Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

  • Zhang, Yiyi (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University) ;
  • Wei, Hua (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University) ;
  • Liao, Ruijin (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Wang, Youyuan (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University and State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Yang, Lijun (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Yan, Chunyu (China Electric Power Research Institute)
  • Received : 2016.06.20
  • Accepted : 2016.09.26
  • Published : 2017.03.01
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Abstract

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

Keywords

References

  1. X. Liu, R. J. Liao, Y. Lv, "Study on Influences and Elimination of Test Temperature on PDC Characteristic Spectroscopy of Oil-Paper Insulation System," Journal of Electrical Engineering & Technology, vol. 10, no. 3, pp. 1107-1113, 2015. https://doi.org/10.5370/JEET.2015.10.3.1107
  2. C. H. Shon, S. H. Yi, H. J. Lee, "Study on the Transfer Functions for Detecting Windings Displacement of Power Transformers with Impulse Method," Journal of Electrical Engineering & Technology, vol. 7, no. 6, pp. 876-883, 2012. https://doi.org/10.5370/JEET.2012.7.6.876
  3. D. J. Kweon, K. S. Koo, J. W. Woo, J. S. Kwak, "A Study on the Hot Spot Temperature in 154kV Power Transformers," Journal of Electrical Engineering & Technology, vol. 7, no. 3, pp. 312-319, 2012. https://doi.org/10.5370/JEET.2012.7.3.312
  4. S. Khan, M. D. Equbal, T. Islam, "A Comprehensive Comparative Study of DGA Based Transformer Fault Diagnosis Using Fuzzy Logic and ANFIS Models," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, no. 1, pp. 590-596, 2015. https://doi.org/10.1109/TDEI.2014.004478
  5. R. E. James, Q. Su, "Condition assessment of high voltage insulation in power system equipment," The Institution of Engineering and Technology, London, 2008.
  6. "IEEE Std C57.104," IEEE guide for the interpretation of gases generated in oil-immersed transformers, 2008.
  7. "IEC Std 60599," Guide to the interpretation of dissolved and free gases analysis, 1999.
  8. M. Duval, A. Depabla, "Interpretation of oil in gas analysis using new IEC publication 60599 and IEC TC 10 databases," IEEE Electrical Insulation Magazine, vol. 17, no. 2, pp. 31-41, 2001. https://doi.org/10.1109/57.917529
  9. "CIGRE working group 09 of study committee 12," Life time evaluation of transformer, no. 150, pp. 39-51, 1993.
  10. M. Schou, A. Amdisen, S. E. Jensen, T. Olsen, "A Review of Dissolved Gas Analysis Measurement and Interpretation Techniques," IEEE Electrical Insulation Magazine, vol. 30, no. 3, pp. 39-49, 2014. https://doi.org/10.1109/MEI.2014.6804740
  11. J. Aghaei, A. Gholami, H. A. Shayanfar, "Dissolved gas analysis of transformers using fuzzy logic approach," European Transactions on Electrical Power, vol. 20, no. 5, pp. 630-638, 2010.
  12. A. C. M. D. Silva, A. R. G. Castro, V. Miranda, "Transformer failure diagnosis by means of fuzzy rules extracted from Kohonen Self-Organizing Map," International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 1034-1042, 2012. https://doi.org/10.1016/j.ijepes.2012.06.027
  13. C. H. Lin, C. H. Wu, P. Z. Huang, "Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers," Expert Systems with Applications, vol. 36, no. 2, pp. 1371-1379, 2009. https://doi.org/10.1016/j.eswa.2007.11.019
  14. Z. Y. Wang, C. X. Guo, Q. Y. Jiang, Y. Cao, "A fault diagnosis method for transformer integrating rough set with fuzzy rules," Transactions of the Institute of Measurement and Control, vol. 28, no. 3, pp. 243-251, 2006. https://doi.org/10.1191/0142331206tim173oa
  15. K. Meng, Z. Y. Dong, D. H. Wang, K. P. Wong, "A self-adaptive RBF neural network classifier for transformer fault analysis," IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1350-1360, 2010. https://doi.org/10.1109/TPWRS.2010.2040491
  16. V. Miranda, C. Garcez, R. Adriana, S. Lima, "Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift," IEEE Transactions on Power Delivery, vol. 27, no. 3, pp. 1350-1357, 2012. https://doi.org/10.1109/TPWRD.2012.2188143
  17. C. Pan, W. Chen, Y. Yun, "Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network," let Electric Power Applications, vol. 2, no. 1, pp. 71-76, 2008. https://doi.org/10.1049/iet-epa:20070302
  18. H. Y. Wu, C. Y. Hsu, T. F. Lee, "Improved SVM and ANN in incipient fault diagnosis of power transformers using clonal selection algorithms," International Journal of Innovative Computing Information and Control, vol. 5, no. 7, pp. 1959-1974, 2009.
  19. R. J. Liao, H. B. Zheng, S. Grzyborwski, "A multiclass SVM-based classifier for transformer fault diagnosis using a particle swarm optimizer with time-varying acceleration coefficients," International Transactions on Electrical Energy Systems, vol. 23, no. 2, pp. 181-190, 2013. https://doi.org/10.1002/etep.651
  20. S. W. Fei, X. B. Zhang, "Fault diagnosis of power transformer based on support vector machine with genetic algorithm," Expert Systems with Applications, vol. 36, no. 8, pp. 11352-11357, 2009. https://doi.org/10.1016/j.eswa.2009.03.022
  21. M. S. David, C. Dictino, M. C. Jesus, "Identification of a surface marine vessel using LS-SVM," Journal of Applied Mathematics, vol. 2013, pp. 1-11, 2013.
  22. F. Mei, J. Mei, J. Zheng, Y. Wang, "Development and Application of Distributed Multilayer On-line Monitoring System for High Voltage Vacuum Circuit Breaker," Journal of Electrical Engineering & Technology, vol. 8, no. 4, pp. 813-823, 2013. https://doi.org/10.5370/JEET.2013.8.4.813
  23. V. N. Vapnik, "The nature of statistical learning theory," New York: Springer-Verlag, 1998.
  24. Y. Bazi, F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 11, pp. 3374-3385, 2006. https://doi.org/10.1109/TGRS.2006.880628
  25. E. A. Gargari, C. Lucas, "Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition," IEEE Congress on Evolutionary Computation, vol. 21, no. 1, pp. 4661-4667, 2007.
  26. W. Sun, Y. Liang, "Least-Squares Support Vector Machine Based on Improved Imperialist Competitive Algorithm in a Short-Term Load Forecasting Model," Journal of Energy Engineering, vol. 141, no. 4, pp. 1-8, 2014.
  27. H. M. Moghimi, B. Vahidi, "A solution to the unit commitment problem using imperialistic competition algorithm," IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 117-124, 2012. https://doi.org/10.1109/TPWRS.2011.2158010
  28. C. W. Hsu, C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 415-425, 2002. https://doi.org/10.1109/72.991427
  29. F. Melgani, Y. Bazi, "Classification of electrocardiogram signals with support vector machines and particle swarm optimization," IEEE Transactions on Information Technology in Biomedicine, vol. 5, pp. 667-677, 2002.
  30. C. C, Chang, C. J. Lin, "LIBSVM: A library for support vector machines," 2001.
  31. A. Frank, A. suncion. "UCI machine learning repository", http://archive.ics.uci.edu/ml, 2012.

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