Abstract
This chapter presents an intelligent fault classification approach to transformer DGA for dealing with highly versatile or noise-corrupted data. Two methods, i.e. bootstrap and GP, are employed to preprocess gas data and extract fault features for DGA, respectively. GP is applied to establish classification features for each fault type based on dissolved gases. In order to improve GP performance, bootstrap preprocessing is utilised to equalise the sample numbers for different fault types. The features extracted using GP are then used as inputs fed to ANN, SVM and KNN classifiers for fault classifications. The classification accuracies of integrated GP-ANN, GP-SVM and GP-KNN classifiers are compared with the ones of ANN, SVM and KNN classifiers, respectively. The test results indicate that the developed classifiers using GP and bootstrap can significantly improve diagnosis accuracies for transformer DGA.
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References
The Institute of Electrical and Electronics Engineers (1994) Transformers Committee of the IEEE Power Engineering Society, IEEE guide for the interpretation of gases generated in oil immersed transformers, IEEE Std. C57.104-1991. The Institute of Electrical and Electronics Engineers, Inc., 345 East 47th Street, New York, NY 10017, USA
Morais DR, Rolim JG (2006) A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil. IEEE Trans Power Deliv 21(2):673–680
Heathcote MJ (1998) The J&P transformer book, 12th edn. Newnes imprint, UK
Zhang Y, Ding X, Liu Y (1996) An artificial neural network approach to transformer fault diagnosis. IEEE Trans Power Deliv 11(4):1836–1841
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence (IJCAI), Monreal, Canada, pp 1137–1143
Guardado JL, Naredo JL, Moreno P, Fuerte CR (2001) A comparative study of neural network efficiency in power transformers diagnostic using dissolved gas analysis. IEEE Trans Power Deliv 16:643–647
Huang YC (2003) Condition assessment of power transformers using genetic-based neural networks. IEE Proc Sci Measure Technol 150(1):19–24
Dong M, Xu DK, Li MH, Yan Z (2004) Fault diagnosis model for power transformer based on statistical learning theory and dissolved gas analysis, Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, USA, 85–88
Hao X, Sun CX (2007) Artificial immune network classification algorithm for fault diagnosis of power transformer. IEEE Trans Power Deliv 22(2):930–935
Cho MY, Lee TF, Kau SW, Shieh CS, Chou CJ (2006) Fault diagnosis of power transformer using SVM/ANN with clonal selection algorithm for features and kernel parameters selection. In: Proceedings of the 1st international conference on innovative computing, information and control (ICICIC’06) 1:26–30
Huang YC, Yang HT, Huang CL (1997) Developing a new transformer fault diagnosis system through evolutionary fuzzy logic. IEEE Trans Power Deliv 12(2):761–767
Tang WH, Wu QH, Richardson ZJ (2004) An evidential reasoning approach to transformer condition monitoring. IEEE Trans Power Deliv 19(4):1696–1703
Spurgeon K, Tang WH, Wu QH, Richardson ZJ, Moss G (2005) Dissolved gas analysis using evidential reasoning. IEE Proc Sci Measure Technol 152(3):110–117
Kishore JK, Patnaik LM, Mani V, Agrawal VK (2000) Application of genetic programming for multicategory pattern classification. IEEE Trans Evol Comput 4(3):242–258
Guo H, Jack LB, Nandi AK (2005) Feature generation using genetic programming with application to fault classification. IEEE Trans Syst Man Cybern B Cybern 35(1):89–99
Zhang L, Jack LB, Nandi AK (2005) Fault detection using genetic programming. Mech Syst Signal Process 19:271–289
Zhang L, Jack LB, Nandi AK (2005) Extending genetic programming for multi-class classification by combining k-nearest neighbor. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP 2005), vol 5, pp V-349–V-352
Sun RX, Tsung F, Qu LS (2004) Combining bootstrap and genetic programming for feature discovery in diesel engine diagnosis. Int J Indust Eng 11(3):273–281
Li RH, Xie HK, Gao NK, Shi WX (2003) Genetic programming for partial discharge feature construction in large generator diagnosis. In: Proceedings of the 7th international conference on properties of dielectric materials, Nagoya, June, pp 258–261
Zhang Z, Huang WH, Xiao DM, Liu YL (2004) Fault detection of power transformers using genetic programming. In: Proceedings of the 3rd international conference on machine learning and cybernetic, Shanghai, China, pp 3018–3022
Zhang Z, Xiao DM, Liu YL (2005) Discriminant function for insulation fault diagnosis of power transformers using genetic programming and co-evolution. In: Proceedings of 2005 international symposium on electrical insulation materials, Kitakyushu, pp 881–884
Efron B, Tibshirani RJ (1993) An introduction to the bootsrap. Chapman and Hall, New York
Zoubir AM, Boashash B (1998) The bootstrap and its application in signal processing. IEEE Signal Process Mag 15:56–76
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425
Theodoridis S, Koutroumbas K (2003) Pattern recognition, 2nd edn. Academic Press, London
Abdelhak MZ, Iskander DR (2007) Bootstrap Matlab Toolbox. http://www.csp.curtin.edu.au/downloads/bootstrap_toolbox.html
ECJ 16 (2007) A Java-based evolutionary computation research system. http://cs.gmu.edu/∼eclab/projects/ecj/
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Tang, W.H., Wu, Q.H. (2011). Fault Classification for Dissolved Gas Analysis Using Genetic Programming. In: Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. Power Systems. Springer, London. https://doi.org/10.1007/978-0-85729-052-6_7
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DOI: https://doi.org/10.1007/978-0-85729-052-6_7
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