INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS VOL.4 NO.2 ISSN 2165-8277 (Print) ISSN 2165-8285 (Online) http://www.researchpub.org/journal/jac/jac.html
APRIL 2015
Artificial Neural Networks for Power Transformers Fault Diagnosis Based on IEC Code Using Dissolved Gas Analysis Sherif S. M. Ghoneim1, 2 and Ibrahim B. Taha1, 3 The most five criteria that are commonly used for dissolved gas analysis are the International Electrotechnical Commission standard (IEC) Code, the Central Electric Generating Board (CEGB) Code based on Rogers four ratios, Rogers’ method, Dornenburg method and Duval triangle according to the Institute of Electrical and Electronics Engineers standard (IEEE-C57) [3]. The above criteria do not involve any mathematical formulation and their interpretations are based on heuristic methods that may vary based on experience of the analyst, results in unreliable analysis [4]. To overcome the drawbacks come from these criteria, various computational models using Artificial Intelligence (AI) have been used to analysis the incipient fault in power transformer. Application of AI in transformer incipient fault diagnosis requires real DGA data. During the period of 1987-2012, there were over 400 research published on IEEE (26 EPSs, 72 ANNs, 58 FL and ANN-FL, 20 ANN-EPS; 248 DGA and related ones) [5]. Several AI methods have been developed for more accurate diagnosis. These methods are mostly suitable for transformers with a single fault or a dominant fault. These AI methods are: Artificial Neural Network (ANN) [6,7], Fuzzy Logic [8, 9], Neuro–Fuzzy [10, 11], Genetic [12, 13], Hidden Markov Model (HMM) [14], Support Vector Machine (SVM) [15, 16], and Graphical Techniques [17, 18]. They were developed as a novel technique to interpret the faults in power transformers. In this paper, back propagation ANN model is constructed based on DGA of the IEC Standard rules method. A comparison between the results of the ANN and that obtained from the literatures is presented. The results refer to the reliability of the proposed ANN model as a diagnostic tool for incipient power transformer fault.
Abstract—Transformer is the main important equipment in electrical power system. Early stage detection of the transformer faults has great economic significance because it considered expensive equipment and it helps to maintain the continuous operation of the electrical power system. Transformer oil is used for two main purposes, one for insulating liquid and the other for cooling. Some physicalchemical tests are carried out to determine the physical and chemical properties of the oil. Dissolved Gas Analysis (DGA) is now considered a common practice method for detection of the transformer incipient fault. This paper focuses on the employment of the Artificial Neural Network techniques (ANN) to diagnose dissolved gas in transformers, in order to determine the fault causes based on the IEC standard method. The ANN on IEC Code results meets the similar results of the other techniques that use to diagnose the transformer fault. Therefore, this method is very reliable to use as a diagnostic tool for transformer fault detection. Keywords—Transformer faults, Dissolved gas analysis, Neural Networks.
I. INTRODUCTION Power transformer is considered as one of the most vital, important and expensive components in electric power systems. Any fault in power transformer may result in power outages and black-outs of the electrical power system. Therefore, the early detection of the power transformer incipient faults lead to an improvement in power system reliability and operation. Moreover, the replacement of a power transformer is very costly and time consuming; hence it is very important to diagnose incipient faults as soon as possible to prevent an increase of the transformer faults Dissolved Gas Analysis (DGA) in the transformer oil is a wide spread method that is used to identify the incipient faults in oil-filled power transformers. There are different stresses affect on the insulating transformer oil, which are electrical and thermal stresses due to arcing, corona discharges, sparking, or overheating fault. As a result of these stresses, insulating materials may be damaged and several gases are released. The main dissolved gases in the transformer oil are: hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO) and carbon dioxide (CO2). The detection methods based on dissolved gas analysis are used to diagnose the incipient fault in power transformer before deteriorating to a severe state [1-2].
II. DISSOLVED GAS ANALYSIS (DGA): IEC STANDARD CODE The IEC three-ratio method is widely used as a guideline and a standard in diagnosis stage as it is being one of the effective and convenient guidelines and available standards [4]. Table 1 shows the relations between the three-ratios and the method codes while Table 2 tabulates the IEC standard fault types in power transformer. It consists of three key-gas ratios corresponding to the suggested fault diagnosis.
1
College of Engineering Taif University, Saudi Arabia Kingdom Faculty of Industrial Education, Suez University, Suez, Egypt,
[email protected], 3Faculty of Engineering, Tanta University, Tanta, Egypt.
[email protected] 2
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INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS VOL.4 NO.2 ISSN 2165-8277 (Print) ISSN 2165-8285 (Online) http://www.researchpub.org/journal/jac/jac.html
working with the back-propagation learning algorithm [21, 22].
Table 1 IEC Code [1] Gas Ratio R1=C2H2/C2H4
R2=CH4/H2
R3=C2H4/C2H6
Value
Code
R13
2
R21
2
R33 2 Table 2 Fault diagnosis using IEC Code
Input Layer
Hidden Layers
Output Layer
Fig. 1: MLP neural network training
The output from the sigmoid function lies between 0 and 1. The mean square error is proposed to a level of 0.0001, where a satisfactory agreement is found between the training set results and the network result. In this study 74 samples of DGA were provided by the electrical utility and 125 samples were taken from DGA results published in the literatures [23, 30]. All 199 samples are used for validating the neural network model.
Code No.
APRIL 2015
Fault Type R1
R2
R3
1
No fault
0
0
0
3
Partial discharge with low energy density
0
1
0
4
Partial discharge with high energy density
1
1
0
5
Arcing discharge with low energy
1or 2
0
1
6
Arcing discharge with high energy
1
0
2
7
Thermal fault with temperature less than 150 oC
0
0
1
This diagnosis criterion uses basically Rogers input vector as follows:
8
Thermal fault with temperature between 150 to 300 oC
0
2
0
[𝐼𝑛𝑝𝑢𝑡] = [𝑅1 , 𝑅2 , 𝑅3 ]𝑇
9
Thermal fault with temperature between 300 to 700 oC
0
2
1
The output vector is build up with ten elements according to Table 2. Ten neurons are utilized in the output layer.
10
Thermal fault with temperature greater than 700 oC
0
2
2
IV. VALIDATION OF THE PROPOSED SMART DIAGNOSTIC DECISION SYSTEM
2
Undetermined fault (fail to determine the fault type)
The input and output patterns are required for Neural network validity. Input patterns are considered the dissolved gas ratios codes according to each fault state. For each input pattern, there exists an output pattern that describes the fault type. Both input and output patterns constitute ANN training set. Input and output patterns are defined as follows:
(1)
The validation of the proposed model is achieved by comparing its results with the results in literatures. Some samples are collected from some researches and laboratory analysis then compare them with the results in literatures. Table 3 illustrates the agreement between the ANN results and the results in literatures. It is agreement percentage between the results from ANN and that in Literatures as shown in Table 3 is more than 90%. It appears that a conflict among the results comes from the No fault identification as well as normal operation state with the thermal state in literatures. The results refer to the reliability of the proposed ANN model as a diagnostic tool for incipient fault detection.
For above codes not obtained
III. APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) FOR TRANSFORMER FAULT DETECTION ANN model is constructed using MATLAB software for IEC Standard code interpretation method. Figure 1 shows the multilayer feed forward back-propagation is chosen as the network architecture because it considers the most popular ANN Architecture [19] and its ability for pattern recognition [20]. The ANN architecture model consists of four layer networks (one input layer, two hidden layer and one output layer).
V. CONCLUSION An artificial neural network (ANN) model is constructed for IEC Standard code method that based on dissolved gas analysis. To test the NN model based on IEC rules, 102 samples are used. The agreement of NN model with
A two layer perception has been utilized because of two reasons. These are; the highly nonlinearity between the input and due to a good performance of ANN when
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INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS VOL.4 NO.2 ISSN 2165-8277 (Print) ISSN 2165-8285 (Online) http://www.researchpub.org/journal/jac/jac.html
the literatures is more than 90% of the tested cases. The problem is experienced, the difference between the results from the proposed model and the results from literatures come from No fault and No fault identification states with the thermal fault for the literatures results.
ACKNOWLEDGMENT The authors thank Prof. Dr. Ayman A. Aly for his valuable Discussion in construction ANN model. REFERENCES
Table 3 Comparison between the results from ANN based on IEC Standard interpretation method and the results from literatures and lab analysis H2
CH4
C2H6
C2H4
C2H2
CO
IEC state
Ref
Ref. state
269
1081
347
1725
25
360
HTH
[23]
HTH
10
10
8
1
0.01
334
NF
[23]
LTH
30
22
14
4.1
0.1
400
NF
[24]
NF
2.9
2
1.5
0.3
0.1
200
UD
[24]
NF
4
99
82
4.2
0.1
200
LTH
[24]
TH
21
34
5
47
62
390
UD
[24]
HAD
50
100
51
305
9
400
HTH
[24]
TH
120
17
32
4
23
350
UD
[24]
NF
980
73
58
12
0.01
243
PD
[24]
PD
30.8
149
47.9
146
0.1
350
HTH
[24]
TH
27
136
46.9
131
0.1
360
MTH
[24]
TH
1607
615
80
916
1294
380
HAD
[24]
HAD
14.7
3.7
10.5
2.7
0.2
1046
NF
[25]
NF
181
262
41
28
0.01
415
LTH
[25]
TH
173
334
172
812.5
37.7
404
HTH
[25]
TH
127
107
11
154
224
478
HAD
[25]
HAD
60
40
6.9
110
70
678
HAD
[25]
HAD
27
90
42
63
0.2
470
MTH
[25]
TH
980
73
58
12
0.01
243
PD
[25]
PD
86
187
136
363
0.01
26
MTH
[26]
HTH
10
24
372
24
0.01
343
LTH
[26]
MTH
30.4
117
44.2
138
0.1
380
HTH
[27]
TH
260
3
18
2
0.01
350
PD
[28]
PD
586
19
77
6
0.01
370
PD
[28]
PD
200
700
250
740
1
415
MTH
[29]
TH
33
26
6
53
0.2
678
UD
34.45
21.3
3.19
45
19.62
211
HAD
180.85
0.5
0.234
0.18
0.0001
252
PD
12
8
40
5
0.01
436
NF
16
25
19
39
0.01
383
MTH
22
40
36
6
1
422
UD
1770
3630
1070
8480
78
350
HTH
86
30
10
59.3
41
239
27.5
469
147
35
29
1014
HAD
9.9
111
70
224
HAD
5.5
25.5
85
317
LAD
12.5
265
520
211
HAD
56
5.5
92
34.5
27.5
436
PD
14
237
92
470
0.01
365
HTH
157
127
34
96
0.01
422
LTH
[29] [29] [29] [29] [29] [29] [29] [29] [30] [30] [30] [30] [30] [30]
APRIL 2015
[1] IEC Publication 599, “Interpretation of the analysis of gases in transformers and other oil-filled electrical equipment in service”, First Edition 1978. [2] IEEE Guide for the Interpretation of Gases Generated in OilImmersed Transformers, IEEE StandARCd C57.104-2008, Feb.2009. [3] Rahmat-Allah Hooshmand, and Mahdi Banejad, “Fuzzy Logic Application in Fault Diagnosis of Transformers Using Dissolved Gases”, Journal of Electrical Engineering & Technology, Vol. 3, No. 3, pp. 293~299, 2008. [4] Fathiah Zakaria, Dalina JohARCi and Ismail Musirin, “Artificial Neural Network (ANN) Application in Dissolved Gas Analysis (DGA) Methods for the Detection of Incipient Faults in Oil-Filled Power Transformer”, 2012 IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, 23 - 25 Nov. 2012. [5] Nguyen Van Le, "Application of Artificial Intelligence in Diagnosis of Power Transformer Incipient Faults", 26th IEEE Canadian Conference Of Electrical And Computer Engineering (CCECE), Regina, Saskatchewan, Canada, 2013. [6] J. L. Guardado, J. L. Nared, P. Moreno, and C. R. Fuerte, “A Comparative Study of Neural Network Efficiency in Power Transformers Diagnosis Using Dissolved Gas Analysis”, IEEE Transactions on Power Delivery, Vol. 16, No. 4, October 2001. [7] Vladimiro Miranda, Adriana R. Garez Castro, and Shigeaki Lima, “Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift”, IEEE Transactions on Power Delivery, Vol. 27, No. 3, July 2012. [8] Rahmatollah Hooshmand, and Mahdi Banejad, “Application of Fuzzy Logic in Fault Diagnosis in Transformers using Dissolved Gas based on Different Standards”, World Academy of Science, Engineering and Technology, 17, 2006. [9] Yann-Chang Huang and Huo-Ching Sun, “Dissolved Gas Analysis of Mineral Oil for Power Transformer Fault Diagnosis Using Fuzzy Logic”, IEEE Transactions on Dielectrics and Electrical Insulation Vol. 20, No. 3; pp. 974-981, June 2013. [10] M. Allahbakhshi and A. Akbari, "Novel Fusion Approaches for the Dissolved Gas Analysis of Insulating Oil", IJST, Transactions of Electrical Engineering, Vol. 35, No. E1, pp 13-24, 2011. [11] J. P. Lee, D. J. Lee, S. S. Kim, P. S. Jiand J.Y. Lim, “Dissolved Gas Analysis of Power Transformer Using Fuzzy Clustering and Radial Basis Function Neural Network”, Journal of Electrical Engineering & Technology, Vol. 2, No. 2, pp. 157~164, 2007. [12] Alamuru Vani, and Pessapaty Sree Rama Chandra Murthy, “A Hybrid Neuro Genetic Approach for Analyzing Dissolved Gases in Power Transformers”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 11, November 2014. [13]Wei Chang, and Ning Hao, “Prediction of Dissolved Gas Content in Transformer Oil Based on Genetic Programming and DGA”, International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), December 16-18, Changchun, China, 2011. [14]Qian Suxiang, Jiao Weidong, Hu Hongsheng, and Yan Gongbiao, "Transformer Power Fault Diagnosis System Design Based on the HMM Method", Proceedings of the IEEE International Conference on Automation and Logistics, August 18 - 21, 2007, Jinan, China, pp. 1077-1082, 2007. [15]Chenghao Wei, Wenhu Tang, and Qinghua Wu, "Dissolved Gas Analysis Method Based on Novel Feature Prioritisation and Support Vector Machine", IET Electric Power Applications, Vol. 8, Issue 8, pp. 320–328, 2014. [16]Seifeddine Souahlia, Khmais Bacha, and Abdelkader Chaari, "SVM-Based Decision for Power Transformers Fault Diagnosis Using Rogers and Doemenburg Ratios DGA", 10th International Multi-Conference on Systems, Signals & Devices (SSD), Hammamet, Tunisia, March 18-21,pp. 1-6, 2013.
NF HAD PD NF TH TH TH HAD HAD LAD HAD LDA HTH LTH
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[17] Diaa-Eldin A. Mansour, “A New Graphical Technique for the Interpretation of Dissolved Gas Analysis in Power Transformers”, 2012 Annual Report Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), pp.195 – 198, 14-17 Oct. 2012. [18] Y.M.Kim, S.J.Lee, H.D.Seo, J.R.Jung and H.J.Yang, “Development of Dissolved Gas Analysis (DGA) Expert System Using New Diagnostic Algorithm for Oil immersed Transformers”, Journal of Electrical Engineering & Technology, Vol. 3, No. 3, pp. 293~299, 2008. [19] Wan Mohd Fahmi Wan Mamat,; Nor Ashidi Mat Isa,; Zamli, Kamal Zuhairi; Wan Mohd Fairuz Wan Mamat,; , "Hybrid Version of MLP Neural Network for Transformer Fault Diagnosis System", International Symposium on Information Technology, Vol.2, No., pp.1-6, 26-28 Aug. 2008. [20] Sánchez and Lau, "Artificial Neural Networks," New York, USA: IEEE Press, 1992. [21] L. Haykin, "Neural Networks: A Comprehensive Foundation," Montreal, Canada: Macmillan College Publishing Company Inc., 1994. [22] J. Hilera and V. Martínez, "Redes Neuronales Artificiales," Addison- Wesley Iberoamérica, RA-MA, 1995. [23] Sherif Ghoneim, and Nadjim Merabtine, “Early Stage Transformer Fault Detection Based on Expertise Method”, International Journal of Electrical Electronics and Telecommunication Engineering, Vol. 44, pp.1289 – 1294, Aug. 2013. [24] Souahlia Seifeddine, Bacha Khmais, and Chaari Abdelkader, "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Artificial Neural Network", First International Conference on Renewable Energies and Vehicular Technology, 2012. [25] Mang-Hui Wang,"A Novel Extension Method for Transformer Fault Diagnosis", IEEE Transactions on Power Delivery, vol. 18, no. 1, January 2003. [26] Sherif S. M. Ghoneim, and Sayed A. Ward, " Dissolved Gas Analysis as a Diagnostic Tools for Early Detection of Transformer Faults", Advances in Electrical Engineering Systems (AEES), Vol. 1, No. 3, 2012, pp. 152-156. [27] Khmais Bacha, Seifeddine Souahlia, and Moncef Gossa, "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Support Vector Machine", Electric Power System Research, pp. 7379, 2012. [28] M. R. Ahmed, M. A. Geliel, and A. Khalil, "Power transformer Fault Diagnosis using Fuzzy Logic Technique Based on Dissolved Gas Analysis", 21st Mediterranean Conference on Control&Automation (MED), Platanias-Chania, Crete, Greece, June 25-28, 2013, pp. 584-589, 2013. [29] Mehrdad Beykverdi, F. Faghihi, and A. MoArefian pour, "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. 2(3), pp. 1-8, March 2014. [30] Amit Kumar Mehta, R. N. Sharma, Sushil Chauhan, and Satyabrata Saho, "Transformer Diagnostics under Gas Analysis Using Support Vector Machine", International conference on Power, Energy and Control (ICPEC), pp. 181-186, 2013.
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Appendix ARC: discharge arcing UD: undetermined fault or no fault identification TH: thermal fault PD: partial discharge NF: no fault (normal operation) HAD: high discharge energy arcing LAD: low discharge energy arcing HTH: high temperature thermal fault LTH: low temperature thermal fault MTH: medium temperature Author' profile: Sherif S. M. Ghoneim Received B.Sc. and M.Sc. degrees from the Faculty of Engineering at Shoubra, Zagazig University, Egypt, in 1994 and 2000, respectively. Starting from 1996 he was a teaching staff at the Faculty of Industrial Education, Suez Canal University, Egypt. Since end of 2005 to end of 2007, he is a guest researcher at the Institute of Energy Transport and Storage (ETS) of the University of DuisburgEssen-Germany. In 2008, he got Ph.D Degree in Electrical power and machines, Faculty of Engineering-Cairo University (2008). He joins now the Taif University as an assistant professor in the Electrical Engineering Department, Faculty of Engineering. His research focuses in the area of Grounding systems, Dissolved gases analysis, Breakdown in SF6 gas and artificial intelligent technique applications. Ibrahim B. Taha Received B.Sc. degree from the Faculty of Engineering at Tanta, Tanta University, Egypt, in 1995. He received M.Sc. degree from the Faculty of Engineering at Mansoura, Mansoura University, Egypt, in 1999. Starting from 1996 he was a teaching staff at the Faculty of Engineering, Tanta University, Egypt. In 2007, he got Ph.D Degree in Electrical power and machines, Faculty of Engineering-Tanta University (2007). He joins now the Taif University as an assistant professor in the Electrical Engineering Department, Faculty of Engineering. His research focuses in the area of steady state and transient stability of HVDC systems, FACTS, Multi Level Inverters, Dissolved gases analysis, and artificial intelligent technique applications.
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