A New Approach for Transformer Incipient Fault Diagnosis Based on Dissolved Gas Analysis (DGA)

Nova Explore Publications Nova Journal of Engineering and Applied Sciences Vol. 3(2), 2014: 1-7 PII: S229279211400008-3 www.novaexplore.com Research Article A New Approach for Transformer Incipient Fault Diagnosis Based on Dissolved Gas Analysis (DGA) Mehrdad Beykverdi 1, F. Faghihi 1, A. Moarefian pour 1 1 Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, IRAN Corresponding Author: Mehrdad Beykverdi, Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, IRAN Email: Mehrdad_b2003@yahoo.com Abstract Transformer incipient fault diagnostic method based on dissolved gas analysis (DGA) using Artificial Neural Networks (ANN) and Neural-Imperialistic Competitive Algorithm (Nero-ICA) hybrid approach is simulated in this paper and the results has been compared with IEC standard. Firstly, dissolved gas analysis method and IEC DGA standard has been presented. In the second step, application of ANN and Nero-ICA for DGA interpretation where 30 data sample tests of different transformers have been selected very carefully in order to extract known as well as unknown diagnosis correlations implicitly and these samples are used for ANN and Nero-ICA test. Finally, the results obtained from Artificial Neural Networks and Nero-ICA is compared with the actual results. Simulation results show that Nero-ICA is more accurate and effective than ANN with simple structure, if training data increased more and more. Keywords: Dissolved gas Analysis (DGA), power transformer, fault diagnosis, Neural Networks (ANN), Nero -ICA, Imperialistic Competitive Algorithm. Introduction Power transformers are the most significant and vital components of the power delivery system. Sudden failure of a large transformer may cause interruption in power supply resulting in high repair costs, revenue losses as well as environmental and collateral damages. Condition monitoring of the power transformer can eliminate all these problems. Online monitoring (dissolved gas analysis, moisture contents, temperature and partial discharge detection) and diagnostics (winding deformation, local breakdown and inter-turn faults) are the important tools for condition monitoring of power transformers [1, 2]. DGA method is used for condition monitoring of large power transformers. When fault is occurred in the transformer, the higher current carried by the windings results in higher heat loss, hence rising the temperature beyond normal limits. This causes disintegration of insulation materials within the transformer tank, such as mineral oil and paper pressboard etc. The decomposed products are mainly in the form of gases, which will eventually dissolve in the tank oil or rise up within the tank to operate the Buchholz Relay. The types and quantity of dissolved gases depend fundamentally on the temperature to which the insulation had got heated up, or more particularly on the amount of energy available to decompose the insulation materials. Arcing, partial discharge (PD), low -energy sparking, severe overloading, pump motor failures are the major causes of overheating of the insulation systems [3, 4]. In [5], a combination of extension theory and neural network is used for incipient fault diagnosis of power transformers. The ENN method has some advantages such as less time of learning that traditional neural networks and accuracy, but in some cases it can't diagnose the fault type when 3 or more faults are occurred. In the recent years, artificial intelligence is used widely in power system applications and transformer fault detection. In [6], a review on fuzzylogic method is done for power transformer fault diagnosis based on dissolved gas analysis of mineral oil. This review shows that various fuzzy-logic techniques for power transformer fault detection have been developed to reduce operating costs, enhance operational reliability and improve power and services of customers. Some disadvantages of fuzzy-logic methods are that membership functions must be determined according to practical experience or expert advice and an efficient fuzzy-logic system requires complete knowledge provided by human specialists. Some inaccuracy is always associated with lab DGA measurements of transformer oil, which may affect the gas ratios, concentrations differences and other calculations [7]. Interval or simple statistical calculations provide Nova Journal of Engineering and Applied Sciences Page: 1

a practical indication of the nature of this effect and improve the accuracy and reliability of diagnosis by associating a confidence factor with each diagnosis provided. In [8], Neural-Fuzzy approach is used for transformer fault detection based on DGA. ANFIS 1 tool box is a powerful tool in this area that applied to detect of incipient faults of power transformers. According to lack of training data and incorrect tuning of network parameters, there are situations of errors and misleading results, when some faults occurred simultaneously. In [9], ANN and fuzzy tools are applied for power transformer with mineral oil incipient fault diagnosis. Methods of Rogers, Doernenburg and NBR 7274 standard are applied for this purpose. The accuracy of proposed ANN is low but fuzzy proves highly applicable to the problem because it combines the values of gas concentrations with their evolution during the time period. In this investigation, artificial neural networks and Nero-ICA is applied for detection of power transformer faults based on DGA method. The IEC code and gas ratio is used to identify the type of faults. Method Most of power transformers are filled with oil that serves several purposes. The oil acts as dielectric media which is an insulator and as heat transfer agent. During normal use, there is usually slow degradation of the mineral oil to yield certain gases that dissolve in the oil. However, when an electrical fault happens inside the transformer, the oil start to degrade and temperature will rise abnormally which generate various gases at rapid ratio. Consider the widely used chemical test for power transformers of insulating oil called dissolved gas analysis (DGA). The DGA method as it is commonalty known is one of the most accepted methods for detecting incipient fault condition in power transformers [10, 11]. There are several ways to diagnosis the transformer fault using the DGA method which include the key gas Analysis, Rogers Ratio Method, IEC gas ratio code, Dornenberg Ratio Method, Duval Method, etc. All these methods are quite similar where different patterns and concentration of gases are matched with the fault types. The Chromatographic analysis of the insulation oil shows that it contains concentrations (PPM in volume) of dissolved H2, CH4, C2 H6, C2H4, C2H2, CO and CO2. DGA techniques can determine the condition of the transformers according to the concentration of the dissolved gases, their generation rate, ratios of specific gases, and the total amount of combustible gas in the oil [12]. In dissolved gas analysis, the IEC2 codes have been used for several decades and considerable experience accumulated throughout the world to diagnose incipient faults in transformers. The ratios of certain gases establish more comprehensive diagnostic techniques. These techniques were standardized by IEC 1978 in: "Guide for Interpretation of the Analysis of Gases in Transformer and Other Oil Filled Electrical Equipment in Service". The individual gases used to determine each ratio and its assigned limits are shown in Tables (1) and (2). Codes are then allocated according to the value obtained for each ratio and the corresponding fault characterized [13]. Gas ratio Table 1: IEC RATIO CODE IEC Code Codes of different gas ratios C 2 H 2 / C 2 H 4 CH 4 /H 2 C 2 H 4 /C 2 H 6 < 0.1 0 1 0 0.1-1 1 0 0 1-3 1 2 1 > 3 2 2 2 Table 2: FAULT CLASSIFICATION ACCORDING TO IEC CODES No. Fault type 2 2 2 4 4 2 2 4 2 6 0 No Fault 0 0 0 1 Partial discharges of low energy density 0 1 0 2 Partial discharges of high energy density 1 1 0 3 Discharges of low energy 1or 2 0 1or 2 4 Discharges of high energy 1 0 2 5 Thermal Fault of low temperature <150 C 0 0 1 6 Thermal Fault of low temperatures 0 2 0 150-300 0 C 7 Thermal Fault of medium temperatures 0 2 1 300-700 0 C 8 Thermal Fault of high temperatures > 700 C 0 2 2 Figure 1: Structure of two layer MLP 1 Adaptive Nero-Fuzzy Inference System 2 International Electric Committee Nova Journal of Engineering and Applied Sciences Page: 2

Figure 2: The structure of proposed ANN Figure3: Block diagram of Nero-ICA approach Indication for the possibility of each fault is given. Also, in some cases, the DGA results cannot be matched by the existing codes making the diagnosis unsuccessful. In multiple fault conditions, gases from different faults are mixed up resulting in confusing ratios between different gas components. This could only be dealt with by the aid of more sophisticated analysis methods such as the ANN or Nero-ICA method presented in this paper [14, 15]. Iii. Application of Ann and Nero-Ica method in transformer fault detection The fundamental concept of neural networks is the structure of the information processing system. Composed of a large number of highly interconnected processing elements or neurons, a neural network uses the human-like technique of learning by example to solve problems. Neural networks can differ based on the way their neurons are connected, the specific kinds of computations their neurons perform and the way they transmit patterns of activity throughout the network [16]. In this investigation, multi Layer Perceptrons (MLP) are utilized. In a multi -layer feed forward neural network, the artificial neurons are arranged in layers, and all neurons in each layer have connections to all the neurons in the next layer. Associated with each connection between these artificial neurons, a weight value is defined to represent the connection weight. Figure 1 shows architecture of a feed-forward neural network with an input layer, an output layer, and one hidden layer. All problems that can be solved by a neural network can be solved with only one hidden layer, but it's sometimes more efficient to use two or more hidden layers. In this research, a two layer perceptron with 14 and 20 neurons in hidden layers with sigmoid functions have been utilized. The numbers of layers and neurons have been achieved by trial and error method. A sigmoid function used in hidden layers as activation function and is an S-shaped that maps a real value, which may be arbitrarily large in magnitude positive or negative to a real value lies within some narrow range. The result of this sigmoid function lies in the range of 0 1. One pass over the entire training data set represents a training "epoch". The criterion of convergence in training is based on minimizing the mean squared error (MSE) to a level where a satisfactory agreement is found between the training set results and the network result. Once the network is considered to be trained, testing data are presented to it and outputs are compared with the experimental or observed results. Figure 2 shows the structure of proposed neural network in this paper. The numbers have been showed under the hidden layers, are neurons quantity. According to the structure of input and output vectors, MLP is precedent on RBF structure, because RBF has only one hidden layer that it increased the number of neurons in hidden layer and increased training time, and MSE function in MLP structure is more minimized than RBF. To modify the results of the ANN in accuracy and precision, this structure has been hybridizing with Imperialistic Competitive Algorithm (ICA). Nero-ICA method has been applied to minimize the MSE function by tuning network parameters. Figure 3 shows the procedure of proposed method. Figure 4 shows the flow chart of proposed ANN and Nero-ICA method. Results and Discussion In this paper, a set of 150 data were used, out of which 105 data patterns were used as the training data, 15 data patterns were used as the checking data sets and the rest 30 data patterns were used for final testing. The results of ANN and Nero-ICA have been compared with Conventional method advocated by IEC (Table 4). In order to validate two methods used in this work, 30 gas samples with actual fault type already known are collected. Simulation results obtained by each method including the conventional method are compared Nova Journal of Engineering and Applied Sciences Page: 3

and are tabulated as shown in Table 4. It can be seen that among these 30 samples, IEC method has 7 no decision (ND) and it h as failed to correctly diagnose three faults i.e. diagnosis result does not match with actual result. However, for the ANN method used in this work, it could correctly diagnose 25 of the 30 test samples under IEC method. In any case this method proves to be better than conventional method. The accuracy of the IEC method is about 66%, but the accuracy of the proposed ANN method is about 84%. From this analysis, it can be realized that though ANN method is much efficient than the conventional methods, the proposed method is not perfect, for some cases. The common problem with both, the conventional method and the ANN method is the problem of no diagnoses. Nero-ICA when tested on the same data, proved to be better in this aspect. The result shows that the Nero-ICA approach is promising for transformer fault diagnosis. The Nero-ICA used for the work has completely removed the cases of no diagnosis and is able to correctly diagnose 28 out of 30 cases. The best part of the diagnosis is that the Nero-ICA has correctly diagnosed the faults which are not detected by the ANN method. The efficiency of the Nero-ICA is around 94% in this case which can be increased to even more than 98% if more and more data samples are considered. Theoretically, the ANN and Nero-ICA can be trained to represent any observable phenomenon, if there are sufficient data available. The transformer fault diagnosis could be very complicated. For example, it is desirable to distinguish between faults of oil and cellulosic materials (paper), different temperatures (low, medium, and high) for overheating in oil, or low energy and high energy sustained arcing. To deal with such a complicated diagnosis problem, the available input data may not always be enough. It can be a very effective way to construct different Nero-ICA s for different pattern recognition to obtain the highest diagnosis accuracy for each pattern. The accuracy of the diagnosis can be improved with the increase of training data. Another very important part of dissolved gas-in-oil analysis is evaluation of trends the increasing rates of gas generation. Once this information is available, the diagnosis can be made more reliable. Start Input: Individual Concentration of Gases (ppm) and Divide them IEC Method Output Rules Identified Fault Yes No ANN & Nero-ICA Condition Identification End Figure 4: Flow chart of proposed Methods Nova Journal of Engineering and Applied Sciences Page: 4

Conclusion IEC gas ratio method can be used to automate standard methods of transformer oil DGA, providing enhanced information for the maintenance engineer while remaining faithful to the original methods. ANN is simulated in this paper, proved to be successful tool for diagnosing of transformer faults. Applicability of neural networks depends upon the training data for which it is trained. The problem with ANN is the availability of database upon which the credibility of ANN greatly lies. MLP structure of neural networks applied in this investigation to decrease number of neurons and training time and minimize MSE function. Powerful new diagnostic methods based on large neural networks may be possible, but their development requires the assembly of a large database of examples for training and validation. In some cases, neural networks can be used in combination with ICA, Genetic or PSO algorithm to implement more complex diagnostic methods while maintaining a straightforward relationship between the enhanced method and the original one. The results demonstrate that Nero-ICA method is a powerful tool and found to be highly useful for transformer monitoring and fault diagnostics. Table 4: Results Of Ann And Nero-Ica And Comparison With Iec Method No. H 2 CH 4 C 2H 2 C 2H 4 C 2H 6 IEC Method Actual Fault IEC Fault Type ANN Nero-ICA Code 1 200 700 1 740 250 TF 021 TF TF TF 2 300 490 95 360 180 TF 121 ND TF TF PD-HED 3 56 61 31 32 75 120 ND ND PD- HED 4 33 26 0.2 5.3 6 NF 000 NF NF NF 5 176 205.9 68.7 75.7 47.7 DLE 121 ND DLE DLE 6 70.4 69.5 10.4 241.2 28.9 TF-H 002 ND TF-H TF-H 7 162 35 44 30 5.6 DHE 102 DHE DLE DHE 8 345 112.25 58.75 51.5 27.5 DLE 101 DLE DLE DLE 9 181 262 0 528 210 TF-M 021 TF-M TF-M TF-M 10 172.9 334.1 37.7 812.5 172.9 TF-H 022 TF-H TF-H TF-H 11 2587.2 7.882 0 1.4 4.7 PD-LED 000 NF PD PD 12 1678 652.9 419.1 1005.9 80.7 DHE 102 DHE DHE DHE 13 206 198.9 15.1 612.7 74 TF-H 002 ND TF-H TF-H 14 180 175 4 50 75 TF-L 000 NF TF-L TF-L 15 34.45 21.92 19.62 44.96 3.19 DHE 102 DHE DHE DHE 16 51.2 37.6 51.6 52.8 5.1 DHE 102 DHE DHE DHE 17 106 24 37 28 4 DHE 102 DHE DHE DHE 18 180.85 0.5 0 0.18 0.234 PD-LED 000 NF PD PD 19 12 8 0 5 40 NF 000 NF NF NF 20 16 25 0 39 19 TF-M 021 TF-M TF-M TF-M 21 36 26 0 2 45 TF-M 000 NF NF TF-M 22 30 5 0 13 10 TF-L 001 TF-L TF-L TF-L 23 645 86 317 110 13 DHE 102 DHE DHE DHE 102 or DLE or DHE 24 385 60 159 53 8 DHE 202 DHE DHE 25 595 80 244 89 9 DHE 102 DHE DHE DHE 26 22 40 1 6 36 TF 120 ND TF TF 27 1770 3630 78 8480 1070 TF 022 TF-H TF TF 28 86 30 29 35 10 DHE 102 DHE DHE DHE 29 34 39 9 40 9 DHE 122 ND DHE DHE 30 142 3 1 8 2 DHE 102 DHE DHE DHE Nova Journal of Engineering and Applied Sciences Page: 5

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