower m θ Pitch angle α t Tilt angle α c Cone angle ψ m Azimuth angle ψ 0,b Offset azimuth angle blade b φ Yaw angle with respect to North M tilt Rotor tilt moment knm M yaw Rotor yaw moment knm M torque Rotor torque knm F thrust Rotor thrust force kn y 1 Distance main bearing - rotor c.o.g. m y 2 Distance main bearing - gearbox bearing m y 3 Distance gearbox bearing - gearbox c.o.g. m y 4 Distance main bearing - main shaft c.o.g. m F z,rotor Mass rotor kn F z,shaft Mass main shaft kn F z,gearbox Mass gearbox kn N Number of stress cycles - F EQ Damage equivalent load range knm m Slope of S-N curve - M 1 Moments in the vertical plane knm M 2 Moments in the horizontal plane knm F r Radial bearing force kn F a Axial bearing force kn p Material property - P Combined bearing load kn P EQ Dynamic equivalent bearing load kn K a Application factor - T N Nominal torque knm 6 MSc thesis

7 T EQ Equivalent torque knm P e Electrical power kw n gen Rotational speed generator rad s -1 D Damage - d Partial damage - λ Failure rate year -1 θ Mean time between failure year λ U Upper confidence limit failure rate year -1 λ L Lower confidence limit failure rate year -1 x Cumulative number of failures - T Cumulative number of operation time days α Confidence interval - χ 2 Chi-square distribution - MSc thesis 7

8 1. Introduction 1.1 Introduction At the moment of writing this thesis, the first Dutch offshore wind farm already produces energy [1] and the second farm is currently being installed [2]. These two farms are the first steps in realizing 6000 MW of offshore wind energy in the Dutch part of the North Sea by the year 2020 [3]. When assuming an average rated capacity of 5 MW it is obvious that transporting, installing and operating and maintaining 1200 turbines will require massive effort. This implicates that optimizing the operation and maintenance (O&M) of the future offshore wind farms is essential for an economical exploitation. Where for onshore wind energy the contribution of operation and maintenance to the energy cost is a mere 5-10%, for offshore wind energy the contribution is in the order of 25-30% [4]. Not only is the contribution of O&M to the energy cost of offshore wind energy considerably larger compared to onshore, also the costs of offshore O&M show a large amount of uncertainty and spread in the long-term period. At certain points during the operation of an offshore wind farm (e.g. at the end of the warranty period of the turbines, change in O&M strategies or when the farm is sold to another investor) it is required to make accurate estimates of future O&M costs. ECN is currently developing the Operations and Maintenance Cost Estimator (OMCE) [5]. The purpose of this program is to be able to accurately estimate the future long-term O&M costs. Many costs (e.g. insurance, preventive maintenance) show little uncertainty and can easily be estimated by the wind farm operator. However, costs related to corrective maintenance are more difficult to estimate. A major factor is the reliable determination of the probability of a wind turbine failure. When an offshore wind farm has been in operation for a certain amount of time, the wind farm operator has the possibility of collecting a huge amount of data. One can think of information from the turbine s SCADA system, wind farm control information, meteorological data, maintenance reports and possibly also mechanical load measurements on one or more turbines in the farm. It is essential to reduce this huge amount of data to dedicated information that can be used to accurately estimate the failure probability of the several components of the wind turbines in the offshore farm. In this thesis it is investigated to what extent the available data can be used to make accurate predictions of the probabilities of wind turbine failures in an offshore wind farm. Three major aspects are investigated: 1) Mechanical loading: In a large offshore wind farm the turbines in the middle of the farm face considerable higher turbulence levels and operate more often in partial wake compared to the turbines on the edge of the farm. This raises the suspicion that also large differences in mechanical loading exist between the wind turbines in the farm. The differences in mechanical loading are calculated for the blades, tower and three major drive train components. If the calculated differences in mechanical loading are significant, different estimates for the failure rates of these components should be considered. Of course this should be in accordance with the experienced maintenance need. 2) Maintenance reports: At present maintenance reports (paper and often handwritten) are usually collected and stored by the wind farm operator. In order to extract useful information the reports have to be digitalized and stored in a database. This makes it possible to study the occurrence of failures as function of time for the various wind turbine com- 8 MSc thesis

9 ponents. These failure rates can then be interpreted and used to estimate the future maintenance need of the different wind turbine components. 3) SCADA data: The SCADA system installed in each wind turbine generally also contains information of the measured temperature at several components (e.g. gearbox, main bearing and generator). It is investigated whether this information can be used to detect upcoming failures by irregular trends in the measured temperatures. In contradiction to the previous two points, which intend to estimate long-term failure behaviour, the information from the SCADA data is more suited for the prediction of failures on the short-term period. 1.2 Novelty of the work Using operational experience for optimizing operation and maintenance is, at present, hardly used in the wind industry. This is in sharp contrast to other branches of industry, where it is more or less common practice to feedback operational experience in order to adjust and optimize maintenance strategies. At present, little insight is available in the differences in mechanical loading between the heavier and lighter loaded turbines in an offshore wind farm. Some published research [6],[7],[8] is performed in the field of relating fatigue loading of wind turbine blades with wind conditions. However, these relations are derived from measurements performed on small and old-fashioned turbines. In this thesis relationships between fatigue loading and wind conditions are established, using measurements on a modern multi-megawatt-class wind turbine, not only for the wind turbine blades, but also for the wind turbine tower and three major drive train components. In the course of the research also new analyses techniques on the measured data of wind turbines have been investigated. Instead of using a meteorological mast for characterizing wind conditions, in this thesis the nacelle anemometer has been used to characterize wind conditions. This has some important advantages, which will be described in this report. Generic estimates of reliability numbers are generally used by wind farm operators in order to estimate the maintenance needs and costs of a wind farm. Although the operator usually collects and stores maintenance data (in the form of paper and often hand-written maintenance reports) no useful information is extracted. In this thesis a method is presented, which enables the wind farm operator to use the already collected and stored maintenance data of the wind farm to accurately determine the probabilities of wind turbine failures in the wind farm. This basically means that instead of using general and often inaccurate data, the wind farm operator would use situation specific data (from his own wind farm) in order to estimate the maintenance needs and costs of this particular wind farm. Condition monitoring techniques have been applied successfully in other branches of industry for a long time. In 2002 the European project CONMOW [9] was started in order to investigate whether condition monitoring techniques can also have added value for application in wind turbines. In the project it was also investigated whether SCADA data can be used for assessing the health of the turbines and whether the data can be used for early fault detection. Due to limited measuring time it was not possible to demonstrate the feasibility. In addition to this it was concluded that the large amount of data are difficult to interpret by a wind farm operator. In this thesis the applicability of using SCADA data for early fault detection is investigated using a larger set of data. Also it is tried to develop a format which presents the vast amount of data in an accessible way. This format gives the wind farm operator a clear monthly overview of the relevant SCADA signals. MSc thesis 9

10 1.3 Structure of report This report is structured into three major blocks. The first part (chapter 2 till chapter 5) treats the relative fatigue damage in an offshore wind farm. In chapter 2 some information is provided on the ECN Wind Turbine Test Location Wieringermeer (EWTW). In chapter 3 the relevant fatigue loading parameters are discussed for each main wind turbine component. Using data from mechanical load measurements at EWTW relations between wind conditions and these characteristic fatigue load parameters are established. This is described in chapter 4. The last step consists of the calculation of the relative fatigue damage in a fictitious, but realistic, offshore wind farm. This calculation is performed for the blades, tower, main bearing, gearbox bearing and gearbox. The outcome of this relative fatigue damage within an offshore wind farm can be used to devise different estimates of the failure rates of the lighter and heavier loaded turbines in the farm. In the second part of this thesis (chapter 6) the maintenance reports of a wind farm, consisting of nine modern (megawatt class) turbines, are processed and analysed. The analysis consists of the distribution of the failures per turbine and per system. Using the distribution the bottle-neck turbine and turbine component can easily be identified. The second part of the analysis of the failure and maintenance reports consists of a study of the failure rates. This analysis provides the wind farm operator with, for example, information on whether the time between occurring failures is constant, decreases or even increases. This information can then be used to estimate the future maintenance needs and costs and if necessary for altering the preventive maintenance schedule. The third and last part of the thesis (chapter 7) consists of an analysis of the SCADA data of the wind farm. It is investigated whether it is possible to use these data to identify irregular behaviour of the turbines, which, possibly, could lead to a failure. If the accuracy and reliability of this method can be proven it could be used by the wind farm operator for optimizing the O&M strategy on the medium to short-term period. For each of these three major blocks, it has been tried to specify to what extent and in what way the information can be used as input for the O&M Cost Estimator. 10 MSc thesis

11 2. EWTW site description In this chapter some relevant information is provided on the ECN Wind Turbine Test Location Wieringermeer (EWTW) [10],[11],[12]. Its location is shown and information about the relevant wind turbines and meteorological masts is given. The chapter is concluded with a brief overview of the measurements performed at the EWTW. 2.1 General The ECN Wind Turbine Test Location Wieringermeer (EWTW) is located in the Wieringermeer, a polder in the northeast of the province Noord-Holland, 3 km north of the village of Medemblik and 35 km east of ECN Petten (Figure 2.1) [10]. The test site and its surroundings are characterized by flat terrain, consisting of mainly agricultural area, with single farmhouses and rows of trees. The lake IJsselmeer is located at a distance of 2 km east of EWTW. Figure 2.1: Location EWTW The polder Wieringermeer consists of flat agricultural land at an altitude of 5m below sea level. In this area the wind turbine test site, including meteorological masts, is positioned (Figure 2.1) [10]. The East border of the polder is a dike (or sea wall) of ± 8m height, seen from the land site, and 3m height seen from the IJsselmeer. 2.2 Layout An overview of EWTW is shown in Figure 2.2 [10]. The map shows the topography, the several wind turbines and meteorological masts located at the site. The EWTW contains two rows of wind turbines; a row of five research Nordex N80 turbines and a row of four prototype turbines. In addition to these two rows some single wind turbines are also located near the research and prototype turbines. South of the row of prototype turbines a row of NEG Micon turbines is located. For wind measurements three meteorological masts are located at the EWTW; meteorological mast 3 (MM3) just south of the row of research turbines and meteorological masts 1 and 2 (MM1 and MM2) just south of the row of prototype turbines. MSc thesis 11

12 2000 Meteo mast Meteo mast 1 Meteo mast 2 Nordex N80 NM52 Meteo mast Proto types Single WEC km Figure 2.2: Layout EWTW, where numbers 1 till 4 are the prototype turbines and numbers 5 till 9 are the research Nordex turbines. Nearby single turbines are also numbered. The three meteorological masts are shown as well. The prototype, research and nearby single turbines are numbered as is indicated in Figure 2.2 and some information is given in Table 2-1. The table also gives the name of the turbines, which will be used consequently throughout this report. 12 MSc thesis

13 Table 2-1: Description wind turbines EWTW. Number Description Rated Power [kw] Rotor diameter [m] Name in report 1 NEG Micon NM NM92 2 GE GE GE GE Siemens Siemens Nordex N N Nordex N N Nordex N N Nordex N N Nordex N N NEG Micon NM NM52-south 11 NEG Micon NM NM52-north 12 Vestas V V52 13 Lagerwey LW LW750 The relevant distances and relative directions of the objects are given in Table A.1 and Table A.2 in Appendix A. 2.3 Measurements The main interest for this thesis are the measurements performed on the Nordex N80 turbines The Nordex N80 is a modern three-bladed, variable speed, pitch-controlled wind turbine with a rotor diameter of 80 m and a rated power of 2500 kw. This section will give a brief overview of the measurements performed at EWTW and the Nordex N80 research turbines. For more detailed information references [11] and [12] can be consulted Meteorological Wind speed measurements, using both cup and 3D sonic anemometers, are performed at several heights on three meteorological masts. At meteorological mast 3, which is located just below the row of Nordex research turbines, wind conditions are measured at 52 m, 80 m (hub height) and 108 m. In addition to this wind direction, air pressure and temperature difference are also monitored Turbine data SCADA data is collected from five N80 turbines. The data consists of the 10-minute statistics of 134 signals and 25 Hz data of eight signals. In addition to the SCADA data ECN has developed the DANTE data acquisition system, which is used to gather the data from the various sensors placed inside the turbines Mechanical loads Mechanical load measurements are performed using strain gauges in the blades and tower of turbine N80-6. Here some information about both is provided. For detailed information on the mechanical load measurements reference [12] can be consulted Blade loads Blade root bending moments are measured in edgewise and flapwise direction (blade coordinate system). These moments can be translated to in-plane and out-of-plane (hub coordinate system) blade root bending moments using equation 2.1. M M in out cosα c cosθ = sinθ cosα sin c cosθ θ M M e f (2.1) MSc thesis 13

14 where α c is the cone angle, θ the blade pitch angle and M in, M out, M e and M f are the in-plane, out-of-plane, edgewise and flapwise blade root bending moments respectively Tower loads The tower bottom bending moment is measured in North-South and East-West direction. From these measurements the sidewise and for-aft moment, which cause tower bending perpendicular and along the rotor axis respectively, can be derived according equation 2.2. M M side for aft sinϕ = cosϕ cosϕ M sinϕ M NS EW (2.2) where φ is the nacelle yaw angle (with respect to North) and M side, M for-aft, M NS and M EW are the sidewise, for-aft, North-South and East-West tower bottom bending moment respectively. 14 MSc thesis

15 3. Loading of components The goal of the first part of this thesis is to study the relative fatigue damage (and thus lifetime) of the turbines in an offshore wind farm. In this chapter the characteristic parameter for describing fatigue loading is discussed for the blades, tower, main bearing, gearbox bearing and gearbox of a wind turbine. 3.1 Theory on fatigue Fatigue is defined as the permanent structural damage that occurs when a material is subjected to fluctuating stresses that have maximum values less than the static yield strength of the material S-N curve The performance of a material subjected to stress fluctuations is described by the S-N curve (also known as the Wöhler curve), which shows the magnitude of a stress cycle (S) against the number of cycles (N), usually in a double logarithmic graph. An example of an S-N curve is shown in Figure 3.1. Figure 3.1: Example of an S-N curve S-N curves are derived from tests on samples of the material to be characterized. The material is subjected to sinusoidal stress fluctuations with a certain amplitude. The number of cycles after which the material fails defines one point of the S-N curve. If the test is repeated using different stress amplitudes an S-N curve can be drawn. The slope of the S-N curve (also known as Wöhler s constant m) is highly material dependent Miner s rule Miner s rule states that if a material is subjected to a number of stress cycles N smaller than the maximum amount of stress cycles (at which the material would fail) N D a fraction d = N / N D of the material s lifetime is used. This fraction is called partial damage Complex loading In reality mechanical loading is highly complex, where large load fluctuations are followed by small and vice versa. In order to asses fatigue of a material subjected to a complex load the load history is transferred to a series of simple cyclic loadings using rain flow counting. From this rainflow counting a histogram can be constructed, which contains the number of stress cycles for each stress amplitude. Using Miner s hypothesis partial damage can be determined for each MSc thesis 15

16 stress amplitude. Total damage D can be determined by a summation of partial damage d. Miner s hypothesis states that the material fails if D = Blades The blades of the wind turbine convert the kinetic energy stored in the wind to mechanical energy in the form of rotation. A wind turbine s blade is subjected to severe load fluctuations during its lifetime. For every rotation of the rotor each blade experiences a complete gravity stress reversal and in addition to this wind shear, yaw error, shaft tilt, tower shadow and turbulence cause fluctuations in the out-of-plane blade bending moment. Therefore fatigue damage is an important parameter when estimating the lifetime of the blades. It should be mentioned that other effects, such as extreme loading or lightning strikes also influence the lifetime of a wind turbine blade. The parameter used for expressing fatigue loading of the blade (and tower) is the damage equivalent load range. The damage equivalent load range FEQ is the load range that for some arbitrarily chosen number of cycles N would produce the same damage as all actual load ranges (which follow from rain flow counting) combined. This is shown in equation 3.1; 1 m 1 m FEQ = n j F j N (3.1) j where m is the slope of the S-N curve and n j is the actual number of cycles and F j is the actual load range for each occurring case j. The damage equivalent load is calculated for each 10-minute period and using a frequency of 1 Hz. Representing a load history by one characterizing parameter gives the possibility of correlating fatigue loading with external parameters. 3.3 Tower Similar as the wind turbine blades the wind turbine tower is also subject to a fluctuating load pattern. The load on the tower fluctuates due to variations in rotor thrust, and to lesser extent, due to each blade passage. Similar as for the blades the damage equivalent load range FEQ (see equation 3.1) is also used for characterizing the fatigue loading of the tower. A distinction is made between load fluctuations in for-aft direction (along the rotor axis) and side direction (perpendicular to the rotor axis). 3.4 Drive train The previous sections discussed loading on the wind turbine blades and tower. The loads on these two components are directly measured using strain gauges (see section 2.3.3). Unfortunately no load measurements are performed on the drive train components. Therefore an effort is made to estimate the loads on the drive train components using the load measurements on the blades and tower. In order to do this a simple model of the drive train is derived. Using this simple model it is possible to calculate the relevant loads on the main bearing, gearbox bearing and gearbox using the loads measured on the blades and tower of the turbine. In the following subsections first the drive train model is discussed, where attention is paid to the several assumptions that have been made. After that the translation of the loads on blades 16 MSc thesis

17 and tower to loads on the shaft is treated. This section is concluded with a description of the characteristic fatigue loading parameter for the main bearing, gearbox bearing and gearbox respectively Drive train model A simplified model of the drive train of the Nordex N80 is constructed. The shaft is supported by the main bearing, which is located adjacent to the rotor. A second bearing at the opposite end of the shaft is integrated in the gearbox; this bearing will be referred to as gearbox bearing. The gearbox is part of a so-called 3-point drive train assembly. In this 3-point assembly the gearbox housing contains two further cast-on support brackets, one on each side of the gearbox housing. Their main function is to keep the gearbox unit fixed in position by two corresponding main chassis supports, an assembly designed for a limited degree of freedom by means of rubber dampers. For the calculation of the relevant forces on bearings and gearbox the following assumptions are made: 1) All axial force is absorbed by the main bearing; the spherical roller type of bearing (main bearing) is able to absorb axial force; whereas the cylindrical roller type bearing (gearbox bearing) is not designed for absorbing axial force. 2) The two bearings can be modelled as hinges; for the main bearing this assumption is reasonable, however the gearbox is supported by a 3-point assembly (gearbox bearing and two support brackets), which probably has the result that the amount of radial freedom (especially in horizontal direction) is limited. In Figure 3.2 the simplified drive train model is shown. All relevant forces, moments and distances are shown in the figure. Figure 3.2: Simplified model of the drive train of the Nordex N80 turbine. The relevant distances, forces and moments are indicated. The relevant distances have been determined using technical drawings of the nacelle of the Nordex N80 and the weights are specified in the technical data sheet of the Nordex N80. MSc thesis 17

18 3.4.2 Moments and forces For the load calculation of the drive train components the thrust force and the tilt and yaw moments need to be derived from the load measurements on blades and tower. This is described in this section. The tilt and yaw moments on the shaft can be calculated from the three out-of-plane blade root bending moments. This is not entirely correct; in fact the blade root out-of-plane bending moments should be transposed to the rotor centre. Because the tilt and yaw moments are calculated from the load measurements on three blades it is essential to ensure that the load measurements on the three blades are in accordance with each other. Unfortunately strain gauges show drift over the course of time, which badly affects the reliable determination of the tilt and yaw moments. Therefore also a normalization algorithm has been developed as an attempt to eliminate these effects of drift on the calculation of the tilt and yaw moment. Both the procedure for transposing the blade root moments to the rotor centre and the algorithm for eliminating drift effects are discussed in Appendix C. The tilt and yaw moments are calculated from the normalized rotor centre out-of-plane bending moments as shown in equations 3.2 and 3.3. M M = 3 tilt M out RC, norm, b b= 1 = 3 yaw M out RC, norm, b b= 1 ( ψ ), cos ψ m 0, b (3.2) ( ψ ), sin ψ m 0, b (3.3) where M out,rc, norm, b is the normalized rotor centre out-of-plane bending moment of blade b, Ψ m is the azimuth angle and Ψ 0,b is the offset azimuth angle for blade b. The thrust is estimated by considering the tilt moment due to out-of-plane blade bending moments and the for-aft tower bottom bending moment [12]. This is shown in equation 3.4, F thrust M tilt + M for aft = (3.4) l cos T ( α ) t where M tilt is the tilt moment, M for-aft the for-aft tower bottom bending moment, l T the length of the tower and α t the tilt angle. In Figure 3.3 the measured tilt and yaw moments are shown as function of wind direction and the thrust force is plotted as function of wind speed. The data (10-minute averages) are derived from measurements on turbine N80-6 at EWTW. 18 MSc thesis

19 Figure 3.3: Tilt moment, yaw moment as function of wind direction and thrust force as function of wind speed. Tilt moment mainly is a function of vertical wind shear. Because wind speeds usually increase with height the tilt moment generally has a positive value. However, the upper graph shows that at wind directions of 90 and (even more significantly) 270 the tilt moment clearly has a considerable negative value. On the other hand at wind directions of 100 and (even more significantly) 280 the tilt moment is larger compared to non-wake conditions. These two observations indicate that in partial wake operation the tilt moment can either be significantly positive or significantly negative depending on which side of the wake the turbine is operating. In Appendix D it is tried to identify the possible causes for this observation. The yaw moments in non-wake conditions vary around zero, where yawed wind inflow is the main cause for an experienced yaw moment. In partial wake conditions, where one half of the rotor experiences wake flow (reduced wind speed) and the other half of the rotor experiences non-wake flow (ambient wind speed), the yaw moment is significantly positive or negative depending on which side of the wake the turbine is operating. Thrust is generally a function of wind speed. It increases with wind speed until nominal power is reached, at which point the blade pitch angle is altered, resulting in a decreasing thrust force with wind speed Main bearing The forces and moments on the shaft result in a force on the main bearing. In this section the forces on the main bearing are calculated using simple beam theory. As follows from the assumptions described in section all axial force is absorbed by the main bearing. MSc thesis 19

20 Loading When we consider a balance of moments around the gearbox bearing it shows that for the vertical plane; M ( y1 + y2 ) cos( t ) Fz, rotor ( y2 y4 ) cos( α t ) Fz, shaft + y3 ( t ) Fz, gearbox = α α (3.5) 1 M tilt cos and for the horizontal plane; M 2 = M yaw (3.6) The radial force on the main bearing is calculated by combining the moments in the horizontal and vertical plane and dividing them by the distance between the main bearing and the gearbox bearing. 2 2 F 1 r = M 1 + M y 2 (3.7) 2 The axial force on the main bearing is equal to the sum of the thrust force and the horizontal components of the gravitational forces; ( ) ( F + F F ) F a Fthrust + sin t z, rotor z, shaft + z, gearbox = α (3.8) For lifetime calculations the axial and radial forces on the bearing have to be combined to one characterizing parameter. For spherical roller bearings this combined bearing load is calculated using equation 3.9 [13], P = F r + 3.4F P = 0.67 F r a F a F for F F for F a a r r 0.2 > 0.2 (3.9) where F a and F r are the axial and radial bearing force respectively Fatigue analysis For bearing lifetime calculations bearing loads have to be specified in terms of a load duration distribution, which on discretized form gives the number of hours in operation within each of a number of suitably chosen load intervals. The dynamic equivalent load needed for lifetime calculations can be derived using equation 3.10 [14], hi P = eq Pi (3.10) i ht where P i is the ith defined combined bearing load interval, h i the time of operation at this load interval and h t the total time of operation. This dynamic equivalent load is calculated for each 10-minute time series. 20 MSc thesis

21 3.4.4 Gearbox bearing The procedure of the calculation of the forces on the gearbox bearing is similar to the procedure for the main bearing as described in the previous section Loading Using simple beam theory and considering a balance of moments around the main bearing it is derived that for the vertical plane; ( t ) Fz, rotor + y4 cos( α t ) Fz, shaft + ( y2 + y3 ) ( t ) Fz gearbox = α α (3.11) M 1 M tilt y1 cos cos, and for the horizontal plane; M 2 = M yaw (3.12) All axial force is absorbed by the main bearing (see assumptions in section 3.4.1) and therefore; F = 0 (3.13) a The radial force on the main bearing is calculated by combining the moments in the horizontal and vertical plane and dividing them by the distance between the main bearing and the gearbox bearing. 2 2 F 1 r = M 1 + M y 2 (3.14) 2 Because of the absence of an axial force the combined load for the gearbox bearing is obviously equal to the radial force on the bearing Fatigue analysis The characteristic parameter for describing the fatigue loading of the gearbox bearing is similar to the parameter for the main bearing, as is described in equation Gearbox Background At the moment around 85% of all installed wind power [15] still uses a conventional multistage gearbox. With the rapid increase in size and power of modern turbines also the challenge of designing reliable gearboxes that are able to withstand the massive forces they are subject to has become more and more complicated. According to a recent report all 30 Vestas V80 offshore wind turbines installed at Scroby Sands (UK) needed gearbox bearing replacements after less than one and a half year of operation [16]. Another report reveals that in a five-year-old wind farm some turbines have already undergone two or three gearbox replacements [15]. According to Jan van Egmond, who is the managing director of an independent Dutch consultancy specialized in wind turbine inspection and damage assessment, a lot of the gearbox-related problems are caused by misalignment of gearbox and generator shafts. Other causes are the lack of knowledge on determining the real turbine design loads combined with the demand of top head mass reduction and the compactness of the system. In addition to this under-dimensioning of specific components, insufficient rigidity of the gearbox housing, problems with lubrication and oil filtration and excessive overheating are known causes of gearbox problems. MSc thesis 21

22 Loading The problems described in section indicate that a detailed lifetime calculation for a wind turbine gearbox is immensely complex. Effects of misalignment, bending, damage or deformation of gears or bearings on the lifetime of a gearbox require detailed knowledge on the construction of the gearbox and how these effects influence the lifetime of a gearbox. This knowledge might be available to the manufacturer of the gearbox, but is out of reach for this study. In this study a simplified fatigue analysis for the gearbox is performed. Literature [17],[18] suggests that torque is the most important parameter when considering gearbox fatigue. Because no measurements on the shaft are performed, other parameters have to be used to estimate the torque on the high-speed and low-speed shafts. Basically two options are available: 1) Determination of rotor torque by summing the blade root in-plane bending moments of the three blades. It should be noted that the blade root in-plane bending moments have to be converted to the rotor centre. Because the in-plane moments are caused by both a gravity component and an aerodynamic component it is practically impossible to determine the offset in a similar way as was done for the out-of-plane moment (see Appendix C). 2) Calculation of torque in the high speed shaft using electrical power and rotational speed of the generator. From these two options the second one is deemed the more reliable of the two in order to estimate the shaft torque. The torque on the high speed shaft is calculated using equation 3.15, P e T hss = (3.15) ngen where T hss is the high speed shaft torque in knm, P e is the electrical power output of the generator in kw and n gen is the rotational speed of the generator in rad/s. When neglecting efficiencies the torque in the low speed shaft can be determined by multiplying the high speed shaft torque T hss with the gearbox ratio R GB, T lss = T R (3.16) hss GB Fatigue analysis For the fatigue analysis of a gearbox the so-called equivalent torque T eq is used as a measure [17]. This equivalent torque is calculated by multiplying the nominal torque of the gearbox with the application factor K a, which accounts for variations in torque. This is shown in equation 3.17, T eq = K T (3.17) a N where T eq is the equivalent torque, K a is the application factor which accounts for torque fluctuations and T N is the nominal torque. In theory the lifetime of the gearbox, which would be subjected to this constant equivalent torque, would be equal to a gearbox subjected to the real load pattern. 22 MSc thesis

23 The nominal torque on the high speed shaft is determined by considering the nominal power of the gearbox and the nominal rotational speed of the gearbox. Using the gearbox ratio the nominal values for the low speed shaft can also be determined. Values for K a can be found in handbooks and generally vary between 0.6 and 3. If the load spectrum of the gearbox is known factor K a can also be calculated using equation 3.18 [17]; K a N p 1 p i i = (3.18) i N N eq T T where N i is the number of load changes (rotations) during the ith time interval, N eq the equivalent amount of load changes (rotations), T i the torque level at the ith time interval, T N the nominal torque level and p a factor which depends on the kind of material used (here p = 6.7 is used). This equivalent torque is calculated for each 10-minute time series for both the gearbox high and low speed shaft. 3.5 Conclusions In this chapter the characteristic fatigue loading parameters have been determined for blades, tower, main bearing, gearbox bearing and gearbox. For blades and tower fatigue loading is characterized by the damage equivalent load range F EQ, which describes the load fluctuations in a 10-minute period by a single value. The loads on blades and tower are directly measured using strain gauges. Using a simplified model of the drive train the loads on the drive train components is estimated. For both the main bearing and gearbox bearing fatigue loading is characterized by the dynamic equivalent load P EQ, whereas for the gearbox equivalent torque T EQ is the characteristic fatigue loading parameter. Both parameters are calculated for each 10-minute period. MSc thesis 23

24 4. Relation between fatigue loading and wind conditions In this chapter it is tried to establish a relation between wind conditions and the characteristic fatigue loading parameter (as determined in chapter 3) of the blades, tower and drive train components. The distribution of wind conditions in time is generally known. Therefore, if a relation between wind conditions and fatigue loading can be established, also the distribution of fatigue loading in time can be estimated. Using this method the relative fatigue damage (and thus lifetime) of the turbines in an offshore wind farm can be determined. Wind conditions measured by the nacelle anemometer will be used for the relations with fatigue loading. This has several advantages over using a meteorological mast for the characterization of wind conditions: 1) Amount of data: If the meteorological mast is used to measure inflow wind conditions only a small wind direction sector is suitable for establishing a relation between wind conditions and fatigue loading, whereas if the nacelle anemometer is used for characterizing wind conditions all wind directions are suitable for use in order to determine the relations. 2) Wake conditions: Using the nacelle anemometer the relationships between wind conditions and fatigue loading can be determined for free-stream, single wake and multiple wake situations, whereas when using inflow measurements with a meteorological mast the relationships can only be determined under free-stream conditions. 3) Offshore wind farm: It is expected that in an offshore wind farm one or two turbines are equipped with mechanical load measurements. These turbines can be used to establish the relations between fatigue loading and wind conditions (measured by the nacelle anemometer). Using these relationships the mechanical loading on the other turbines in the farm can be estimated. This is not possible if the relationships would be established using the meteorological mast to characterize wind conditions. The disadvantage of using nacelle anemometry is that the measured wind conditions are per definition not equal to wind conditions measured at for instance a meteorological mast. This is mainly caused by the disturbance of the rotor, which of course is located just in front of the nacelle anemometer. However, in [19] it was shown that the average wind speed measured by the nacelle anemometer can be corrected to match the average wind speed measured at a meteorological mast. In this thesis also a relation is established between turbulence measured by the nacelle anemometer and turbulence measured by the meteorological mast. The corrections for both nacelle wind speed and nacelle turbulence are discussed In Appendix B. In the following subsections the relationship between the characteristic fatigue loading parameter (as determined in chapter 3) and wind conditions (measured by the nacelle anemometer) are discussed for the blades, tower, main bearing, gearbox bearing and gearbox respectively. 4.1 Blades As described in section 3.2 the damage equivalent load parameter F EQ will be used for characterizing the fatigue loading of a wind turbine blade. In this section it is investigated which parameters (measured by the nacelle anemometer) are most suited for estimating blade fatigue loading. The structural properties of a wind turbine blade differ in flapwise and edgewise direction and therefore a relation is determined for the fatigue loading in both directions. 24 MSc thesis

25 4.1.1 Blade root flapwise bending The flapwise blade root bending moment of the blade fluctuates due to wind shear, yaw error, shaft tilt, tower shadow and turbulence. These fluctuations in bending moment, and thus stress, cause fatigue damage in the blade Turbulence in non-wake and wake conditions Literature [6],[7],[8] suggests that turbulence is the parameter most suited to estimate fatigue loading due to bending moment fluctuations in the flapwise direction of the blades. In references [19] and [20] it was already shown that turbulence intensity is significantly higher in wake conditions compared to non-wake conditions. However, these observations were made using measurements at the meteorological mast 3. The intention here is to use parameters measured by the nacelle anemometer for the relation with fatigue loading. In Figure 4.1 the turbulence intensity measured at N80-6 is shown as function of wind direction. Figure 4.1: Nacelle turbulence intensity as function of wind direction. Only data where the 10- minute average wind speeds lies between 6 and 8 m/s are considered. At a wind direction of 95 turbine N80-6 experiences the triple wake of both N80-7, N80-8 and N80-9, and for a wind direction of 275 it is influenced by the single wake of turbine N80-5. The effect of the wakes (increased turbulence intensity) can be clearly distinguished in the graph. For both single and triple wake the turbulence intensity seems largest at ±5 of the wake centres (275 and 95 respectively). In addition to this a small, but visible increase in turbulence intensity is observed at a wind direction of about 196. This increase is caused by the double wake of the NM92 and NM52-south wind turbines Fatigue loading in non-wake and wake conditions In the previous section it was shown that the turbulence level in wake conditions is considerably higher compared to free-stream conditions. In this section it is investigated whether also fatigue loading is larger in wake conditions compared to free-stream conditions. Fatigue loading for the blades (and tower) is characterized by the damage equivalent load range F EQ, as was shown in chapter 3. In Figure 4.2 the damage equivalent load range is shown as function of wind direction. The damage equivalent load range is calculated using a slope of the S-N curve equal to MSc thesis 25

26 m = 7. Only data is selected where wind speed is between 6 and 8 m/s. The figure gives information on if, as would be expected, fatigue loading is higher in wake conditions compared to non-wake conditions. Figure 4.2: Damage equivalent flapwise blade root bending moment range as function of wind direction. Only data where the 10-minute average wind speed lies between 6 and 8 m/s is considered. The damage equivalent load range is calculated using an S-N curve slope value m = 7. The increased fatigue loading when the turbine operates in single and triple wake (at wind directions of 275 and 95 respectively) can be distinguished in the graphs. In addition to this also the NM92 and NM52-south turbines at 196 cause a significant increase in fatigue loading. Comparing the maximum values of the damage equivalent loads little difference can be observed between single and triple wake. It is clearly visible that the fatigue loading in partial wake is visibly higher than in mid-wake. This is a consequence of the fact that in partial wake for every rotation of the rotor each blade would experience both non-wake conditions (ambient wind speed) and wake conditions (reduced wind speed), causing considerable bending moment fluctuations in flapwise direction Fatigue loading versus turbulence The two previous sections show that the profiles of turbulence and fatigue loading are quite similar; both turbulence levels and fatigue loading are higher in wake conditions compared to non-wake conditions and in addition to this both turbulence levels and fatigue loading in partial wake is slightly larger compared to mid-wake. These observations indicate the likelihood that the two parameters give a good relation. After initial research it is found that the fatigue loading increases with both wind speed and turbulence intensity. Further on it will be shown that another parameter is needed in order to use one relation for both non-wake and wake conditions. In order to make this relation visible in a graph it is preferable to have one parameter for the characterization of turbulence. For that reason here nacelle turbulence (defined as standard deviation of wind speed, measured by the nacelle anemometer) is used for the relation with fatigue loading (in terms of damage equivalent 26 MSc thesis

27 load range). It is found that the relation with this single parameter (turbulence) gives similar results compared to when two parameters (wind speed and turbulence intensity) are used. On the basis of Figure 4.1 and Figure 4.2 it is chosen to define the single and triple wake as 30 sectors around 275 and 95 respectively. Outside these sectors turbulence intensity and fatigue loading are at a similar level compared to non-wake conditions. Table 4-1: Specification of wind direction sectors for free-stream, single wake and triple wake conditions. Wind directions Free-stream & Single wake 275 ±15 Triple wake 95 ±15 Figure 4.3 shows the relation between nacelle turbulence (standard deviation of wind speed, measured by nacelle anemometer) and the damage equivalent flapwise blade root bending load range for operation in non-wake, single wake and triple wake conditions. Figure 4.3: Relation between nacelle turbulence and damage equivalent blade root flapwise bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 7. Figure 4.3 shows that for both free-stream, single wake and triple wake the relation between turbulence and equivalent load shows a relatively small amount of scatter. For the same measured turbulence the corresponding equivalent load for operation in wake is higher than when operating in free-stream. This indicates that besides (lateral) turbulence there must be other effects that contribute to the load fluctuations. Another interesting observation is that for turbulence values higher than 3 m/s (which correspond to operation at nominal power) the equivalent load for free-stream and single wake is equal for the same level of nacelle turbulence, whereas for triple wake fatigue loading seems higher, even for high wind speeds. MSc thesis 27

28 In Appendix E it is investigated whether the observed differences between free-stream, single wake and triple wake can be explained by other parameters. It is found that no clear difference exists between single and triple wake in terms of wind speed deficit, power deficit and horizontal wind shear. However, measurements seem to indicate that vertical turbulence seems to be larger in multiple wake conditions compared to single wake conditions. The measurement campaign on the LIST-turbine, described in [7], shows that vertical turbulence has, besides wind speed and turbulence intensity, the largest influence on fatigue loading. This could explain the difference between single and triple wake shown in Figure 4.3. Due to the fact that vertical turbulence can only be measured at the meteorological masts and not at the turbines this suspicion cannot be confirmed Fatigue loading versus turbulence and wind speed deficit In section it was shown that turbulence gives a good relation with the damage equivalent load range, although a difference was observed between free-stream, single wake and triple wake. In order to use one relation for all situations the wind speed deficit is chosen as a second parameter. Wind speed deficit is defined as; U local τ w = (4.1) U free stream Wind speed deficit is obviously equal to one in case the wind turbine operates in non-wake conditions and smaller than one in case of wake operation. Therefore this parameter should be able to 'match' the relation for free-stream and wake. Unfortunately this additional parameter is not able to completely 'match' the relation for single and triple wake, because no difference in wind speed deficit was observed between single and triple wake (see Appendix E). The relation between turbulence, wind speed deficit and damage equivalent load range is shown in Figure 4.4. The data for free-stream conditions is shown in black, the data for single and triple wake conditions are shown green and red respectively, and the quadratic surface fit (based on the binned averages of the combined free-stream and wake data) is shown in colour. 28 MSc thesis

29 Figure 4.4: Relation between nacelle turbulence, wind speed deficit and damage equivalent blade root flapwise bending moment range, where the damage equivalent load range is calculated using a slope of the S-N curve m = 7. MSc thesis 29

30 4.1.2 Blade root edgewise bending The in-plane blade root bending moment fluctuates mainly as a result of the changing gravity stress for each revolution of the rotor. In this section it is investigated if turbulence also has an effect on the amount of fatigue loading due to fluctuations in the blade root edgewise bending moment Fatigue loading in non-wake and wake conditions In Figure 4.5 the damage equivalent blade root edgewise bending moment is shown as function of wind direction, where only wind speeds between 6 m/s and 8 m/s are considered. The damage equivalent load range is calculated using a slope of the S-N curve m = 7. The figure gives information on how the fatigue loading in wake situations compares to non-wake situations. Figure 4.5: Damage equivalent edgewise blade root bending moment range as function of wind direction. Only data where the 10-minute average wind speed lies between 6 and 8 m/s is considered. The damage equivalent load range is calculated using an S-N curve slope value m = 7. The effect of the experienced single and triple wake (at 275 and 95 respectively) can be distinguished in the graph. Interestingly, the damage equivalent load in wake has an asymmetric profile. At centre wake the fatigue loading is about equal to undisturbed conditions, the largest fatigue loading occurs for a wind direction of about 10 smaller than centre wake (265 and 85 for single and triple wake respectively) and the smallest fatigue loading occurs at a wind direction of about 10 larger than centre wake (285 and 105 respectively). A clear explanation for this observation is not available. In addition to the effects of the wakes of the N80 turbines also the increased fatigue loading due to the wakes of the 4 prototype turbines can be observed in the graph, where the highest fatigue loading in in-plane direction occurs when N80-6 operates in the combined wake of both the NM92 and NM52-south turbine at MSc thesis

31 Fatigue loading versus turbulence Figure 4.6 shows the relation between turbulence (standard deviation of wind speed) and the damage equivalent in-plane blade root bending moment for operation in free-stream, single wake and triple wake. Figure 4.6: Relation between nacelle turbulence and damage equivalent blade root edgewise bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 7. Figure 4.6 shows that for non-wake conditions turbulence is a good predictor for the edgewise damage equivalent load range. The relationship is not entirely linear and for turbulence values larger than 3 m/s the damage equivalent load decreases with increasing turbulence. This is caused by pitching of the blades, causing a part of the changing gravity loading to act in the flapwise direction of the blade. For very low values of nacelle turbulence (< 1 m/s) the damage equivalent load range still has a significant positive value. This is solely due to the complete gravity stress reversal with every rotation of the rotor. For operation in single and triple wake the relation between turbulence and damage equivalent load shows much more scatter. This can be explained when looking at Figure 4.1 and Figure 4.5, where it was shown that the turbulence intensity profile in wake is more or less symmetric, whereas on the other hand the profile of the damage equivalent edgewise load range in wake is asymmetric. When correlating these two parameters the large amount of scatter is a logical consequence. In section Appendix E it is shown that the investigated parameters are not able to explain this asymmetric profile of edgewise fatigue loading in wake. Therefore it is decided to correlate the blade edgewise fatigue loading just with turbulence. The relation between turbulence and damage equivalent edgewise load range is shown in Figure 4.7. The data for free-stream conditions is shown in blue, the data for wake is shown in red and the linear surface fit (using a fifth order polynomial fit based on the binned averages of the combined free-stream and wake data) is shown in black. MSc thesis 31

32 Figure 4.7: Relation between nacelle turbulence and damage equivalent blade root edgewise bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 7. The relation between nacelle turbulence and the damage equivalent load is estimated by a fifth order polynomial. It may seem that the accuracy of the fit is not very high for wake conditions, but the largest difference between the data points and the fit is well within 25%, which is, compared to for instance the relation of the flapwise bending moment, very small. 32 MSc thesis

33 4.2 Tower As described in chapter 3 the damage equivalent load range F EQ is the parameter used for characterizing the fatigue loading of the tower. In this section a relation is established between wind conditions measured by the nacelle anemometer and fatigue loading in both for-aft and side direction Tower bottom for-aft bending The tower bending load in axis (for-aft) direction is mainly influenced by the rotor thrust, whereas each blade passage also causes some tower load fluctuations. As a consequence tower fatigue damage is mainly caused by fluctuations in rotor thrust Fatigue loading in non-wake and wake conditions In Figure 4.8 the damage equivalent blade root edgewise bending moment is shown as function of wind direction using only wind speeds between 6 m/s and 8 m/s. For the tower bending moment m = 4 is the relevant slope of the S-N curve, because this value corresponds to steel, which is the material used in modern wind turbine tower structures. Figure 4.8: Damage equivalent for-aft tower bottom bending moment range as function of wind direction. Only data where the 10-minute average wind speed lies between 6 and 8 m/s are considered. The damage equivalent load range is calculated using an S-N curve slope value m = 4. A large amount of scatter is observed. No clear wake effects can be seen, although the damage equivalent load range seems to be a bit higher at wind directions of 95 and 275. Also a peak at around 196 is observed, which can be attributed to the combined wake of the NM92 and NM52-south turbines. MSc thesis 33

34 Fatigue loading versus turbulence Since fluctuations in rotor thrust are the main cause for fluctuations in the for-aft tower bottom bending moment it is expected that the fatigue loading in for-aft direction can be related to turbulence (which causes rotor thrust fluctuations). In Figure 4.9 the relation between turbulence (standard deviation of wind speed) and the for-aft damage equivalent load range is shown for operation in free-stream, single and triple wake conditions. Figure 4.9: Relation between nacelle turbulence and damage equivalent tower bottom for-aft bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 4. For both non-wake and wake conditions a good relation is found to exist between turbulence and damage equivalent for-aft load range. For the same value of turbulence the load range is slightly larger in wake, although for high turbulence (> 3 m/s) the relation for non-wake and wake conditions is more or less at the same level. Also no clear difference between the relation for single and triple wake is found Fatigue loading versus turbulence and wind speed deficit In section it was shown that turbulence gives a good relation with tower for-aft fatigue loading, although a difference was observed between free-stream, single wake and triple wake. Similar as for flapwise fatigue loading (see section ) wind speed deficit will be used to 'match' the relation for free-stream and wake conditions. The relation between turbulence, wind speed deficit and damage equivalent for-aft load range is shown in Figure The data for free-stream conditions is shown in black, the data for single and triple wake is shown in green and red respectively, and the linear surface fit (using multivariate linear regression based on the binned averages of the combined free-stream and wake data) is shown in colour. 34 MSc thesis

35 Figure 4.10: Relation between nacelle turbulence, wind speed deficit and damage equivalent for-aft tower bottom bending moment range, where the damage equivalent load range is calculated using a slope of the S-N curve m = 4. MSc thesis 35

36 4.2.2 Tower bottom side bending In addition to bending in axis direction the tower of a wind turbine also experiences a bending moment perpendicular to the axis direction, the so-called side bending Fatigue loading in non-wake and wake conditions In Figure 4.11 the tower bottom side bending damage equivalent load range is shown as function of wind direction. Figure 4.11: Damage equivalent side tower bottom bending moment range as function of wind direction. Only data where the 10-minute average wind speed lies between 6 and 8 m/s are considered. The damage equivalent load range is calculated using an S-N curve slope value m = 4. Similar to the damage equivalent for-aft load range, also the damage equivalent side load range shows a relatively large amount of scatter. From Figure 4.11 can be concluded that operation in wake does not seem to influence the amount of fatigue loading due to fluctuations in tower bottom side bending moment Fatigue loading versus turbulence In this section the relation between turbulence and damage equivalent side load range is studied. Figure 4.12 shows the relation between nacelle turbulence (standard deviation of wind speed) and the side damage equivalent load range for operation in free-stream, single wake and triple wake. 36 MSc thesis

37 Figure 4.12: Relation between nacelle turbulence and damage equivalent tower bottom side bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 4. An excellent relation between turbulence and damage equivalent tower bottom side bending load range is observed. The relation is found to be similar for free-stream and both single and triple wake. In Figure 4.13 a second order polynomial fit is used to describe the relation between turbulence and tower side fatigue loading. The polynomial is calculated used the binned averages, which are shown in black, that are calculated from the combined free-stream (shown in blue), single wake (shown in green) and triple wake (shown in red) data. MSc thesis 37

38 Figure 4.13: Relation between nacelle turbulence and damage equivalent side tower bottom bending moment range for free-stream, single wake and triple wake conditions. The damage equivalent load is calculated using an S-N curve slope value of m = 4. The relation between nacelle turbulence and damage equivalent load is estimated by a second order polynomial. 4.3 Main bearing In chapter 3 it was described that the dynamic equivalent bearing load is used to characterize the fatigue loading of the main (and gearbox) bearing. The dynamic equivalent load on the main bearing is dominated by the force in axial direction. This is a logical consequence of the weigh factors shown in equation 3.9. As a result the influence of tilt and yaw moment (mostly significant in wake situations, see Figure 3.3) on the dynamic equivalent load is negligible. The only significant influence is the thrust force, which again is a function of wind speed (see Figure 3.3). The relation between wind speed (nacelle, measured at N80-6) and dynamic equivalent load on the main bearing is shown in Figure All wind directions are included (operation in wake is also taken into account). Per wind speed bin (bin size equals 1 m/s) the average dynamic equivalent load on the main bearing is calculated. The binned averages are indicated by the red dots. The relation between the wind speed and dynamic equivalent load on the main bearing is indicated by the red line, which is a cubic interpolation between the binned averages. The extrapolation to values outside the range of measured data (i.e. U < 3 m/s and U > 23 m/s) is done by taking the value of the nearest data point. 38 MSc thesis

39 Figure 4.14: Relation between wind speed and dynamic equivalent load on the main bearing. The relation between these parameters in described using cubic interpolation between the binned averages (red dots) and for extrapolation the nearest value is used. It is shown that the maximum dynamic equivalent load on the main bearing is reached at wind speeds around 13 m/s. 4.4 Gearbox bearing It was found that the characteristic fatigue loading parameters for the bearing is the dynamic equivalent load P EQ. Since the assumption was made that all axial force is absorbed by the main bearing (see section 3.4.1) the rotor tilt and yaw moments have a major influence on the loading of the gearbox bearing. In Figure 4.15 the dynamic equivalent load on the gearbox bearing is shown as function of wind direction. Figure 4.15: Dynamic equivalent load on the gearbox bearing as function of wind direction. The tilt moment generally is a function of vertical wind shear (see Figure 4.16) and the yaw moment occurs generally in partial wake situations (see Figure 3.3). It was also shown that tilt MSc thesis 39

40 moment has an asymmetric shape in partial wake situation; on one side of the partial wake operation the tilt moment is slightly larger compared to the free-stream situation, but on the other side of partial wake operation tilt moment is almost zero or even negative. The result of this is that the largest dynamic bearing load on the gearbox bearing occurs in partial wake; for the situation where the tilt moment is negative and the rotor experiences a significant yaw moment. The maximum dynamic equivalent load on the gearbox bearing is found at wind directions of 90 and 270 and the maximum for triple wake (90 ) is slightly larger than the maximum in single wake (270 ). For non-wake conditions the dynamic equivalent load on the gearbox bearing strongly depends on the tilt moment. The tilt moment is mainly caused by vertical wind shear and therefore it is expected that vertical wind shear can also be used as a measure for the dynamic equivalent load on the gearbox bearing in non-wake conditions. The relation between vertical wind shear (defined as difference in lateral wind speed between 108 m and 52 m height) and tilt moment and dynamic equivalent load on the gearbox bearing is shown in Figure Figure 4.16: Tilt moment and dynamic equivalent load on the gearbox bearing as function of vertical wind shear. The upper graph confirms that the tilt moment is mainly caused by vertical wind shear. The lower graph shows that de dynamic equivalent gearbox load decreases with vertical wind shear in non-wake conditions. Although vertical wind shear gives a good correlation with the dynamic equivalent load it cannot be determined using nacelle anemometry. In addition to this the dynamic equivalent load on the gearbox bearing has an asymmetric profile in wake (see Figure 4.15), which cannot be correlated properly with any other wind conditions describing parameters (e.g. wind speed deficit, turbulence) because these parameters all have a symmetric profile in wake. 40 MSc thesis

41 4.5 Gearbox In section it was shown that the equivalent torque T EQ is the parameter used for quantifying the fatigue loading of the gearbox. After some initial research it is found that the equivalent torque mainly is a function of average electrical power. This is shown in Figure 4.17 for both the high and low speed shaft. Figure 4.17: Relation between average electrical power and equivalent torque for both the gearbox high and low speed shaft. In the theoretical case that the power output would be constant in a 10-minute period the equivalent torque would be equal to the average torque in that 10-minute period. Of course the electrical power output fluctuates during a 10-minute period, which leads to a higher K a (see equation 3.18), and thus a higher equivalent torque T EQ. In order to get a more accurate prediction the standard deviation of the electrical power is also considered. In Figure 4.18 the relation between average electrical power, standard deviation of electrical power and equivalent torque T EQ is shown for both the high and low speed shaft. The surface fit gives a good approximation of T EQ. MSc thesis 41

42 Figure 4.18: Relation between average electrical power, standard deviation of electrical power and equivalent torque for both the gearbox high and low speed shaft. Of course the average electrical power is a function of wind speed. The relation between average wind speed and average electrical power is described by the turbine s power curve. A measure for the fluctuation of electrical power (in terms of standard deviation) is the fluctuation in wind speed (in terms of turbulence). The relation between turbulence and standard deviation of electrical power is shown in Figure Only data where the 10-minute average power lies below 2000 kw is considered, because at around and above nominal power the standard deviation of electrical power obviously is very small, due to the electrical power limitation by blade pitching. 42 MSc thesis

43 Figure 4.19: Relation between nacelle turbulence and standard deviation of electrical power. 4.6 Conclusions In this chapter relationships between wind conditions (measured by the nacelle anemometer) and the characteristic fatigue loading parameters (as determined in chapter 3) have been determined for blades, tower, main bearing, gearbox bearing and gearbox. The relations will be applied together with a wind farm model in the next chapter in order to determine fatigue damage in an offshore wind farm. For the blades turbulence (defined as standard deviation of wind speed) and wind speed deficit (defined as ratio of local wind speed over free-stream wind speed) are the parameters used for the correlation with fatigue loading (in terms of damage equivalent load range) in flapwise direction for both free-stream and wake conditions, whereas just turbulence is suited for estimating the fatigue loading is edgewise direction. The fatigue loading (in terms of damage equivalent load range) of the tower in for-aft direction is best estimated using both turbulence and wind speed deficit, whereas just turbulence gives a good relation with fatigue loading in side direction for both free-stream and wake conditions. As axial force is the dominating parameter for the loading on the main bearing, and therefore the fatigue loading of the main bearing (in terms of dynamic equivalent load) can be estimated by just wind speed. The fatigue loading on the gearbox bearing depends mainly on the tilt and yaw moment. For non-wake conditions vertical wind shear is a good measure for the fatigue loading of the gearbox bearing. Unfortunately vertical wind shear cannot be determined using nacelle anemometry. In addition to this the dynamic equivalent load of the gearbox bearing has an asymmetric profile in wake, which makes it very difficult to correlate the fatigue loading with wind conditions in wake conditions. Average and standard deviation of electrical power are most suited to estimate the fatigue loading in the gearbox (in terms of equivalent torque). In order to estimate the standard deviation of electrical power a relationship with turbulence has been established. It should be mentioned that all relationships are determined using measurements in an onshore wind farm (EWTW) where the turbine spacing equals 3.8 D. The established relations are quite likely situation specific and possibly differ significantly for offshore wind farms with larger turbine spacing or for different type of turbines. MSc thesis 43

44 5. Fatigue damage in an offshore wind farm In a large offshore wind farm the turbines in the middle of the farm face considerable higher turbulence levels and operate more often in partial wake compared to the turbines on the edge of the farm. This raises the suspicion that also large differences in fatigue damage exist between the wind turbines in the farm. To quantify whether the differences are significant a calculation has been performed to determine the relative fatigue damage in a fictitious but realistic offshore wind farm consisting of 25 Nordex N80 turbines. In chapter 3 the characteristic parameters for fatigue loading are specified for the blades, tower, main bearing, gearbox bearing and gearbox. Based on measurements relationships between wind conditions and these characteristic fatigue loading parameters are established, as is described in chapter 4. In this chapter the following steps are taken in order to calculate the fatigue damage in the fictitious offshore wind farm: 1) Wind farm description: A description of the fictitious wind farm is given, where the main focus lies on the layout of the farm. 2) Description offshore wind climate: The probability of occurrence of wind directions and wind speeds are specified and the dependence of ambient turbulence intensity on wind speed is discussed. 3) Calculation of wake effects: The wind resource program FluxFarm is used to determine the wake effects (wind speed decrease and turbulence increase) for each turbine and for each wind direction and wind speed in the wind farm. 4) Determining fatigue load cases: The output of FluxFarm is converted and using the relations described in chapter 4 the fatigue loading for each turbine and for each wind direction and wind speed is determined. These fatigue load cases are determined for blades, tower, main bearing, gearbox bearing and gearbox. 5) Calculation of fatigue damage: When combining the wind climate (which gives the probability of occurrence of each wind speed and wind direction) with the fatigue load cases the fatigue damage for each turbine can be calculated. This calculation is performed for the blades, tower, main bearing, gearbox bearing and gearbox. 5.1 Wind farm description In Figure 5.1 the layout of the offshore wind farm is shown. The rows are shifted with respect to each other in order to increase separation between the individual turbines. The spacing between the turbines in one row equals 7 D, the distance between the rows is 8.3 D, which gives a spacing of 9 D on the diagonals. The turbines are Nordex N80s, which have a rotor diameter of 80m and a rated power of 2.5 MW. 44 MSc thesis

45 Figure 5.1: Layout of the fictitious offshore wind farm 5.2 Wind climate One of the requirements for calculation of fatigue damage is the wind climate. The wind climate basically specifies the probability of occurrence of each load case. In the following two subsections the probability of occurrence of wind direction and wind speed are specified and in addition to this the relation between ambient turbulence intensity and wind speed is determined Distribution wind speed and wind direction A uniform wind climate has been assumed for all 25 turbines. This is reasonable to expect for an offshore wind farm located far (> 20 km) from the coast. In Table 5-1 the characterization of the wind climate expressed in average wind speed U and frequency of occurrence f per wind direction bin is shown. The wind climate is based on the ECN offshore wind atlas [21], location A at 90m height. Table 5-1: Wind climate characterization taken from the offshore wind atlas, location A at 90m height f [%] U [m/s] For further analysis a Weibull characterization is used, which is derived from the tabled data using a Weibull shape factor of k = Ambient turbulence In contrast with land surfaces the roughness of a sea surface is not constant, but depends strongly on wind speed [22]. With increasing wind speed waves are usually higher, which results in an increased roughness length. MSc thesis 45

46 For calculation of fatigue damage the ambient (free-stream) turbulence intensity will be specified as function of wind speed. The relation between wind speed and ambient turbulence intensity is derived from measurements at the 106 m high meteorological mast located at the offshore wind farm Egmond aan Zee [23]. Data from July 2005 till June 2006 have been used. The measured relation between wind speed and turbulence intensity is shown in Figure 5.2. Figure 5.2: Relation between wind speed and turbulence intensity. The relation is calculated using data (July 2005 till June 2006; at 70m height; using all wind directions) from the offshore wind farm Egmond aan Zee. For low wind speeds the main influence on turbulence intensity is thermal induced turbulence. The minimum turbulence intensity (about 6.8%) occurs around wind speeds of m/s. For higher wind speeds turbulence intensity increases slightly with wind speed due to increasing wave height and surface roughness. Here the mechanical part of turbulence begins to dominate while the thermal effect becomes negligible [22]. 5.3 Calculation of wake effects by FluxFarm The ECN wind resource analysis program FluxFarm [24] has been used to determine the local wind conditions for every turbine i in the fictitious offshore wind farm. The output of FluxFarm consists of two parts: 1) Wind speed deficit τ wake, i,j,k for every wind turbine i as function of wind direction θ j and wind speed U free-stream, k. Wind speed deficit is defined as the ratio of local wind speed over free-stream wind speed. This is shown in equation ) Added turbulence intensity I add, i,j,k, for every turbine i, as function of wind direction θ j and wind speed U free-stream, k. The added turbulence intensity is a measure for the increase in turbulence due to the presence of a wind turbine. The wake effects are calculated by FluxFarm using a wind direction bin size of 3 and a wind speed bin size of 1 m/s. In [19] the wake effects simulated by FluxFarm were compared with measurements at EWTW. It was found that for single wake conditions the simulations by FluxFarm are in good accordance with the measurements. In Appendix F a more extensive comparison is made, where attention is paid to the modelling of wake effects in multiple wake conditions. The comparison shows that for multiple wake situations (when a wind turbine operates in the wake of more than one turbine) a difference between the simulated and measured wake effects is found. The simulated profile of wind speed deficit for multiple wake situations is deeper but smaller compared to the measured profile. The effect of this observation on the results of the fatigue damage calculation is uncertain. 46 MSc thesis

47 5.4 Calculation of fatigue damage The calculation steps that are necessary to convert the FluxFarm output to fatigue damage of the wind turbines in the farm are described in the following subsections for the blades, tower, main bearing, gearbox bearing and gearbox respectively. A lot of different terms will be used throughout this section. Therefore here a brief explanation of the used terms is given in order to prevent the reader from getting confused. Three different locations for wind conditions (wind speed & turbulence) are used throughout this section (see Figure 5.3): 1) The free-stream wind; this is the wind, which is undisturbed by wind turbines. 2) The local wind; this is the wind a turbine experiences. Note that in reality a wind turbine also has an effect on the wind conditions just in front of the rotor plane. This however, is not simulated by FluxFarm and therefore here local wind speed can either be equal to free-stream wind speed (in case of the left turbine) or lower than free-stream wind speed (in case of the right turbine). The same holds for local turbulence, which is either equal to free-stream turbulence (in case of the left turbine) or higher than freestream turbulence (in case of the right turbine). 3) The nacelle wind; these are the wind characteristics measured by the nacelle anemometer, which are by definition not equal to the local wind conditions, due to the disturbance of the rotor and nacelle. Figure 5.3: Definition of different wind conditions, where position 1 shows the free-stream wind conditions, position 2 the local wind conditions and position 3 the nacelle conditions. As was described in chapter 4 the wind conditions measured by the nacelle anemometer are not equal to the local values. The relations between wind conditions (wind speed and turbulence) measured by the nacelle anemometer and the local values are shown in Appendix B Blades In section 4.1 it was found that nacelle turbulence σ nacelle and wind speed deficit τ wake are the two parameters most suited for estimating the fatigue loading (in terms of damage equivalent load range F EQ ). For the flapwise direction both nacelle turbulence and wind speed deficit are included in the relation, whereas for the edgewise direction only nacelle turbulence is required in order to estimate the fatigue loading. The following subsections describe the calculation steps that are necessary to convert the Flux- Farm output in order to get the fatigue loading of each turbine for each wind speed and direction MSc thesis 47

48 bin. When combining these fatigue load cases with the offshore wind climate the fatigue damage for each turbine can be calculated Calculation of fatigue load cases The wind speed deficit is a straightforward output from FluxFarm, whereas to determine the nacelle turbulence for each turbine for al wind directions and wind speed bins some conversion steps are necessary. First the local turbulence intensity I local, i,j,k. needs to be calculated for each turbine, wind direction and wind speed. This is achieved by combining the calculated added turbulence intensity I add with the ambient turbulence intensity I free-stream (which is a function of free-stream wind speed, see Figure 5.2) using equation 5.1: I = I + I (5.1) 2 2 local i, j, k free stream add i, j, k This local turbulence intensity can be converted to local turbulence σ local,i,j,k, my multiplying with the local wind speed: σ local i, j, k I local, i, j, k U local, i, j, k = I local, i, j, k U free stream τ wake i, j, k = (5.2) As shown in section 4.1 the relation between wind and fatigue loading is based on turbulence measured by the nacelle anemometer. Therefore the local turbulence has to be converted to nacelle turbulence using the relation shown below, which was determined in Appendix B. σ.5138 σ (5.3) nacelle i, j, k = 1 local i, j, k + After going through the steps described above the nacelle turbulence and wind speed deficit are known for every wind turbine, for all wind directions and all wind speed bins. The relationship between the damage equivalent load range F EQ, the wind speed deficit and the nacelle turbulence (for edgewise direction the relation only depends on nacelle turbulence) has been determined from measurements in sections 0 and With this information, the damage equivalent load range has been determined for all turbines, wind directions and wind speeds: F EQ, i,j,k Calculation of fatigue damage The wind climate determines the probability of occurrence p j,k of each load case (which of course is equal for each turbine i). When multiplying this probability of occurrence of each load case with the lifetime L (in seconds) we get the number of load cycles for each load case N j,k. Using the S-N curve of the material it is possible to specify a maximum allowed load range (at which the component would fail) F D,i,j,k for each corresponding N j,k. Miner s hypothesis states that for a load case where the equivalent load range F EQ,i,j,k is smaller than F D,i,j,k a fraction d i,j,k = ( F EQ,i,j,k / F D,i,j,k ) m of the component s lifetime has been used. This fraction d i,j,k is called partial damage. The total damage of all load cases combined D i is equal to the sum of all partial damages d i,j,k.. This is shown in equation 5.4. D i = j, k m FEQ i F j k EQ i j k di j k N j k L p j k j k FD i j k j k F,,,,,, =, =, (5.4),,,,, D, i, j, k In this study only the relative effects are of interest. It is decided to use the fatigue damage sustained by turbine 1 as reference. With this assumption Miner s rule can be applied to calculate the relative fatigue damage of turbine i using in a straightforward manner by equation 5.5; m 48 MSc thesis

49 D rel m p j, k FEQ, i, j, k Di j, k, i = = (5.5) D1 m p F j, k j, k EQ,1, j, k Flowchart for the calculation of fatigue damage The calculation steps that are necessary in order to determine the fatigue damage for the blades are summarized in the flowchart shown in Figure 5.4. Wind farm layout FluxFarm τ wake(i,θ,u free-stream) I add(i,θ,u free-stream) Relation I free-stream(u free-stream) U free-stream I local(i,θ,u free-stream) U local(i,θ,u free-stream) σ local(i,θ,u free-stream,θ) Relation σ nacelle(σ local) σ nacelle(i,θ,u free-stream) Relation F EQ(σ nacelle,τ wake) F EQ(i,θ,U free-stream) Wind climate Miner s rule Relative fatigue damage Figure 5.4: Flowchart for the calculation of fatigue damage of blades and tower. MSc thesis 49

50 5.4.2 Tower In section 4.2 it was found that nacelle turbulence σ nacelle and wind speed deficit τ wake are the two parameters most suited for estimating the fatigue loading (in terms of damage equivalent load range F EQ ) of the tower. For the for-aft direction both nacelle turbulence and wind speed deficit are included in the relation, whereas for the side direction only nacelle turbulence is required in order to estimate the fatigue loading. The following subsections describe the calculation steps that are necessary to convert the Flux- Farm output in order to get the fatigue loading of each turbine for each wind speed and direction bin. Finally the method for determining the fatigue damage for each turbine is discussed. Obviously effects of waves are not taken into account. The fatigue damage in the tower is caused by both an aerodynamic part (thrust fluctuations) and a hydrodynamic part (waves). The results presented here should be interpreted as the relative fatigue damage (between turbines in the farm) due to aerodynamic effects Calculation of fatigue load cases For the tower the method of calculation of the fatigue load cases is exactly the same as for the blades as described in section , where of course the relations for for-aft and side tower bottom bending moments (as described in sections and respectively) should be used Calculation of fatigue damage The fatigue damage calculation for the tower is similar to the fatigue damage calculation of the blades and thus equation 5.5 can be applied to calculate the relative fatigue damage of the tower (due to aerodynamic forces) of the turbines in the fictitious offshore wind farm Flowchart for the calculation of fatigue damage The procedure for the calculation of the fatigue damage of the tower is similar to the fatigue damage calculation for the blades, which is summarized in the flowchart shown in Figure Main bearing In section 4.3 it was found that nacelle wind speed U nacelle is most suitable to estimate the fatigue loading (in terms of equivalent load P EQ ) of the main bearing. The following subsections describe the calculation steps that are necessary to convert the Flux- Farm output in order to get the fatigue loading of each turbine for each wind speed and direction bin. Finally the method for determining the fatigue damage for each turbine is discussed Calculation of fatigue load cases The wind speed deficit τ wake is a straightforward output from FluxFarm. If the wind speed deficit is multiplied with the free-stream wind speed U free-stream the local wind speed for each turbine for each wind speed and direction is known. This is shown in equation 5.6. U local, i, j, k U free stream τ wake i, j, k = (5.6) As shown in section 4.3 the relation between fatigue loading and wind conditions is based on wind speed measured by the nacelle anemometer. Therefore the local wind speed has to be converted to nacelle wind speed using the relation described in Appendix B. After going through the steps described above the nacelle wind speed is known for every wind turbine, for all wind directions and all wind speed bins: U nacelle,i,j,k. The relationship between the equivalent load P EQ and the nacelle wind speed U nacelle has been determined from measurements, which is described in section 4.3. With this information, the equivalent load has been determined for all turbines, wind directions and wind speeds: P EQ, i,j,k. 50 MSc thesis

51 Calculation of fatigue damage The fatigue damage calculation for bearings and gearbox differs slightly from the fatigue damage calculation of the blades and tower. The main difference is the derivation of the load spectra. For a gearbox the stress cycle of a single gear tooth emerges with every tooth flank contact (i.e. with every revolution), whereas for the stress cycles for the wind turbine blades occur in correspondence with fluctuations (cycles) of its external loads (turbulence, gravity). According to ISO281/1 [14] the basic rating life (in hours) of a roller bearing is defined as; p 10 6 C dyn L 10 = (5.7) 60 n P eq where n is the rotational speed in rpm, C dyn is the basic dynamic capacity of the bearing and p is an exponent, which has the value of 10/3 for roller bearings. The index 10 refers to a 10% failure probability associated with the bearing lifetime L 10. As fatigue damage basically equals the reciprocal of lifetime it is easy to judge that: D ~ n (5.8) p P eq When using this information combined with Miner s hypothesis it can be derived that for each turbine i the fatigue damage D i equals: D i = j, k d P p P EQ i, j, k EQ i, j, k i, j, k = N j, k = L p j, k ni, j, k (5.9) j k C, dyn j, k Cdyn p As the main interest of this calculation is to quantify the relative differences in fatigue damage between the turbines in the farm equation 5.9 can be simplified as shown in equation D rel p p j, k ni, j, k PEQ, i, j, k Di j, k, i = = (5.10) D1 p p n P j, k j, k 1, j, k EQ,1, j, k where p j,k is the probability of occurrence of each load case j,k, n i,j,k, is the rotational speed of the main shaft, P EQ,i,j,k is the equivalent bearing load for each turbine i and load case j,k and p is a material parameter. This expression has the advantage that the relative fatigue damage can be calculated without the need for information on the dynamic capacity of the bearing C dyn. MSc thesis 51

52 Flowchart for the calculation of fatigue damage The calculation steps that are necessary in order to determine the fatigue damage for the main bearing is summarized in the flowchart shown in Figure 5.5. Wind farm layout FluxFarm τ wake(i,θ,u free-stream) I add(i,θ,u free-stream) U free-stream U local(i,θ,u free-stream,θ) Relation U nacelle(u local) U nacelle(i,θ,u free-stream) Relation P EQ(U nacelle) Relation n(u nacelle) P EQ(i,θ,U free-stream) n(i,θ,u free-stream) Wind climate Miner s rule Relative fatigue damage Figure 5.5: Flowchart for the calculation of fatigue damage of the main bearing. 52 MSc thesis

53 5.4.4 Gearbox bearing As described in section 4.4 the fatigue loading of the gearbox bearing cannot be related properly to wind conditions. For free-stream conditions vertical wind shear is the dominating influence, whereas in wake operation the effects of tilt and yaw moment result in an asymmetric profile of the equivalent bearing load in wake, which makes the correlation with wind conditions quite impossible. Therefore in this section no fatigue damage calculation is performed for the gearbox bearing. It should however be mentioned that it is expected that significant differences exist between the heavier and lighter loaded turbines in the farm. This can be easily seen in Figure 4.15 where it was shown that fatigue loading in wake is significantly larger compared to the fatigue loading in free-stream conditions. Because no relationship with wind conditions could be established the differences cannot be quantified Gearbox In section 4.5 it was found that average power P avg and standard deviation of power P stddev are the two parameters most suited for estimating the equivalent torque T EQ. The following subsections describe the calculation steps that are necessary to convert the Flux- Farm output in order to get the fatigue loading of each turbine for each wind speed and direction bin. When combining these fatigue load cases with the offshore wind climate the fatigue damage for each turbine can be calculated Calculation of fatigue load cases The wind speed deficit τ wake is a straightforward output from FluxFarm. If the wind speed deficit is multiplied with the free-stream wind speed U free-stream the local wind speed for each turbine for each wind speed and direction is known. This is shown in equation U local, i, j, k U free stream τ wake i, j, k = (5.11) Using the power curve of the turbine the local wind speed can be converted to average power for each turbine for each wind speed and direction bin: P avg,i,j,k. In Figure 4.19 in section 4.5 it was shown that the standard deviation of power is a function of nacelle turbulence. The calculation of nacelle turbulence from the FluxFarm output I add is described in section Using the relation between standard deviation of power and nacelle turbulence the standard deviation of power is known for each turbine for each wind speed and direction bin: P stddev,i,j,k. After going through the steps described above both the average and standard deviation of electrical power are known for every wind turbine, for all wind directions and all wind speed bins. The relationship between the equivalent torque T EQ, average electrical power and standard deviation of electrical power has been determined from measurements in section 4.5. With this information, the equivalent load has been determined for all turbines, wind directions and wind speeds: T EQ, i,j,k Calculation of fatigue damage As mentioned in section the calculation of fatigue damage for a gearbox differs slightly from the fatigue damage calculation of blades and tower. The stress cycle of a gear tooth occurs with every rotation and therefore the rotational speed is incorporated in the fatigue damage calculation. With this information we can apply Miner s hypothesis (similar as shown in equation 5.4 for blades and tower). MSc thesis 53

54 D i = j, k p TEQ i T j k EQ i j k d i j k N j k L p j k ni j k j k TD i j k j k T,,,,,, =, =,,, (5.12),,,, D i, j, k p Because only relative effects are of interest equation 5.12 can be simplified to get the fatigue damage of turbine i relative to the fatigue damage of turbine 1. This is shown in equation D rel p p j, k ni, j, k TEQ, i, j, k Di j, k, i = = (5.13) D1 p p n T j, k j, k 1, j, k EQ,1, j, k Flowchart for the calculation of fatigue damage In section 4.5 it was found that the characteristic fatigue loading parameter for the gearbox T EQ is best estimated by the average electrical power and standard deviation of electrical power. The calculation steps necessary to convert the FluxFarm output to these two parameters is shown in the flowchart in Figure 5.6. Wind farm layout FluxFarm Relation I free-stream(u free-stream) I add(i,θ,u free-stream) τ wake(i,θ,u free-stream) U free-stream I local(i,θ,u free-stream) U local(i,θ,u free-stream) σ local(i,θ,u free-stream) Relation σ nacelle(σ local) Relation U nacelle(u local) σ nacelle(i,θ,u free-stream) U nacelle(i,θ,u free-stream) Relation P stddev(σ nacelle) Relation P avg(u nacelle) Relation n(u nacelle) P stddev(i,θ,u free-stream) P avg(i,θ,u free-stream) n(i,θ,u free-stream) Relation T EQ(P avg,p stddev) T EQ(i,θ,U free-stream) Wind climate Miner s rule Relative fatigue damage Figure 5.6: Flowchart for the calculation of fatigue damage of the gearbox. 54 MSc thesis

55 5.5 Results of the calculation In this section the results of the fatigue damage calculation are presented. The calculation is performed for the blades, tower, main bearing and gearbox, using the method described in section 5.4. As mentioned in the previous section the fatigue damage of the turbines in the fictitious farm will be specified relative to the fatigue damage sustained by turbine 1 (which is located at the southwest corner of the farm, see Figure 5.1). It should be noted that the results presented in the following subsections are determined without taking the effects of waves into account. Obviously waves have a significant effect on the loading of the support structure (tower) of the wind turbines in an offshore wind farm. In addition to this it is possible that due to the excitation of the support structure also the loading in the blades and drive train components is influenced. Unfortunately it has not been possible to investigate the influence of waves on the loading of the wind turbine components, due to the fact that the wind turbine model of the Nordex N80 turbine in the ECN aero-elastic code PHATAS was not completed in time for this thesis Blades As discussed in chapter 4 the structural properties of wind turbine blades differ for flapwise and edgewise direction. Therefore, the relation between wind conditions and fatigue loading was established for both the flapwise and edgewise bending moment. In the following subsections the fatigue damage (relative to turbine 1) is discussed for both the flapwise and edgewise blade root bending moment. The fatigue damage of the blades is calculated using a slope of the S-N curve of m = Flapwise blade root bending moment The fatigue damage (relative to the fatigue damage of turbine 1) of the 25 turbines in the farm is shown in Figure 5.7. For this calculation the ambient turbulence intensity is specified as function of wind speed (as shown in Figure 5.2). Figure 5.7: Fatigue damage (relative to turbine 1) for the blade root flapwise bending moment for the 25 turbines in the fictitious offshore wind farm. The calculation is performed using ambient turbulence intensity which is specified as function of wind speed. MSc thesis 55

56 The results of the calculation show that the difference in fatigue damage between the heaviest and lightest loaded turbine (turbine 24 and 1 respectively) is in the order of 10%. To investigate the influence of the ambient turbulence intensity on the outcome of the fatigue damage calculation the same calculation is performed using a constant (independent from wind speed) ambient turbulence intensity. The results for this calculation are shown in Figure 5.8. Figure 5.8: Fatigue damage (relative to turbine 1) for the blade root flapwise bending moment for the 25 turbines in the fictitious wind farm. The calculation is performed using different values of (wind speed independent) ambient turbulence intensity. These results indicate that in case the level of ambient turbulence intensity is very low, considerable differences in fatigue damage exist between the heavier and lighter turbines in the farm. This is easily explained by looking at equation 5.1; for low values of ambient turbulence intensity the added turbulence intensity (which is calculated by FluxFarm) has a significant influence, whereas for high levels of ambient turbulence intensity the added turbulence intensity is less significant. This explains why for turbulence intensities of 10% or higher the difference in fatigue damage between the turbines becomes negligible. 56 MSc thesis

57 It is interesting to get a bit more insight in the distribution of the fatigue damage as function of wind speed. The contribution of each wind speed bin to the total amount of fatigue damage of the complete wind farm is shown in Figure 5.9. Figure 5.9: Contribution of each wind speed bin to the fatigue damage sustained by the complete wind farm. It is clearly visible that most fatigue damage is sustained at wind speeds higher than 15 m/s. This is typically the wind speed range where the turbines operate at nominal power. For this wind speeds the blade pitch angle is altered in order to keep the power output of the turbine constant. The reduced aerodynamic efficiency lowers the thrust force (which is shown in Figure 3.3), which has the consequence that the wind speed reduction and amount of added turbulence by the wind turbine decreases. As a result, for high wind speeds, the difference in wind conditions between the turbines in the farm is relatively small. Consequently, because for high wind speeds wind conditions are similar for all wind turbines in the farm, also the differences in fatigue loading between the turbines in the farm are small for high wind speeds. At low wind speeds (where wake effects play a significant role) the differences in wind conditions, and thus fatigue loading, between the turbines in the farm are considerable. Because the fatigue damage sustained at high wind speeds is considerably larger than the fatigue damage sustained at low wind speeds, the differences in fatigue damage between the turbines (calculated using all wind speeds) are quite modest, as was shown in Figure 5.7. The results shown here are calculated using a slope of the S-N curve m = 7. If an even higher value would be used the fatigue damage sustained at high wind speeds would be even more significant compared to the amount of fatigue damage for low wind speeds. This has the consequence that if the calculation is performed with a larger value for m, the differences in fatigue damage between the turbines in the farm are smaller compared to the results calculated with m = 7, as shown in Figure 5.7. MSc thesis 57

58 Edgewise blade root bending moment The fatigue damage (relative to turbine 1) for the edgewise blade root bending moment is shown in Figure 5.10 for the 25 turbines in the fictitious offshore wind farm. For this calculation the ambient turbulence intensity is specified as function of wind speed (see Figure 5.2). Figure 5.10: Fatigue damage (relative to turbine 1) for the blade root edgewise bending moment for the 25 turbines in the fictitious wind farm. The result of the calculation indicates that the difference between the heaviest and lightest loaded turbine in the farm is marginal (around 4%). This is a logical consequence of the fact that the main contributor to fatigue loading in the blade s edgewise direction is the gravity force acting on the blades. 58 MSc thesis

59 5.5.2 Tower In the following two subsections the fatigue damage for the tower is discussed for both for-aft and side direction. For the fatigue damage calculation of the tower a slope of S-N curve of m = 4 is used. Again it should be emphasized that the effects of waves are not taken into account. The presented results should be interpreted as the fatigue damage caused by aerodynamic effects Tower bottom for-aft bending moment The fatigue damage (relative to the fatigue damage of turbine 1) for tower bottom for-aft bending is shown in Figure Figure 5.11: Fatigue damage (relative to turbine 1) for the tower bottom for-aft bending moment for the turbines in the fictitious wind farm. It should be emphasized that effects of waves are not taken into account. The difference between the heaviest and lightest loaded turbines (turbine 18 and turbine 1 respectively) is around 12%. As mentioned before the effect of waves is not taken into account. Because wave conditions are generally equal for all turbines in the farm it is expected that if wave effects would be taken into account the difference in fatigue damage between the turbines would be smaller than the values shown in Figure This however, should be verified using either an aero-elastic simulation or using measurements from an offshore wind farm, where both wind and waves are monitored in combination with mechanical loads measurements in at least one on the turbines. MSc thesis 59

60 Tower bottom side bending moment The fatigue damage (relative to the fatigue damage of turbine 1) for tower bottom side bending is shown in Figure Figure 5.12: Fatigue damage (relative to turbine 1) for the tower bottom side bending moment for the turbines in the fictitious wind farm. It should be emphasized that effects of waves are not taken into account. The differences in fatigue damage for the tower bottom side moment are slightly smaller (maximum around 7%) compared to the differences for the tower bottom for-aft moment as shown in Similar as was concluded for the fatigue damage in for-aft direction, the differences shown in Figure 5.12 are expected to be smaller if the effects of waves would be taken into account. Note that this should be verified. 60 MSc thesis

61 5.5.3 Main bearing In the previous sections the results of the fatigue damage calculation for the blades and tower were shown. In the following sections the results of the fatigue damage calculation for the drive train are discussed, starting in this section with the main bearing. The method for calculating fatigue damage of the main bearing is described in section In Figure 5.13 the result of the calculation of fatigue damage of the main bearing is shown. Again the fatigue damage of the 25 turbines in the farm is specified relative to the fatigue damage of the main bearing of turbine 1. Figure 5.13: Fatigue damage (relative to turbine 1) for the main bearing for the turbines in the fictitious offshore wind farm. In contradiction to the fatigue damage calculation for the blades and tower, where turbine 1 was the heaviest loaded turbine, the fatigue damage of the main bearing of turbine 1 is larger than that of the other turbines in the farm. However, the differences (maximum around 2%) are negligible. In section 4.3 it was shown that the fatigue loading of the main bearing is solely a function of wind speed. The average wind speed experienced by the turbines in the middle of the farm is lower than the average wind speed turbine 1 experiences. This explains why the fatigue damage of the main bearing, sustained by the turbines in the middle of the farm, is slightly smaller compared to the fatigue damage sustained by turbine Gearbox bearing In section 4.4 it was shown that it is not possible to correlate the fatigue loading of the gearbox bearing with wind conditions measured at the nacelle anemometer. As a result it is also not possible to determine to quantify the fatigue damage of the gearbox bearing. Due to the fact that fatigue loading in wake conditions is larger (mainly due to the experienced rotor yaw moment) it is expected that differences in fatigue damage of the gearbox bearing exist between the turbines in the farm. Unfortunately, it is not possible to quantify the magnitude of the differences. MSc thesis 61

62 5.5.5 Gearbox In this section the results of the fatigue damage calculation of the gearbox are presented. The fatigue damage calculation is performed for both the high and low speed side of the gearbox. The method for the calculation was discussed in section In Figure 5.14 the fatigue damage (relative to turbine 1) of the gearbox for the 25 turbines in the fictitious offshore wind farm is shown. Figure 5.14: Fatigue damage (relative to turbine 1) of the gearbox for the 25 turbines in the fictitious offshore wind farm. The result of the calculation is shown for both the low and high speed side of the gearbox. Similar as was observed for the main bearing, the fatigue damage of the gearbox of turbine 1 is smaller compared to the other turbines in the farm. The difference in fatigue damage between the heaviest and lightest loaded turbine (turbine 1 and turbine 18 respectively) for both the high and low speed side of the gearbox is about 4%. In section 4.5 it was shown that the fatigue loading of the gearbox increases with both average and standard deviation of electrical power. The standard deviation of the electrical power was found to increase with turbulence. The average level of turbulence for the turbines in the middle of the farm is clearly larger compared to the average turbulence level experienced by turbine 1. However, the average power output of the turbines in the middle of the farm is obviously lower compared to the turbines on the edge of the farm (e.g. turbine 1). This effect clearly dominates the higher turbulence levels in the middle of the farm and therefore the results shown in Figure 5.14 are a logical consequence. 62 MSc thesis

63 5.6 Implementation in O&M cost estimator In the planning phase of an offshore wind farm a wind turbine aero-elastic simulation program (such as PHATAS or GH Bladed) can be used to establish relations between wind and waveconditions and fatigue loading of the wind turbines for various situations (e.g. free-stream, partial wake and full wake). Using an offshore wind climate the differences in fatigue damage between the turbines in the farm can be estimated. If the outcome of this calculations shows that significant differences exist between the turbines in the wind farm different failure rates can be assumed for the lighter and heavier loader turbines in the farm. At the time of the commissioning of an offshore wind farm the relation between wind and the characteristic fatigue loading parameters for the different components is still unknown. It is reasonable to expect that mechanical loading is measured at one or two wind turbines in the farm. During the first years of operation at these turbines data is gathered that can be used to establish relations between wind and wave conditions and fatigue loading (as is shown in chapter 4 for the onshore wind farm EWTW). Because the wind conditions are measured at all turbines (using the nacelle anemometer) and wave conditions on average are equal for all turbines, the determined relations can be used to estimate the experienced fatigue loading at the other turbines in the wind farm. Now the relative differences in fatigue damage between the turbines can be determined. These measured relative fatigue damage should be compared with the initial calculated relative fatigue damage and with the experienced maintenance need in order to judge whether the estimates of the failure rates in the O&M cost estimator should be adjusted. 5.7 Conclusions In this chapter fatigue damage has been calculated for a fictitious, but realistic, offshore wind farm. The main interest is the difference in fatigue damage between the turbines in the farm. The calculation is performed for the blades, tower, main bearing and gearbox of the wind turbines. The fictitious wind farm consists of 25 Nordex N80 turbines. A realistic offshore wind climate (which gives the probability of occurrence of wind speed and direction) has been specified using information from the ECN offshore wind atlas. The relation between ambient turbulence intensity and wind speed has been derived from measurements at the meteorological mast at the offshore wind farm Egmond aan Zee. ECN s wind resource program FluxFarm has been used to calculate the local wind conditions for every turbine in the farm as function of wind direction and wind speed. Using the relations between the characteristic fatigue loading parameters and wind conditions, which were specified in chapter 4, fatigue loading has been determined for every turbine as function of wind direction and wind speed. When combining this result with the wind climate, Miner s rule can be applied to calculate the fatigue damage of the turbines in the farm. For the blades a distinction is made between fatigue damage due to bending moment fluctuations in flapwise direction and bending moment fluctuations in edgewise direction. For the flapwise direction, the difference in fatigue damage between the lightest and heaviest loaded turbine is in the order of 10%. The outcome is found to strongly depend on the level of ambient turbulence intensity; for a low level of ambient turbulence the differences in fatigue damage is considerable, whereas in case of high ambient turbulence intensity the difference in fatigue damage between the turbines in the farm becomes negligible. Another conclusion from the calculation is that high wind speeds contribute most to fatigue damage. At high wind speeds wake effects are negligible, which explains why the differences in fatigue damage between the turbines in the farm are relatively modest. MSc thesis 63

64 The difference in fatigue damage for edgewise blade bending between the heaviest and lightest loaded turbine in the offshore wind farm is marginal. This is a logical result of the fact that gravity is the main cause for the load fluctuations in the blade s edgewise direction. The calculation of tower fatigue damage is performed for both for-aft and side direction. The difference in fatigue damage between the heaviest and lightest loaded turbine equals 12% in foraft direction and 7% for the side direction. Because effects of waves are not taken into account these numbers should be seen as the difference in fatigue damage due to aerodynamic effects. As the hydrodynamic forces (due to waves) are similar for all turbines in the farm, it is expected that the differences in fatigue damage, if the effect of waves would be included in the calculation, are smaller than the results presented in this thesis. This however, should be verified using either an aero-elastic simulation or using measurements in an offshore wind farm where both wind and wave conditions are monitored in combination with mechanical load measurements on one or more turbines. Turbines in the middle of the farm face slightly smaller fatigue damage of the main bearing compared to the turbines on the edge of the farm. This is a logical result of the fact that the fatigue loading of the main bearing was found to depend on wind speed. As the average wind speed in the middle of the farm is obviously lower compared to the edge of the farm also the fatigue loading of the turbines in the middle of the farm. However, the differences in fatigue damage of the main bearing between the turbines in the farm are negligible. Since no relation between wind conditions (measured at the nacelle anemometer) and fatigue loading of the gearbox bearing could be established, also the differences in gearbox bearing fatigue damage between the turbines in the offshore wind farm could not be quantified. As mentioned in chapter 4 it is expected that significant differences in fatigue damage of the gearbox bearing exist between the heavier and lighter loaded turbines in an offshore farm due to the fact that it was found that the fatigue loading between non-wake and wake conditions differs significantly (see Figure 4.15 in section 4.4). The turbines in the middle of the farm face slightly smaller fatigue damage of the gearbox compared to the turbines located at the edge of the farm. The differences are relatively marginal, with the difference in gearbox fatigue damage between the heaviest and lightest loaded turbine in the farm being equal to around 4%. Based on the results of the fatigue damage calculation it is not expected that the failure rates of the heavier and lighter loaded turbines significantly differ for blades and tower. These however, are so-called safe life components, which are designed for a lifetime of more than the lifetime of the turbine and generally do not fail (due to fatigue damage) in the regular lifetime of a wind turbine. Failures in these components are often caused by poor manufacturing, or by unforeseen conditions (such as lightning strikes). The calculated difference in fatigue damage between the heavier and lighter loaded turbines for the main bearing and gearbox are also too small to consider different failure rates. For the gearbox bearing significant differences in fatigue damage between the heavier and lighter turbines are expected, but more research is necessary in order to be able to quantify these differences to determine whether different failures rates should be considered for the gearbox bearing of the heavier and lighter loaded turbines in an offshore wind farm. 64 MSc thesis

65 6. Analysis maintenance reports In the previous chapters the determination of fatigue damage in an offshore wind farm was discussed, which was the main goal of the first part of this thesis. In this chapter the second part of the thesis is presented, which consists of an investigation of the possibility of using the information from maintenance reports of an (offshore) wind farm to make accurate estimates of the failure rates of the various wind turbine systems and components. 6.1 Introduction In order to make reliable predictions of the costs of operation & maintenance (O&M) of an (offshore) wind farm it is essential to have accurate estimates of the failure rates of the various wind turbine components. These failure rates can be derived from sources that publish generic reliability numbers, which are usually derived from operational experience of a large group of wind turbines. Unfortunately often essential information (i.e. type, size and age of the turbines) is not available. In addition to this the numbers from the different sources show large differences. This indicates that generic information is not very suitable to make accurate predictions of the failure behaviour of a specific wind farm. In this part of the thesis a method is presented which enables a wind farm operator to use the operational experience of its wind farm in order to make accurate predictions of the (future) failure rates of the turbines in the farm. Wind farm operators receive a maintenance report, which contains details about the performed maintenance action, for each maintenance action on the turbines in the farm. These paper and often hand-written reports are usually collected and stored by the wind farm operator. For a small wind farm the operator might have a decent knowledge about the failure behaviour of the turbines in the farm. However for offshore wind farms, which usually contain a large number of turbines, it would be almost impossible for the wind farm operator to have detailed knowledge about the failure behaviour of the farm. In order to extract useful information from the maintenance reports they will have to be digitalized. For this purpose ECN has developed a database in which the information from the paper service reports can be entered in a structured manner. Using the digitalized database numerical information on the failure behaviour of the wind farm can be extracted. The database can be used to: Generate reliability numbers of the different turbine systems and components; To compare the actual numbers with previously used failure rates in the O&M cost estimator; in particular in the planning phase more or less generic figures are used to assess the future O&M costs of the wind farm; For updating the previously used (generic) failure rates and thereby updating the estimate of future O&M costs; To identify possible weak spots in the design; In order to evaluate the effectiveness of the used maintenance procedure; Compare the O&M need of the different turbines in the farm; To make a straightforward comparison of the performance of the turbines in the farm; To get an insight in the influence of wind farm layout, turbine spacing and wake effects on the failure behaviour of turbines; It is interesting to see if the observed failure behaviour is in accordance with the mechanical loading (as determined in part A of this thesis); Obtain better understanding of failure mechanisms and causes; Feedback to the maintenance planning. MSc thesis 65

66 In this part of the thesis the applicability of processing the paper service reports for extracting reliability numbers is studied. For this purpose maintenance reports from a wind farm consisting of nine modern turbines (megawatt class) are used. The full analysis of the maintenance reports is reported in [25]. In this thesis the main focus lies on the method used for extracting information on failure rates from the maintenance reports. This chapter starts with some brief theory on reliability engineering (section 6.2). Also some information on generic reliability data is given (section 6.3), which are normally used to define initial estimates of the failure rates of the various wind turbine components. After that information is provided on the database, which is used for storing the digitalized information from the maintenance reports (section 6.4). The second part of the chapter describes the actual analyses of the maintenance reports. First an overview of the available maintenance data is given (section 6.5). The results of the analysis of the maintenance reports are presented in section 6.6. The chapter is concluded (section 6.7) with a discussion on how the results from the analysis should be used for updating the initial estimates of the failure rates in the O&M cost estimator. 6.2 Theory on reliability engineering A wind turbine generally is a repairable system; an occurrence of a failure does not mean the end of life of the system. After repairing or replacing the failed element the system is back at its operating state. The following subsections will discuss some basic reliability engineering methods for repairable systems Reliability analysis under constant failure rate For a repairable system the elapsed times at which failures occur can be observed, defining: [26] y i = system age at the ith failure t i = interval between (i-1)th and ith failure With the following two initial simplifying assumptions: 1) The repairs that take place at each y i are perfect, so that the repaired item is in an as good as new condition. 2) The failures occur random, but at a constant underlying failure rate. For a system for which these two assumptions are valid a plot of the cumulative number of failures N(t) against cumulative operation time T the expected relationship would be a straight line. The slope of this line is called the failure rate and its reciprocal the mean time between failures (MTBF). The assumption of constant failure rate makes it possible to simply aggregate system life t over any number of systems irrespective of system age to give a total elapsed time T. If in this elapsed time T an x amount of failures is observed then the estimated failure rate λ and mean time between failure θ equal: λ = x T T θ = x (6.1) (6.2) When performing reliability analysis it is common practice to specify a confidence limit on the estimate of, in this case, failure rates. For a certain confidence level (1-α) the upper λ U and lower λ L limits of the estimated failure rate can be calculated using equations 6.3 and 6.4 respectively, 66 MSc thesis

67 2 χ α,2 x λ U = (6.3) 2T λ L 2 χ ( 1 α ),2 x = (6.4) 2T where x represents the cumulative number of failures, T the cumulative number of operation time, the χ 2 symbol represents a chi-square distribution with a (1-α) confidence The assumption of a constant failure rate If the assumption is made that the repairs or replacements result in an as good as new component the failure rate of the component should be more or less constant in time. This is generally the case for components in an electronic system, but for mechanical systems rather the exception. Basically two exceptions from the assumption of an as good as new replacement of a failed component are possible: 1) The replacement component is superior to the failed item due to engineering development. This would result in a longer expected life time and thus a decreasing failure rate. 2) On the other hand; the replacement item can also be worse than the failed item. This is usually caused by an imperfect repair. For this case the expected lifetime would be lower than the original part and as a consequence failure rate would increase. If the system consists of numerous components without a dominant failure mode, the mixing effect of failures and replacements usually results in a more or less constant overall failure rate. This is sometimes referred to as pseudo constant failure rate to distinguish it from the situation resulting from constant hazard (probability of failure is independent of age) components Conclusions Most likely the observed failure rates of the different wind turbine components are not constant in time. In order to make an estimate of the failure rate for the next coming years a period has to be selected during which the failure rate is more or less constant in time. Over this period the mean failure rate can be determined using equation 6.1 and the upper and lower confidence limits of the failure rate can be determined using equations 6.3 and 6.4 respectively. MSc thesis 67

68 6.3 Generic reliability figures In order to get an initial estimate of the failure rate of the various wind turbine components several sources can be consulted [27],[28],[29]. For the DOWEC concept study [30] information from the following sources is studied: 1) WindStats newsletter: Production and failure data of wind turbines in both Germany (2750 turbines) and Denmark (2000 turbines) are published monthly. The reliability numbers are specified per component, but not per turbine type or size. In addition to this no information on the age of the turbines is provided. 2) WMEP: In Germany the ISET collects failure data of 1435 turbines specified per turbine power class. However, the main part of this data (95%) comes from small turbines (< 560 kw). No information is provided on turbine age. 3) Landwirtschaftskammer Schlegwig-Holstein: Failure data from all 510 turbines in the German province Schleswig-Holstein are presented every year. Data is provided per turbine size and type. No information on the age of the turbines is specified. In Figure 6.1 the failure rates retrieved from the three sources is shown for the years 1998 till Figure 6.1: Comparison failures rates taken from different sources [30]. When taking all power classes into account the yearly failure rate varies between 0.6 and 5.4 failures per turbine per year. When taking only turbines with a rated power of larger than 500 kw into account the failure rate mentioned in the three sources varies between 1.9 and 6.4. These results indicate that it is nearly impossible to make a proper judgement of an average failure rate due to the large differences observed in the data of the three sources. The DOWEC concept study also shows that the failure rates of the various turbine components retrieved from the three sources also show a large amount of scatter. 68 MSc thesis

69 In the DOWEC concept study [30] estimates have been made of the failure rates of the various components of a state-of-the-art onshore turbine. Assuming a price increase in order to increase the reliability of certain components the failure rates for the offshore DOWEC turbine were estimated. The estimated values for both the state-of-the-art onshore and the DOWEC turbine are shown in Table 6-1. Table 6-1: Estimated failure rates of the various components of the DOWEC turbine [30]. Component Onshore failure rate [year -1 ] Price increase for reliability increase [%] Offshore failure rate[year -1 ] Shaft & bearing Brake Generator Parking brake Electric Blade Yaw system Blade tip Pitch mechanism Gearbox Inverter Control Total In this thesis the values for the state-of-the-art onshore turbine will be used as reference in order to demonstrate how the operational data of the wind farm can be used to determine whether this reference values (used as initial estimates of the failure rates) are in accordance with the experienced maintenance need. 6.4 Database for failure collection The maintenance and service reports of the wind farm are collected and stored by the wind farm operator. To extract useful information these reports have been digitalized. ECN has developed a database [31] for digitalizing the service reports in such a manner that the failure and maintenance data can be analysed numerically. The database is implemented in Microsoft Access and can be divided in a definition and registration part Definition part To collect the data in a structured manner a breakdown of the wind farm is made. The breakdown is shown in a schematic way below: Wind farm Wind Turbine Main Systems Components Failure Modes The wind farm consists of several wind turbines. Each wind turbine is divided into several systems, which consequently are made up of several components. For each component the possible failure modes are defined. The breakdown of the turbines is made using information from both the technical data and the O&M specification sheets. The complete breakdown is shown in appendix B. MSc thesis 69

70 6.4.2 Registration part The information from the paper service and maintenance reports is entered in a predefined format which uses pull-down menus containing pre-defined answers, which are based on the breakdown of the wind farm. All failure information is stored in a table, which for each recorded failure contains: Wind farm Wind turbine System name Component name Failure mode Service report number Start date and time Stop date and time Repair action Description failure (free-format) 6.5 Overview available data In this part of the thesis maintenance reports of a wind farm consisting of nine modern (megawatt class) turbine are analysed. In this section an overview of the available data from the wind farm is given. Maintenance reports for three years since the commissioning of the wind farm are available from all nine turbines. Maintenance reports of both preventive and corrective maintenance actions are collected. The reports contain information on the turbine involved, the date and time of the start and end of the maintenance action, a description of the maintenance action and, if applicable, information on replaced components. Besides the maintenance reports some additional information is available: Technical description and specifications of the turbines Operation and maintenance specifications sheet Certificate of manufacturer for each turbine Using this information it is found that the nine turbines do not have identical gearboxes and generators. It is found that two types of gearboxes and two types of generators are used in the wind turbines. In Table 6-2 the type of gearbox and generator is shown for each of the nine turbines. Table 6-2: Specification of the type of gearbox and generator for each of the nine turbines. Turbine Gearbox type Generator type 1 Gearbox A Generator A 2 Gearbox A Generator B 3 Gearbox A Generator B 4 Gearbox B Generator A 5 Gearbox A Generator A 6 Gearbox A Generator A 7 Gearbox B Generator A 8 Gearbox B Generator B 9 Gearbox B Generator A 70 MSc thesis

71 6.6 Results analysis After digitalizing all maintenance and service reports the data need to be analysed. In this study the distribution of failures in the wind farm over the different turbines, systems and components is investigated. This gives the wind farm operator the possibility to easily identify possible bottle-neck turbines, systems or components. The second, and most important part of the study, is the investigation of the occurrence of failures in time. The results of this analysis can be used by the wind farm operator to estimate the failure rate with which the operator can estimate the future operation and maintenance needs and costs of his wind farm. For the purpose of the analysis of the distribution of failures and failure rates a post-processor has been designed in MATLAB. Using the stored failures and the breakdown of the wind farm the post-processor plots a distribution of the failures per turbine, system and/or component. In addition to this failure rates can be studied per turbine, system and/or component. The results of the analysis are shown in the following subsections Distribution failures The pie chart shown in Figure 6.2 shows the distribution of the total number of failures per turbine. Failures from all systems and all components are taken into account. A total of 208 failures have been recorded. Figure 6.2: Distribution of total number of recorded failures per turbine. It can be observed that the failures are distributed quite evenly over the nine turbines. Turbines 1, 5 and 6 contribute most at 13% and turbine 9 experienced the least amount of failures (8%). The pie chart in Figure 6.3 shows the distribution of the total number of failures per main system. MSc thesis 71

72 Figure 6.3: Distribution of the total number of recorded failures per turbine system. It can easily be seen that gearbox-related failures have by far the largest share in the total amount of failures. Each of every other system contributes less than 10% to the total amount of failures. Most noticeable are brake-related and pitch-related failures. Also failures related to the PC cabinet and low voltage main distribution have a contribution of larger than 5% to the total number of failures. As mentioned in each turbine system is split up in several components. In this section only the distribution of failures per component is shown for the gearbox system. Figure 6.4: Distribution of total number of recorded gearbox failures per component. Almost 80% of the maintenance actions on the gearbox are due to polluted oil filters. Second largest contributor is the oil of the gearbox; five turbines have had their gearbox oil replaced once and in addition to this gearbox oil has also been refilled a few times. Further, 5% of the failures are related to the oil cooler and one turbine has undergone a complete gearbox replacement. As described in section 6.5 turbines 4, 7, 8 and 9 have a different gearbox compared to the other turbines in the farm. It is interesting to study whether this difference in gearbox has an effect on the number of gearbox oil filter failures per turbine. This is shown in Figure MSc thesis

73 Figure 6.5: Number of gearbox oil filter failures per turbine. It can immediately be seen that the turbines fitted with gearbox A (turbines 1, 2, 3, 5 and 6) have suffered from a considerable amount of gearbox oil filter failures (average equals 10.4 failures per turbine for the three years of operation). The turbines fitted with gearbox B have suffered noticeably less gearbox oil filter failures (average of 4.25 failures per turbine for the three years of operation). The difference in number of failures per gearbox is considerable. When in a large offshore wind farm multiple types of gearboxes are used it could be advisable to have different estimates of the failure rates for each group of turbines equipped with a specific gearbox. Of course the same applies for other components Failure rates The distribution of the failures gives insight in which turbines and systems have suffered most failures. It was found that most failures where gearbox-related; in particular polluted oil filters are by far the largest contributor to the total number of failures. In this section the results of the study of the occurrence of failures in time is presented. This failure rate gives a good indication on whether the time between failures is constant, increases or even decreases. The information from the analysis can be used by the wind farm operator to estimate the future maintenance needs and costs of the wind farm. To keep this report at a reasonable size only the total failure rate and the failure rate of the gearbox and pitch mechanism are discussed in this section. For each of these systems it is also tried to give an example on how the wind farm operator could use the data from the analysis to estimate the future maintenance needs of the wind farm. The future estimates of the failure rates are compared with the estimated failure rates from the DOWEC concept study (see Table 6-3 in section 6.1) Combined failures First the failure rate of all combined failures is studied; so no distinction between individual turbines or systems is made. This is shown in Figure 6.6, where the cumulative number of failures is plotted as function of the cumulative operation time. The cumulative operation time is defined as the cumulative (of the nine turbines) elapsed time since the commissioning of the wind farm. The coloured lines give an indication of the failure rate averaged over 1 year. MSc thesis 73

74 Figure 6.6: Cumulative number of recorded failures as function of cumulative operation time of the wind turbines. The black circles indicate a recorded failure and the coloured lines give an indication of the yearly failure rate. The observed failure rate of all combined failures is more or less constant. In the second year a slight increase of the failure rate can be observed, which, as was reported in [cc], is mainly due to a large number of manual brake releases after about 5500 cumulative operation days. The failure rate in the third year is more or less equal to the first year. In Table 6-3 the yearly average failure rates are summarized. Table 6-3: Yearly averages of the overall failure rate. Year Failure rate / year / turbine / year / turbine / year / turbine The overall failure rate shows no clear sign of increasing of decreasing. Therefore, on the basis on this data, the future estimate of the overall failure rate for the next coming years is best determined over the period of year 3 (6570 till 9855 days of cumulative operation time). The estimated failure rate over this period (including the 90% upper and lower confidence limits, which are calculated using equations 6.3 and 6.4) is shown in Table 6-4. Table 6-4: Future estimate of overall failure rate including 90% confidence limits. Failure rate Upper confidence limit 6.67 / year / turbine Estimated mean 5.67 / year / turbine Lower confidence limit 4.72 / year / turbine 74 MSc thesis

75 The DOWEC concept study estimated an overall failure rate of 2.31 failures per turbine per year for a state-of-the-art onshore turbine (see Table 6-1). This value is a factor two smaller than the lower confidence limit (90%) of the future estimate of the overall failure rate Gearbox oil filter The observation of a more or less constant failure rate over time when combining all failures for all components can more or less be expected (see section 0). It is more interesting to look at the failure rate of individual components. As mentioned before the nine turbines have suffered frequently from polluted oil filters. In Figure 6.7 and Figure 6.8 the cumulative number of oil filter failures is shown as function of cumulative operation time for the group of turbines with gearbox A and gearbox B respectively. Again the lines give an indication of the failure rate averaged over one year. Figure 6.7: Cumulative number of recorded gearbox oil filter failures as function of cumulative operation time of the turbines with gearbox A. The black circles indicate a recorded failure and the coloured lines give an indication of the yearly failure rate. After having a first look at the graph it can immediately be observed that the failure rate of the oil filters is definitely not constant in time. For the first 1000 days of cumulative operation days only two oil filter related corrective maintenance actions are performed (the oil filter replacements after about 250 days are most likely scheduled action because they occur on the exact same date), whereas after 1200 days, the failure rate of the oil filter increases significantly. The maintenance reports show that six turbines have had an oil change after about 2500 days of cumulative operation time. The effect of this oil change is clearly visible in the failure rate analysis; after 2500 days the failure rate clearly is lower compared to the time period before the oil change. After the oil change the failure rate of the oil filters has remained fairly constant. The failure rate analysis for the turbines with gearbox B shows a completely different picture. Similar to the turbines with gearbox A the failure rate in the first year is very low. After 1500 MSc thesis 75

76 cumulative operation days a sharp increase in failure rate is observed. After the oil change at about 2000 days only 4 oil filter failures have occurred. Figure 6.8: Cumulative number of recorded gearbox oil filter failures as function of cumulative operation time of the wind farm for the turbines with gearbox B. The black circles indicate a recorded failure and the coloured lines give an indication of the yearly failure rate. In Table 6-5 the yearly average failure rates are summarized for both the group of turbines with a gearbox A and the group of turbines with gearbox B. Table 6-5: Yearly averages of the gearbox oil filter failure rate. Year Failure rate (gearbox A) Failure rate (gearbox B) / year / turbine 0.80 / year / turbine / year / turbine 5.46 / year / turbine / year / turbine 1.05 / year / turbine The analysis shows that after a relatively trouble-free period the failure rate for both types of gearbox increases significantly at the start of the second year. After the replacement of the gearbox oil the failure rate decreases slightly for the turbines with gearbox A and significantly for the turbines with gearbox B. For the estimation of the future amount of oil filter failures for the turbines with a gearbox A the failure rate of year 3 can be used. The failure rate in this year shows no clear increase or decrease and without future oil changes or gearbox changes it is expected that the failure rate of the oil filter will not change significantly. For turbines with gearbox B the failure rate averaged over the period after the gearbox oil change (2000 days of cumulative operation) should be used as estimate for the future rate of oil filter failures. 76 MSc thesis

77 The estimated failure rate over the specified periods (including the 90% upper and lower confidence limits, which are calculated using equation 6.3 and 6.4) are shown in Table 6-6. Table 6-6: Future estimate of the failure rate of the gearbox oil filter for both the group of turbines with gearbox A and the group of turbines with gearbox B. The 90% confidence limits are also specified. Failure rate (gearbox A) Failure rate (gearbox B) Upper confidence limit 4.19 / year / turbine 1.52 / year / turbine Estimated mean 3.17 / year / turbine 0.91 / year / turbine Lower confidence limit 2.23 / year / turbine 0.40 / year / turbine For the DOWEC concept study the estimated failure rate of the gearbox of a state-of-the-art onshore turbine equals 0.41 failures per turbine per year (see Table 6-1). This value lies just above the lower confidence limit (90%) of the future failure rate for the turbines with gearbox B. The future estimate of the failure rate for the group of turbines with gearbox A is a staggering 7.7 times as large as the value used in the DOWEC study. For an offshore wind farm this failure rate is too large for economic exploitation of the wind farm Pitch mechanism The same failure rate analysis is performed for the pitch mechanism. The graph in Figure 6.9 shows the cumulative amount of failures related to the pitch mechanism as function of operation time. Failures of all nine turbines are taken into account. Figure 6.9: Cumulative number of recorded pitch-mechanism failures as function of cumulative operation time of the wind turbines. The black circles indicate a recorded failure and the coloured lines give an indication of the yearly failure rate. During the first year a quite constant failure rate for the pitch mechanism can be observed. Consequently, during the second year only two failures occur. This trend does not continue in the third year, where the failure rate increases again. Especially after 8000 days of cumulative operation the failure rate seems to increase. MSc thesis 77

78 In Table 6-7 the yearly average failure rates are summarized for the pitch mechanism. Table 6-7: Yearly averages of the pitch mechanism failure rate. Year Failure rate / year / turbine / year / turbine / year / turbine Based on the available information the failure rate after 6000 days of cumulative operation significantly differs from the period days of cumulative of operation. Therefore the failure rate averaged over the period after 6000 days of cumulative days of operation would be most suited as estimate for the future maintenance need of the pitch mechanism. It should be noted that the failure rate seems to increase during the third year, so if this trend continues, the number of failures could possibly be underestimated. The estimated failure rate over this period (including the 90% upper and lower confidence limits, which are calculated using equation 6.3 and 6.4) is shown in Table 6-8. Table 6-8: Future estimate of the failure rate of the pitch mechanism. The 90% confidence limits are also specified. Failure rate Upper confidence limit 1.29 / year / turbine Estimated mean 0.91 / year / turbine Lower confidence limit 0.57 / year / turbine For the DOWEC concept study the estimated failure rate of the pitch mechanism of a state-ofthe-art onshore turbine equals 0.23 failures per turbine per year. This value clearly lies below the lower confidence limit (90%) of the future failure rate shown in Table Implementation in the O&M cost estimator The results, as shown in the previous sections, demonstrate that processing and analyzing the data from the maintenance reports is useful for estimating the failure rates of the different systems and components of the turbine. The O&M cost estimator contains initial estimates of the failure rate of each wind turbine component. From the commissioning of the wind farm maintenance data is gathered. After a few years (typically between three and five years) the warranty period of the wind farm expires. At this point it is necessary to make an accurate estimation of the future O&M costs and therefore it is essential to, if necessary, update the initial assumptions of the failure rates of the various turbine components. The gathered operational data should be used to judge whether the initial assumed failure rates should be accepted or rejected. The results of the analysis show that the failure rates are typically not constant in time. This makes it impossible to develop a fully automated algorithm that decides whether the initial estimate of the failures rates is in accordance with the experienced failure rates. Some manual interaction by the wind farm operator is necessary. The wind farm operator should use the graphs of the cumulative number of failures as function of cumulative operation time (see section 6.6.2) to judge over which time period the experienced failure rate should be determined. Applying a statistical analysis on the data from the selected time period gives the experienced failure rate with a certain confidence interval. If the initial estimate lies between 78 MSc thesis

79 the confidence limits it should be accepted, whereas the experienced failure rate should be used when the initial estimate lies outside the confidence limits. 6.8 Conclusions In order to make reliable predictions of the cost of operations and maintenance (O&M) of an (offshore) wind farm it is essential to have accurate estimates of the failure rates of the different wind turbine components. Estimates of the failure rates can be retrieved from sources that publish generic reliability numbers. However, generic reliability figures from various available sources show a large amount of scatter. Failure rates taken from four different sources vary between 0.6 and 5.4 failures per turbine per year. This implies that these generic values are not suited for a reliable assessment of the maintenance need for one specific wind farm. In this part of the thesis a method is presented that enables the wind farm operator to use operational experience of his own wind farm to make accurate estimates of the failure rates of the turbines in his farm. The method is demonstrated using maintenance data (in the form of paper maintenance reports) from an onshore wind farm consisting of nine modern (megawatt-class) turbines. The information from the paper maintenance reports has been digitalized in a database for failure data collection, which has been developed by ECN. This database has proven a useful tool to store the maintenance data in a structured manner. A postprocessor has been developed in order to be able to analyse the data stored in the database. The analysis shows that more than 40% of the recorded failures in the wind farm are gearbox related. An important result from the performed analysis of the maintenance reports is the fact that the number of gearbox-related failures differs strongly for the two types of gearboxes used in the wind farm. When in a large offshore wind farm multiple types of gearboxes are used it is advisable to have different estimates of the failure rates for each group of turbines containing a specific gearbox. Naturally, the same naturally applies for other components. The analysis of the occurrence of failures in time shows that the overall failure rate (taking all turbines, systems and components into account) is fairly constant. However, when looking at system level non-constant failure rates are observed. For instance the failure rate of the oil filter shows a sharp increase during the second year of operation, but after a gearbox change and/or oil change the failure rate clearly decreases again. Similar observations are made for the other turbine systems. Using the analysis of the occurrence of failures in time future estimates of the failure rates of the various wind turbine components are determined. The comparison of these values with the estimates for the DOWEC concept study shows that large differences exist. This implies that using operational data in order to estimate the future maintenance needs and costs of an (offshore) wind farm results in a much more accurate estimate of O&M costs compared to the situation where generic numbers are used. MSc thesis 79

80 7. Analysis SCADA data The previous chapters discussed how operational experience (mechanical load measurements and maintenance data) can be used to accurately estimate the future failure behaviour of the turbines in a wind farm. In this chapter the third and final part of this thesis is discussed. In this part of the thesis it is investigated whether the collected SCADA data from turbines in an (offshore) wind farm can be used in order to detect imminent failures of a wind turbine. 7.1 Introduction Besides the collection of maintenance reports, wind farm operators often also have data from the turbine SCADA systems at their disposal. The data from the SCADA data acquisition system usually contains information of the measured temperatures on several of the main components (e.g. main bearing, main shaft, gearbox and generator) of a wind turbine. The previous chapter shows that analysing the maintenance reports can provide useful information to the wind farm operator on the medium to long-term maintenance need of the wind farm. In this chapter it is investigated whether the SCADA data can be used to get information on the status and the condition of the wind farm and possibly whether upcoming failures can be detected in advance by trends in the SCADA data. If the feasibility and reliability of this method is proven it can be used for optimizing the operation and maintenance on the medium to shortterm period. For a large offshore wind farm the collected 10-minute average values of up to 80 SCADA signals from, say 100 turbines, for several years of operation represent an enormous amount of data, which is unsuitable for straightforward analysis. In addition to the problem of analyzing this enormous amount of data, the measured temperatures at the components are found to strongly depend on outside temperature and turbine power. This makes a proper detection of irregular behaviour even more complicated. For this part of this thesis again use is made of data from a wind farm consisting of nine modern (megawatt class) wind turbines. First the relevant SCADA signals that are suitable for analysis are selected. The second step is the normalization of the SCADA temperature signals according to outside temperature and turbine power. This makes it possible to better judge whether an observed temperature on a certain component is irregular or not. The process of normalization of the SCADA signals is described in section 7.4. After this it is possible to investigate whether irregular behaviour of the turbine (indicated by the normalized SCADA temperature signals) can be linked with the recorded failures of the wind farm. The results of this analysis are presented in section 7.5. Finally in section 7.6 a format is presented, which gives a clear and accessible overview of the status of the turbines in the wind farm (based on the SCADA information) on a monthly basis. The reader is informed that the analyses of the wind farm under investigation are reported in a separate confidential report issued to the wind farm operator. 7.2 Overview available data For all nine turbines 27 months of SCADA data are available. The data consist of 10 minute average values. In total data from 70 channels are available, but after a first analysis of the available data it is found that not all 70 signals contain valid data. All 70 signals are shown in Appendix H, where the valid signals are shown in black and the invalid signals in red. 80 MSc thesis

81 7.3 Signal selection For the analysis a selection is made from the available SCADA signals (see Appendix H). The only suitable signals for the analysis are the temperatures measured at the main bearing, gearbox and generator. The other signals are either not interesting (e.g. nacelle temperature) or unavailable (e.g. gearbox oil pressure). The signals used for the analysis are: Generator temperature 1 Generator temperature 2 Generator bearing temperature A Generator bearing temperature B Generator cooling air temperature Gearbox temperature Gearbox bearing temperature Main bearing temperature 7.4 Normalization In a previous study [32] it was shown that the measured temperatures on the components generally tend to depend on outside temperature and turbine power and therefore consequently the measured temperatures on the components generally have a large range of values. In order to use the SCADA data for the monitoring of the condition of the wind turbine it is necessary to confine the data to smaller bandwidths by eliminating the effect of outside temperature and turbine power on the temperature of the components. The method of normalization of the SCADA data is the following: 1. Calculate the binned average of the SCADA signal according ambient temperature and turbine power for each turbine; 2. Determine the median value over all turbines for each bin; 3. Fit a polynomial plane to the median-bin-averaged data; 4. The normalization function (as function of ambient temperature and turbine power) is used to normalize the 10-minute averaged data. After a first analysis it is observed that the generator temperature, generator bearing temperature and generator cooling air temperature measured at turbines 2, 3 and 8 differ significantly compared to the other turbines. This is most likely caused by the fact that these turbines have a different generator than the other six turbines (see section 6.5). Therefore for these SCADA signals two normalization functions are determined; one for turbine 2, 3 and 8 and one for the remaining six turbines. In addition to this the main bearing temperature measured at turbines 5 and 8 deviates significantly from the other turbines. The available information from the technical data sheet does not suggest that these turbines have a different main bearing and therefore it is decided to use one normalization function for all nine turbines for this SCADA signal. The information on the technical data sheets also indicates that turbine 4, 7, 8 and 9 have a different gearbox compared to the other six turbines. However in the initial SCADA analysis no significant difference in measured gearbox temperature was found between these two groups of turbines. As a result it is decided to use one normalization function for all nine turbines for the gearbox related signals. The normalization of the SCADA data is shown in Appendix I, where for each relevant SCADA signal one figure is shown. The upper graph shows the dependency of the temperature of the SCADA signal on ambient temperature and turbine power. The plane(s) in the same graph indicate the function that is used for normalizing the data. The middle graph shows the measured value of the SCADA signal in time without processing and the lower graph indicates the value of the SCADA data normalized with the normalization function depending on ambient temperature and turbine power. MSc thesis 81

82 7.5 Failure detection The normalization of the SCADA data offers a better opportunity (compared to the original signal) to observe possible irregular behaviour of the turbines. In this section it is investigated whether the recorded failures can be linked with possible irregular behaviour of the turbine found using the SCADA data. The 10-minute average values of the SCADA data represent a very large amount of data, which is not very suitable for easy interpretation. Therefore daily averages of the relevant normalized SCADA signals are calculated. Using this method trends should still be visible, while the amount of data is significantly reduced. In the previous chapter it was shown that a large share of the turbine failures are caused by polluted oil filters. It is expected that a polluted oil filter leads to an increase in oil pressure. Therefore this SCADA signal would be the most interesting to investigate whether the SCADA data indicate a rising oil pressure in the period before the oil filter pollutes. Unfortunately the oil pressure SCADA signal does not contain valid data (see Appendix H). For that reason here it is tried to see whether the gearbox and gearbox bearing temperature can also be used to detect an upcoming polluted oil filter. As mentioned in section 7.3 besides the temperatures measured on the gearbox the only relevant available measurements are the temperatures on the main bearing and generator. Neither of these two components has experienced significant failures during the time period for which the SCADA data are available. Therefore it is decided to only investigate whether the gearboxrelated failures can be predicted by abnormal behaviour prior to the occurrence of the failure. The analysis shows that no clear relation can be found between the oil filter replacements and a clear abnormal behaviour in the gearbox and gearbox bearing temperature. Therefore in this section only the results for turbine 1, 5 and 7 are presented. For these turbines some interesting observations are made. The results for the other turbines are presented in appendix D. In the following figures the daily averages of the normalized gearbox and gearbox bearing temperature are plotted for the complete time period for which the signal is available. The oil filter replacements are indicated by the red crosses. 82 MSc thesis

83 Figure 7.1: Analysis SCADA data turbine 1, where the blue dots indicate the daily averages of the normalized SCADA signals of gearbox and gearbox bearing temperature and the red crosses represent the gearbox oil filter replacements. Looking at the analysis for turbine 1 (see Figure 7.1) it can be seen that at the end of 2004 en the beginning of 2005 six oil filter replacements have been made. The daily averages of the normalized data in this period show quite a large amount scatter, but no clear rising trend in the temperatures is found in the period up to the oil filter replacements. After the last oil filter change the normalized data show no decrease of the amount of scatter. As described in section turbine 1 has undergone a gearbox replacement in the autumn of This is clearly visible in the graph by the period without data. After the gearbox change the normalized gearbox temperature shows very little scatter compared to the period before the gearbox change. For turbine 5 (see Figure 7.2) in the beginning of 2006 a sharp increase of the gearbox temperature is observed. After an oil filter replacement the temperature is back to a normal level. However, three more oil filter replacements are made during the next months but no clear increase in gearbox temperature is observed. On the other hand the SCADA data of turbine 7 (see Figure 7.3) show a clear increase of the gearbox temperature in the second half of 2005 but in this period no oil filters have been replaced. MSc thesis 83

84 Figure 7.2: Analysis SCADA data turbine 5, where the blue dots indicate the daily averages of the normalized SCADA signals of gearbox and gearbox bearing temperature and the red crosses represent the gearbox oil filter replacements. Figure 7.3: Analysis SCADA data turbine 7, where the blue dots indicate the daily averages of the normalized SCADA signals of gearbox and gearbox bearing temperature and the red crosses represent the gearbox oil filter replacements. 84 MSc thesis

85 The analysis of SCADA data indicates that the upcoming gearbox oil filter failures cannot be detected beforehand in a reliable way. In some cases trends do change after repairs or replacements, but no robust conclusions can be derived. However, because the most appropriate SCADA signal (gearbox oil pressure) is not available it cannot be concluded that it is impossible to detect upcoming gearbox oil filter failures. In addition to this it is not possible to investigate whether SCADA data could be used to detect failures in the main bearing and generator for the simple fact that no serious failures occurred in these systems. 7.6 Implementation in the O&M cost estimator As mentioned before, the information from the SCADA data is more suited for the prediction of failures on the short-term period, whereas the information from the analysis of fatigue damage and maintenance reports can be used to estimate long-term failure behaviour. The analysis of SCADA data should be seen as a sidetrack in the O&M cost estimator, which the wind farm operator could possibly use to judge whether it is necessary to send a maintenance crew for inspection or replacement of a component for which irregular behaviour is observed. If a wind farm operator is going to use SCADA data for analyzing the status and condition of his wind farm it is necessary to present this information in a structured and accessible way. In this section a format is presented, which the wind farm operator could use to analyze the state of his wind farm on a monthly basis. The format consists of a monthly overview of the relevant normalized SCADA signals, which are determined using the method described in section 7.4. The monthly reports could be enhanced by also presenting an overview of the occurred alarms, but because these data are not available this is not presented in this section. An example of a monthly report (turbine 5 in April 2006) is presented in Figure 7.4 and Figure 7.5. In the left graph the daily averages for the three preceding months are shown to get an impression of the general trend of the normalized signal. The right graph shows the normalized 10- minute averages (green dots) and the daily averages (blue dots) of the SCADA signal. Finally in both graphs a polynomial fit through the daily averages of the four months is shown. MSc thesis 85

86 Figure 7.4: Example of a monthly report of the SCADA data. In the left graph the daily averages for the three preceding months are shown to get an impression of the general trend of the normalized signal. The right graph shows the normalized 10- minute averages (green dots) and the daily averages (blue dots) of the normalized SCADA signal. Finally in both graphs a polynomial fit through the daily averages of the four months is shown. 86 MSc thesis

87 Figure 7.5: Continuation of the monthly report (see Figure 4-12). MSc thesis 87

88 7.7 Conclusions Wind farm owners usually have data from the turbine SCADA system at their disposal. In this part of the thesis it has been investigated whether this data can be used by the wind farm operator in order to assess the health of the turbines in his farm and whether the SCADA data can be used for early fault detection. The investigation is performed using data from a wind farm consisting of nine modern (megawatt-class) turbines. The data from the SCADA system contains, amongst others, information of the measured temperatures on several of the main components. It is found that the measured temperatures tend to depend on both ambient temperature and turbine power. As a result, the temperatures of the main components have a large range of values, which means that it is nearly impossible to detect irregular behaviour of a turbine in a reliable manner. To reduce the scatter of the values the SCADA data have been normalized according to ambient temperature and turbine power. This makes is easier to identify irregular behaviour of a turbine. Due to the invalidity of certain SCADA signals (gearbox oil pressure) or absence of failures in the other components (mean bearing and generator) where SCADA data are available the failure detection analysis has been somewhat limited. It was only possible to study whether upcoming gearbox oil filter failures could be detected by an increase in the measured gearbox temperature. This analysis shows that using the measured gearbox temperature gives no reliable prediction of an upcoming oil filter failure. Only one instance was found where prior to a failure a clear increase in gearbox temperature could be observed. For most other gearbox filter replacements no clear abnormal gearbox temperature could be observed prior to the failure. On the other hand on more than one occasion a clear gradual increase in gearbox temperature was observed but no gearbox-related failures could be linked to this observation. It is concluded that although the feasibility and reliability of using SCADA data for failure detection is not proven, the contrary is also not demonstrated. 88 MSc thesis

89 8. Discussion and recommendations Operation and maintenance (O&M) becomes more and more relevant with the current shift from placing wind turbines on land (onshore) to placing turbines at sea (offshore). Where the contribution of O&M to the energy cost of wind energy is a mere 5-10% for turbines placed onshore, for wind farms placed offshore the contribution of O&M is significantly higher at about 25-30%. Not only are the costs higher, also the uncertainty and spread of the costs related to O&M are larger for offshore wind farms compared to onshore wind farms. The largest contributor to the uncertainty is corrective maintenance. To reduce this uncertainty it is essential to have accurate estimates of the failure rates of the different wind turbine components. In this thesis it has been investigated in what way and to what extent operational data of (offshore) wind farms can be used in order to make accurate estimations of the probability of occurrence of failures in an offshore wind farm. Mechanical load measurements have been used to investigate whether fatigue damage (and thus lifetime and failure rate) differs between wind turbines in an offshore wind farm. Furthermore a method has been developed to extract reliability numbers from maintenance reports (which typically are paper and often hand-written) in order to determine the failure rates of the different wind turbine components. In the third and final part of this thesis it has been investigated whether SCADA data can be used to assess the health of the turbines in an offshore wind farm and whether these data can be used for early failure detection. The research has shown that differences in fatigue damage exist between the heavier and lighter loaded turbines in an offshore wind farm. However, the calculated differences in fatigue damage for blades, tower, main bearing and gearbox are not large enough to expect significant differences in lifetime, and thus failure rates, of these components. It has not been possible to determine the differences (between the turbines in an offshore wind farm) in fatigue damage for the gearbox bearing. Further research, using a different method, is necessary to quantify whether the differences in fatigue damage for the gearbox bearing are negligible or not. Furthermore the effect of waves on the loading of the wind turbine components should be determined. This can be done by using either an aero-elastic code or using measurements from an offshore wind farm where wind and wave conditions are monitored in combination with mechanical load measurements on at least one turbine. Finally also the validity of the drive train model (which has been used to estimate the loads on the drive train components) should be proven. The method of digitalizing the information from the maintenance reports in a structured database has made it possible to analyse the experienced maintenance need of the wind turbines in an (offshore wind farm). An important result from the analysis of the maintenance reports is the fact that the number of gearbox-related failures differs strongly for the two types of gearboxes used within the wind farm. Therefore, if in an offshore wind farm different types of a certain component are used it is advisable to have different estimates of the failure rates of each group of turbines equipped with that specific component. The most important aspect of the analysis of the maintenance reports is the analysis of the occurrence of failures as function of time. From this analysis accurate estimates of the failure rates of the different wind turbine components can be established. The research has shown that these estimates generally differ significantly from the initial estimated failure rates (which are usually derived from generic data). This implies that the maintenance needs and costs of an offshore wind farm can be estimated more accurately using operational experience to estimate the failure rates, compared to the situation where the estimates of the failure rates are derived from sources that publish generic reliability data. In order to make this method really effective it is advisable that in the future, especially for offshore wind farms, every maintenance action is reported in a digital format instead of the current paper and hand-written reports. MSc thesis 89

90 The investigation of the SCADA data has shown that the normalization (according to ambient temperature and turbine power) of the temperature measured at several main components of the wind turbines reduces the range of the values considerably. This made it easier to identify possible irregular behaviour of a certain turbine. Using the normalized SCADA data it has been investigated whether failures in the turbines could be linked with irregular behaviour. Unfortunately the analysis has been limited because certain important SCADA signals were not available, whereas on the other hand no failures occurred in the components where SCADA data were available. Therefore it has not been possible to demonstrate the feasibility and reliability of using SCADA data for early fault detection. It should be noted that the contrary is also not demonstrated. 90 MSc thesis

91 References [1] Offshore Wind Farm Egmond aan Zee, visited April 24 th, [2] Offshore Wind Farm Q7 visited April 24 th, [3] J.P. Hondebrink, Connect 6000 MW; Eindrapport, Ministerie van Economische Zaken, Den Haag, July [4] L.W.M.M. Rademakers, Assessment and Optimization of Operation and Maintenance of Offshore Wind turbines, ECN-RX , June [5] P.J. Eecen et. al., Estimations of Operations and Maintenance of Offshore Wind Farms, ECN-M , May [6] S.T. Frandsen, Turbulence and Turbulence-generated Structural Loading in Wind Turbine Clusters, Risø-R-1188(EN), November [7] H.J. Sutherland, Inflow and the Fatigue of the LIST Wind Turbine, Sandia National Laboratory, AIAA , [8] M.M. Hand, N.D. Kelley, M.J. Balas, Identification of Wind Turbine Response to Turbulent Inflow Structures; Preprint, National Renewable Energy Laboratory, NREL/CP , June [9] E.J. Wiggelinkhuizen et. al., CONMOW: Condition Monitoring for Offshore Wind Farms, Scientific Proceedings, European Wind Energy Conference 2007, May [10] P.J. Eecen, J.P. Verhoef, EWTW Meteorological database; Description June May 2006, ECN-E , September [11] P.J. Eecen, et. al., Measurements at the ECN Wind Turbine Test Location Wieringermeer, ECN-RX , February [12] P.J. Eecen, et. al., LTVM statistics database, ECN-CX , September [13] D. Muhs et. al., Roloff/Matek Machineonderdelen; Normering, Berekening, Vormgeving; Theorieboek, 3 e verbeterde druk, Academic Service, Den Haag, [14] Guidelines for Design of Wind Turbines, DNV/Risø, 1 st Edition, [15] E. de Vries, Trouble Spots; Gearbox Failures and Design Solutions, Renewable Energy World, March-April 2006, p [16] Scroby Sands Offshore Wind Farm; Annual Report 2005, Department of Trade and Industry, October MSc thesis 91

92 [17] G. Niemann, H. Winter, Machinenelemente; Band II: Getriebe allgemein, Zahnradgetriebe - Grundlagen, Stirnradgetriebe, Zweite Auflage, Springer-Verlag Berlin, [18] B. Niederstucke, et. al., Load Data Analysis for Wind Turbine Gearboxes, Germanischer Lloyd WindEnergie GmbH, Hamburg, [19] T.S. Obdam, Validation of Wind Farm Production Calculations by WAsP and Flux- Farm; Using EWTW Measurements, ECN-Wind Memo , November [20] L.A.H. Machielse, Validatiemetingen EWTW; Eindrapport, ECN-E , February [21] A.J. Brand, T. Hegberg, Offshore Wind Atlas: Wind Resource in the Dutch Part of the North Sea, ECN-CX , February [22] M. Türk, S. Emeis, The Dependence of Offshore Turbulence Intensity on Wind Speed, DEWI Magazine nr. 30, February [23] Meteorological data OWEZ, visited April 3 rd, [24] E.T.G. Bot, G.P. Corten, P. Schaak, FluxFarm; A Program to Determine Energy Yield of Wind Turbines in a Wind Farm, ECN-C [25] T.S. Obdam, Analysis of Maintenance Reports & SCADA data; ECN-X , May [26] J. Davidson, The Reliability of Mechanical Systems, The Institution of Mechanical Engineers, [27] WindStats Newsletter, Volume 12 No 4 (Autumn 1999) to Volume 14 No 3 (Summer 2001), Denmark. [28] Wissenschaftliches Meß- und EvaluierungsProgramm Jahresauswertung , Institut für Solare EnergieversorgungsTechnik, Universität Gesamthochschule Kassel, Germany. [29] W. Eggersglüß, Windenergie X Praxisergebnisse , Landwirtschaftskammer Schleswig-Holstein, Germany. [30] G.J.W. van Bussel, M.B. Zaaijer, Estimation of Turbine Reliability Figures within the DOWEC Project, Report Nr , Issue 4, October [31] H. Braam, L.W.M.M. Rademakers, Failure Data Collection and Processing for N80 Wind Turbines at EWTW, ECN-X [32] P.J. Eecen, L. Grignon-Massé, Nordex N80 SCADA data; Fingerprints and trends, ECN-X MSc thesis

93 Appendix A Location of objects at EWTW In this appendix the distance and relative direction between the several objects (wind turbines and meteorological masts) at EWTW is shown in Table A.1 and Table A.2 respectively. Table A.1 Relative distance between objects at EWTW measured in rotor diameter. NM92 GE 2.5 GE 2.3 Siemens 3.6 N80-5 N80-6 N80-7 N80-8 N80-9 NM52-south NM52-north Rotor Diameter [m] RD coordinates X RD coordinates Y MM N N N N N MSc thesis 93

94 Table A.2 Relative direction between at objects EWTW. NM92 GE 2.5 GE 2.3 Siemens 3.6 N80-5 N80-6 N80-7 N80-8 N80-9 NM52-south NM52-north RD coordinates X RD coordinates Y MM N N N N N MSc thesis

95 Appendix B Turbulence using nacelle anemometry As described in [19] the relation between turbulence intensity measured by meteorological mast 3 and measured at the nacelle of the N80 turbines shows a large amount of scatter. Also the values measured by the nacelle anemometer are considerably larger than measured at the mast. These observations indicate that the wind speed at the nacelle of the N80s is severely influenced by the turbine itself. Possible contributors to the higher measured turbulence intensity are the blade passages, root vortices and the flow around the nacelle. To see whether it is possible to eliminate these 'turbine' effects from the nacelle anemometry a Fourier analysis on several time series of the nacelle wind speed is performed. B.1 Fourier analysis nacelle anemometry Several 10-minute time series of the wind speed measured at the nacelle of N80-6 have been retrieved from the database. A selection is made so that only time series where N80-6 is in 'normal' operation are retrieved. In addition to this only time series are retrieved for which the wind direction lies between 212 and 244, which, according to IEC-norm is the sector where both MM3 and N80-6 experience free-stream wind speed. The 10-minute time series are converted to frequency domain using a Fast Fourier Transform (FFT). In Figure B.1 two FFT analyses of the wind speed measured at the nacelle and meteorological mast are shown. The upper graph is generated using a time series where the turbulence intensity at MM3 and N80-6 equal 17% and 20% respectively. This is about the smallest difference found for the selected time series. The lower graph is calculated from a time series where the turbulence intensity at MM3 and N80-6 equal 1% and 17% respectively, which is about the largest difference found for the selected time series. Figure B.1 Fourier analysis of two timeseries of wind speed measured at meteorological mast 3 (both sonic and cup anemometer) and by the nacelle anemometer. MSc thesis 95

96 By studying the graph several observations can be made. Firstly it shows that the sample frequency (32 Hz) of the wind speed measurements by the nacelle anemometer is higher than the sample frequency (4 Hz) at the meteorological mast. It is also clearly visible that when comparing the FFT of the cup and sonic anemometers at MM3 that at frequencies of around 0.5 Hz the power of the FFT of the cup measurements starts to deviate from the power of the FFT of the sonic anemometer. This indicates that fluctuations in wind speed which have a high frequency are not properly measured by the cup anemometer. This is of course a logical consequence of its inertia. As a result it can be expected that the turbulence intensity measured by the cup anemometer is slightly lower compared to the sonic anemometer. The most important observation however is the fact that in both the upper and lower graph the power of the N80-6 FFT is larger than the MM3 Fourier transform for the complete frequency range. In addition to this the 3P blade passage effect (which has a frequency of around 1.5 Hz) can be distinguished in neither the upper nor the lower graph. These last two observations indicate that it is unfeasible to attribute a certain 'turbine' effect to specific frequencies and therefore it is concluded that it is impossible to design a filter that eliminates the 'turbine' effects from the wind speed measured by the nacelle anemometer. B.2 Correction nacelle anemometry In the previous section it was shown that it is impossible to develop a reliable filter that removes the 'turbine effects' from the wind speed measurements of the nacelle anemometer. In the first subsection the correction for wind speed measured at the nacelle, as is described in [19], is recapped and in the second subsection a correction to turbulence (standard deviation of wind speed) measured at the nacelle is developed. B.2.1 Wind speed In Figure B.2 the ratio of wind speed measured at N80-6 and wind speed measured at MM3 is shows as function of wind speed measured at MM3. Figure B.2: Correction for wind speed measured by the nacelle anemometer. B.2.2 Turbulence In Figure B.3 the relation between turbulence measured at MM3 with turbulence measured at N80-6 is shown. All wind speeds are used for the relation and again a wind direction bin of is chosen, which according to IEC-norm is the wind direction sector for which both MM3 and N80-6 are undisturbed. The figure shows both the filtered data and the binned averages and a linear fit using linear regression through the binned averages. 96 MSc thesis

97 Figure B.3: Relation between turbulence (defined as standard deviation of wind speed) measured at the nacelle of turbine N80-6 and at the meteorological mast 3. Although a relatively large amount of scatter (R 2 = 0.79) is visible Figure B.3 it is possible to use a linear approximation for correlating turbulence measured at the nacelle with turbulence measured at the mast. It should be mentioned that the uncertainty is larger compared to the correction to 'free-stream' average wind speed measured by the nacelle anemometer (shown in Figure B.2). MSc thesis 97

98 Appendix C Adaptation for the calculation of tilt and yaw moments This document describes the alteration of the calculation of the tilt and yaw moments. C.1 Normalization to eliminate drift effects A Fourier analysis is performed on several 10-minute time series of the calculated tilt and yaw moments. The results show that a strong 1P effect is found in both signals. This however should not be expected, because essentially both tilt and yaw moments are rotor averaged moments. The most likely explanation for the observed 1P effect is that one of the measured out of plane bending moments has a certain offset (caused by drift of the strain gauges) compared to the other blades, which should not be the case if all blades are equal. In this section it is investigated whether an offset in one or more blades is the cause of the observed 1P effect in the tilt and yaw moments. In Figure C.1 the ratio of 10-minute average outof-plane blade root bending moments of the three blades are shown as function of time. Figure C.1: Ratio of 10-minute average out-of-plane blade root bending moments of the three blades as function of time. The upper graph shows that the ratio of blade 1 over blade 2 is generally smaller than 1 and changes significantly in time; especially around Q the ratio of 10-minute average blade root bending moment is significantly lower than 1. The same pattern is observed in the middle graph, where the ratio of blade 1 over blade 3 is shown. The lower graph, which shows the ratio of blade 2 over 3, shows that this ratio is roughly equal to 1. In the last quarter of 2006 the ratio is, at times, significantly larger than 1. It is known that in this period problems with the measurements of the bending moment at blade 3 have occurred. 98 MSc thesis

99 From these observations it can be concluded that blade 2 and blade 3 generally (except for the period with known problems with the measurements at blade 3) give the same out-of-plane blade root bending moment, whereas blade 1 generally has a lower value and in addition to this also varies in time. To see whether only the absolute values of the measured blade root bending moments differ over the three blades in Figure C.2 the ratio of 10-minute standard deviation of the blade root out-of-plane bending moments of the three blades are shown as function of time. Figure C.2: Ratio of 10-minute standard deviation out-of-plane bending moments of the three blades as function of time. It can be seen that the ratio of standard deviations are in all cases about equal to 1, which indicates that the load fluctuations measured by the three blades are equal and thus the only difference is the absolute value. To eliminate the 1P effect from the tilt and yaw moments it is chosen to normalize the values of the three blades. Of course it is impossible to judge which blade gives the value that is closest to reality. Using the information from the previous graphs it is decided that blade 1 cannot be used as reference blade, because the values are clearly lower compared to the values measured at blade 2 and 3. Also blade 3 is not very suited because of the (known) problems that have occurred with the measurements in the final quarter of Therefore it is decided to use blade 2 as a reference. The measurements of blade 1 and blade 3 will be normalized to the value of blade 2 for each 10-minute period. The method for this normalization is the following: 1) For each 10-minute period the bending moments of blade 1 and 3 are normalized to reference blade 2. This is done by adding an offset to the values of blade 1 and 3. The offset for blade 1 and blade 3 is determined by taking the difference of the 10-minute average values between blade 2 and blade 1 and 3 respectively. This is illustrated in the following equations: MSc thesis 99

100 M = M M (C.1) out, offset,1 out,2 out,1 M = M M (C.2) out, offset,3 out,2 out,3 2) The signals of blade 1 and 3 are now normalized by adding the determined offsets to their signal as is shown in equation 3, 4 and 5. M = M M (C.3) out, norm,1 out,1 out, offset,1 M = M (C.4) out, norm,2 out,2 M = M M (C.5) out, norm,3 out,1 out, offset,3 In Figure C.3 an example is presented of a Fourier analysis of a 10-minute time series of tilt and yaw moment. A comparison is made between these moments calculated from the original moments and the normalized moments. Figure C.3: Fourier analysis of tilt and yaw moment. The 1P peak (at around 0.25 Hz) is clearly visible in the original signal (blue line). The corrected signal (green line) shows that this 1P effect is clearly eliminated while the rest of the spectrum remains similar to the original signal. 100 MSc thesis

101 C.2 Translation blade root moments to rotor centre The tilt and yaw moment currently are calculated by the vertical and horizontal projections of the out-of-plane blade root bending moments respectively. This is not correct. For the calculation of the tilt and yaw moments the moments in the rotor centre should be used instead of the moments measured at the blade root. Although the moments in the rotor centre are not measured they can be estimated using the method described in this section. When considering that the out-of-plane moments are mainly caused by the aerodynamic forces it can be concluded that the sum of the out-of-plane aerodynamic forces on the three blades equals the thrust force. As a result it is assumed that each blade experiences 1/3 of this force, which is the main cause for the blade s out-of-plane bending moment. The aerodynamic force on a wind turbine s blade is not constant over the span of the blade, but for this estimation it is assumed that it can be modelled as one force that acts at a certain point at the blade. This is explained in the figure below. Figure C.4: Schematic overview of a wind turbine blade, where the thrust force, the blade root and rotor centre out-of-plane bending moments are indicated. To calculate the out-of-plane bending moments in the rotor centre the distances x 1 and x 2 have to be determined. The distance x 1, which is the distance from the position of the strain gauges in the blade root to the point where the resultant of the axial force distribution on the blade acts, can be determined using the following equation, which easily can be derived from simple beam theory: M out, blade root 3 = 1 F x (C.6) thrust 1 The measured relationship between the blade root out-of-plane bending moments and 1/3 of the trust force is shown in Figure C.5. A good linear relation is found and thus distance x 1 can be determined by taking the slope of the linear fit. It is chosen to calculate the fit on the data of blade 2, because, as will be shown in the next section, the measurements at blade 2 have been the most reliable. MSc thesis 101

102 Figure C.5: Relation between out of plane bending moment and rotor thrust for the three blades. Distance x 2, which is the distance between the strain gauges and the rotor centre, is calculated using information from the technical drawings of the Nordex N80. When adding the hub radius, thickness of the blade bearing and the position of the strain gauges distance x 2 is found. If we now assume that the blade out of plane moment are mainly caused by the out-of-plane forces it is possible to calculate the out-of-plane moments in the rotor centre by the following equation: x + x = (C.7) 1 2 M out, rotor centre M out, blade root = M out, blade root x1 For the calculation of the tilt and yaw moments first the blade root out-of-plane bending moments are normalized using the method described in section C.1. The normalized rotor centre out-of-plane bending moments are consequently calculated by multiplication with a factor of The tilt and yaw moments are now calculated using the original equations with the normalized rotor centre out-of-plane bending moments as input. This is shown in the equations below. M M = 3 tilt M out rotor centre, norm, b b= 1 = 3 ( ψ ), cos ψ (C.8) yaw M out rotorcentre, norm, b b= 1 m 0, b ( ψ ), sin ψ m 0, b (C.9) 102 MSc thesis

103 C.3 Summary The calculation of the tilt and yaw moment is altered in two ways: 1) The out-of-plane blade root bending moments are normalized to blade 2 to eliminate 1P effects, which are most likely caused by drift of one of the strain gauges. 2) Instead of using the normalized blade root out-of-plane bending moments the rotor centre out-of-plane bending moments are used for the calculation of yaw and tilt. The rotor centre moments are determined by multiplying the blade root moments with a factor of MSc thesis 103

104 Appendix D Tilt moment in wake conditions As mentioned in section it is found that the tilt moment experienced by the rotor in wake conditions shows an unexpected behaviour; it is observed that the tilt moment has a considerable negative value in one side of the wake, whereas it has a considerable positive value in the other side of the wake. In general, in non-wake conditions, tilt moment is mainly a function of vertical wind shear (as was shown in Figure 4.16). To see whether the vertical wind shear in wake conditions can explain the observed values of the tilt moment in wake conditions the vertical wind shear (difference in horizontal wind speed between 108 m and 52 m, measured at MM3) is shown as function of wind direction in Figure D.1. Figure D.1: Vertical wind shear (defined as difference in lateral wind speed at 108 m and 52 height) as function of wind direction. Seen from MM3 turbine N80-5 is located at a 3.5 D distance at 315. The vertical wind shear clearly shows a considerable negative peak on one side of the wake and a considerable peak on the other side. This observation explains the behaviour of the tilt moment in wake conditions. Having explained the cause for the behaviour of the tilt moment in wake conditions still a question remains; how can it be explained that on one side of the wake a negative vertical wind shear is found, whereas a large positive wind shear is found on the other side of the wake? It could be down to the rotation of the wake; on one side of the wake air is transported upwards, and on the other side it is transported downwards. However the amount of rotation on a 3.5 D distance from the turbine is very little as is shown in Figure E MSc thesis

105 It can be concluded that no clear explanation is available for the observed vertical wind shear in wake conditions. This behaviour is not taken into account in any currently existing wake models. Further research is necessary to see whether this observed behaviour is an exception or a frequently occurring phenomenon. MSc thesis 105

106 Appendix E Differences between single and triple wake As described in sections 4.1 and 4.2 it is found that the relation between turbulence and fatigue loading differs for free-stream, single and triple wake. It was found that for the same level of measured horizontal turbulence fatigue loading is larger for operation in wake compared to operation in free-steam. In addition to this fatigue loading is slightly larger in triple wake compared to single wake for the same amount of horizontal turbulence. In this section it is investigated whether other parameters (preferably measured at the wind turbine) can explain these observations. The parameters that will be studied are the wind speed deficit in wake, power deficit in wake, horizontal wind shear and vertical turbulence. E.1 Wind speed deficit In the wake of a turbine wind speed is lower compared to the free-stream wind speed. In this section it is investigated whether wind speed this wind speed deficit differs for single and triple wake. These are defined as U = N 80 6 τ wake for single wake, and U N 80 5 U = N 80 6 τ wake for triple wake. U N 80 9 In Figure E.1 the measured wind speed deficit is shown for single and triple wake. Only data where all turbines are in operation and where wind speed lies between 6 and 8 m/s is used. Figure E.1: Wind speed deficit in single and triple wake. 106 MSc thesis

107 The figure shows that no significant difference in wind speed reduction for single and tripe wake can be observed. The maximum wind speed reduction for both is roughly the same at about 0.6. Therefore it can be concluded that wind speed reduction in wake cannot be used to explain the difference between single and triple wake. E.2 Power deficit Due to the wind speed reduction in the wake of a wind turbine also the produced power in wake conditions is lower compared to free-stream conditions. The main difference with the reduction in wind speed (as described in the previous section) is that the wind speed is measured at one single point, namely at the location of the nacelle anemometer, whereas the power is related to the average wind speed in the whole rotor disc. In Figure E.2 the measured relative power is shown for single and triple wake, which are defined as P N 80 6 P rel = for single wake, and PN 80 5 P N 80 6 P rel = for triple wake. PN 80 9 Only data where all turbines are in operation and where wind speed lies between 6 and 8 m/s is used. Figure E.2: Power deficit in single and triple wake. Similar to the wind speed reduction also for power reduction in wake no significant difference between single and triple wake can be distinguished. The minimal value of the ratio for both single and triple wake is about For these observations is concluded that power reduction in MSc thesis 107

108 wake cannot be used for explaining the difference in the relation of turbulence and loads between single and triple wake. E.3 Horizontal wind shear When a wind turbine operates in partial wake each blade experiences considerable load fluctuation during every rotation. The blade experiences both free-stream and wake (reduced wind speed) conditions during one single rotation. This has considerable effect on the fatigue loading, as was confirmed in Figure 4.2 in section A measure for horizontal wind shear is the rotor yaw moment. The yaw moment is calculated using the out-of-plane bending moments of all three blades as was shown in equation 3.3 in section In Figure E.3 the experienced yaw moment is shown for both single and triple wake. Figure E.3: Rotor yaw moment in single and triple wake. The bottom graph shows that little difference is observed between single and triple wake. The average values are more or less equal. From this observation it is concluded that horizontal wind shear, which occurs in partial wake operation, does not differ for single and triple wake. E.4 Vertical turbulence At the nacelle of the N80 turbines only horizontal wind speed measurements are performed, which has the result that only horizontal turbulence is used in the relation with the equivalent load ranges. However, usually also a vertical component in wind speed exists, which also shows fluctuations resulting in angle of attack changes and thus fluctuating blade load. In this section it is attempted to investigate whether the amount of vertical turbulence differs for single and triple wake. As mentioned before the vertical component of wind speed cannot be measured by the nacelle anemometer. Therefore the sonic anemometer at meteorological mast 3 (MM3) is used. 108 MSc thesis

109 The upper graph in Figure E.4 shows the relative vertical wind speed (defined as vertical wind speed divided by horizontal wind speed) and the lower graph shows the relative vertical turbulence (defined as vertical turbulence divided by horizontal turbulence). Both are plotted as function of wind direction. Figure E.4: Relative vertical wind speed and turbulence as function of wind direction. The amount of relative vertical wind speed is fairly small in case MM3 experiences free-stream wind, with values ranging roughly between 0 and At wind directions of 30 and 315 two peaks are observed, which are caused by the wake of turbine N80-6 and N80-5 respectively. As expected the wake of turbine N80-6 causes the largest peak, which is a logical consequence of the fact that N80-6 is located closer to MM3 than N80-5 (2.5 D compared to 3.5 D respectively). The relative vertical turbulence is more strongly affected by the wake of the N80 turbines. For free-stream conditions (between wind directions of 100 and 290 ) the ratio of vertical turbulence over horizontal turbulence is about 0.6. The 'disturbance' found between a wind direction of 150 and 200 is caused by the mast itself; the sonic anemometer is located at a boom pointing northwards, and therefore for southern wind directions its measurements are disturbed by the framework of the mast. Besides this three more peaks can be observed: At a wind direction of 315 (wake of turbine N80-5) the relative vertical turbulence increases to 0.8, at a wind direction of 30 (wake of turbine N80-6) the relative vertical turbulence is about 0.85 and at around 75 (wake of turbines N80-7, N80-8 and N80-9) the relative vertical turbulence also equals roughly This last observation is quite surprising. Although the three turbines (N80-7, N80-8 and N80-9 are located at a considerably larger distance from MM3 (5.4 D, 9.0 D and 12.8 D respectively) than N80-6 (2.5 D) still the amount of relative vertical turbulence is larger. This might be an indication that in triple wake vertical turbulence is larger compared to single wake. Unfortunately it is not possible to confirm this using the nacelle anemometer, as the nacelle anemometer, as said before, can only measure the horizontal component of the wind speed. MSc thesis 109

110 E.5 Conclusions In this section the results described above are summarized. No difference for single and triple wake wind speed deficit is observed. No difference for single and triple wake power deficit is found. Average horizontal wind shear in partial wake operation does not differ for single and triple wake, although for triple wake more scatter is observed. From vertical turbulence measurements at MM3 it is found that vertical turbulence is relatively high in the wakes of turbines N80-7, N80-8 and N80-9 compared to the measurements of a single wake. This could mean that the amount of vertical turbulence is larger in triple wake, but due to the fact that the nacelle anemometer only measures horizontal wind speed, this cannot be proven. 110 MSc thesis

111 Appendix F Comparison FluxFarm simulations with measurements In [19] the simulation of wake effects by FluxFarm was compared with measurements performed at the ECN Wind Turbine Test Location Wieringermeer (EWTW). It was shown that for an ambient wind speed of 7 m/s the simulated wind speed deficit and turbulence intensity at the location of meteorological mast 3 (MM3) are in good accordance with the measurements. In this appendix it is investigated whether similar good results are found for different wind speeds and for multiple wake situations. F.1 Wind speed deficit In [19] it was also shown that the wind speed deficit at a 3.5 D distance for a wind speed of 7 m/s calculated by FluxFarm matches the measured wind speed deficit quite well. In reference [19] the simulated wind speed deficit was only compared with measurements for operation in single wake. Here it is investigated whether the simulated wind speed deficit also matches the measurements for multiple wake situations. As described in Appendix B, average wind speed measured by the nacelle anemometer can be corrected to wind speed measured at the meteorological mast. For western winds the Nordex N80 turbines at EWTW operate in wake. For western winds MM3 is undisturbed by other wind turbines, and therefore MM3 can be used to determine the ambient turbulence intensity. Only data where the turbulence intensity has a value of 10±2% are considered. Turbine N80-5 is used to determine the free-stream wind speed. In Figure F.1 the simulated wind speed deficit (red line) is compared with the measured wind speed deficit (blue line) for single, double, triple and quadruple wakes. Figure F.1: Measured (blue line) and simulated (red line) wind speed deficit for single, double, triple and quadruple wake. MSc thesis 111

112 For single wake the simulated wind speed deficit matches the measured wind speed deficit perfectly, where the minimum value of wind speed deficit equals 0.7 for both the FluxFarm simulation and measurements. However, differences between the simulated and measured wind speed deficit can be observed for multiple wake situations. The measured minimum wind speed deficit is more or less equal to 0.7, regardless of whether the turbine is the second, third, fourth or fifth turbine in the row. The FluxFarm simulation predicts a deeper wind speed deficit for the turbines operating in multiple wakes. It can also be seen that for multiple wake the simulated wind speed deficit profile is deeper, but less wide compared to the measured profile. This indicates that for energy production calculations the modelled results will probably be comparable with the measured energy output. This was confirmed in reference [19]. F.2 Turbulence intensity Turbulence intensity can only be measured at the meteorological mast. In [19] it was shown that only the wakes of turbines N80-5 and N80-6 can be measured independently. Seen from MM3 turbine N80-5 is located at 315 at 3.5 D and turbine N80-6 is located at 31 at 2.5 D. FluxFarm has been used to simulate the turbulence intensity at the location of MM3. It was shown that for a distance of 3.5 D and for a wind speed of 7 m/s, the simulated turbulence intensity is in good accordance with the measured turbulence intensity. In this section it is investigated whether the simulation and measurements are also similar for other wind speeds. In Figure F.2 the measured (blue line) and simulated (red line) turbulence intensity at a 3.5 D distance is shown for different wind speeds. It can be observed that the turbulence intensity calculated by FluxFarm almost perfectly matches the measured turbulence intensity for wind speeds between 4 and 10 m/s. For higher wind speeds FluxFarm calculates a lower level of turbulence intensity compared with the measured values. A clear explanation for this observation is not available, but it could be caused by the limited accuracy of the thrust curve (which specifies the thrust coefficient as function of wind speed) for higher wind speeds. The thrust curve is used by FluxFarm in order to calculate the wake effects. 112 MSc thesis

113 Figure F.2: Measured (blue line) and simulated (red line) turbulence intensity at 3.5 D distance for different wind speeds. MSc thesis 113

114 Appendix G Breakdown turbine The following table shows the breakdown of the turbine, which is based on the information in the technical data sheets and the operation & maintenance instruction sheet. System Name Component Name Failure Mode NA-Base frame (BFR) BFR-Base frame Broken bolt NA-Top-Box/Control Cabinet (TOB) TOB-Sensors Defect TOB-Switches Defect TOB-Power supply Defect NA-Yaw system (YAW) YAW-Motor Defect YAW-Gearbox Defect YAW-Gearbox oil Contaminated YAW-Slide bearing Defect YAW-Friction pads Defect YAW-Brake disc Defect YAW-Controller Defect NA-Drive train (DTR) DTR-Main shaft Defect DTR-High speed shaft Defect DTR-Main bearing Defect DTR-Main bearing housing Defect DRT-Bearing support Broken bolt DTR-Rotor lock Defect DTR-Coupling Defect DTR-Slip ring Defect Short circuit Signal/Transmission error NA-Gearbox (GEB) GEB-Gearbox Bearing damage Complete damage GEB-Gearbox oil Contaminated GEB-Oil cooler Defect GEB-Filter unit Blocked GEB-Gearbox support Broken bolt 114 MSc thesis

115 NA-Generator (GEN) GEN-Brushes Worn GEN-Generator Bearing damage Complete damage GEN-Generator support Broken bolt GEN-Fuses Defect NA-Brake/Hydraulics Unit (BHU) BHU-Brake disc Defect BHU-Coatings Defect BHU-Calipers Defect Worn BHU-Hydraulic system Defect NA-Sensors (SEN) SEN-Wind vane Defect SEN-Anemometer Defect SEN-Warning lights Defect SEN-Rotor speed Defect SEN-Generator speed Defect SEN-Vibration Defect RO-Pitch mechanism (PIM) PIM-Motor Defect PIM-Gearbox Defect PIM-Gearbox oil Contaminated PIM-Bearing Defect PIM-Controller/Inverter Defect PIM-Batteries Defect Short Circuit RO-Blades (BLA) BLA-Blades Defect BLA-Blade fastening Broken bolt RO-Hub (HUB) HUB-Spinner Defect HUB-Shaft fastening Broken bolt HUB-Pitch bearing fastening Broken bolt TO-Tower (TOW) TOW-Steel tube wall Defect TOW-Flanges Broken bolt TOW-Platforms Defect TOW-Ladder Defect MSc thesis 115

116 TOW-Climb protection TOW-Cables Defect Defect TO-Inverter cabinet (INC) INC-Inverter Complete damage Hardware error Software error TO-Low voltage main distribution (LVM) LVM-Power switch Defect LVM-Cables Defect LVM-Grounding Defect LVM-Fuses Defect LVM-Uninterruptible Power Supply Defect TO-PC cabinet (PCC) PCC-PC Defect PCC-Software Software error PCC-Modem connection Transmission/Signal error TO-Transformer (TRA) TRA-Transformer Defect TRA-Fuses Defect 116 MSc thesis

117 Appendix H List of SCADA signals The following table gives an overview of the SCADA signals from the turbines in the wind farm. Note that the results have been issued to the wind farm operator in a separate confidential report. Signals containing invalid data are indicated in red. Channel name Ambient temperature Bearing temperature A Circuit breaker cut-ins Current L1 Current L2 Current L3 DL1 Main DL1 Top DL2 Main DL2 Top DO1 Main DO1 Top DO2 Main Gearbox bearing temperature Gearbox temperature Generator cooling air Generator speed Generator temperature 1 Generator temperature 2 Hydraulic pressure Nacelle rotation Nacelle temperature n-setpoint 1 n-setpoint 2 Operational mode Pitch angle blade 1 Pitch angle blade 2 Pitch angle blade 3 Pitch angle set point blade 1 Pitch angle set point blade 2 Pitch angle set point blade 3 Power Power factor Power factor setpoint Pressure gearbox oil Ratio generator over gearbox speed Reactive power Reserve Reserve 1 Reserve 2 Reserve 3 Reserve 4 Reserve 6 Reserve 7 Rotor speed MSc thesis 117

118 Scope CH1 Scope CH2 Scope CH3 Scope CH4 Shaft acceleration Shaft bearing temperature Status inverter cooling Status signal Temperature container Temperature gearbox bearing B Temperature gearbox oil exit Temperature inverter Tip speed ratio Torque Torque set point Tower acceleration Tower displacement Voltage L2 Voltage L3 Voltage L4 Wave height Wind derivation Wind speed Yaw direction 118 MSc thesis

119 Appendix I Normalization SCADA data In this appendix the normalization figures are shown for the relevant SCADA signals. In each figure three graphs are shown, which give the normalization function(s), the original SCADA data and the normalized SCADA data (using the normalization function) respectively. I.1 Temperature generator 1 The SCADA signals of turbine 2, 3 and 8 are normalized using the upper plane shown in the upper graph and the lower plane is used as normalization function for the other six turbines. MSc thesis 119