Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation

IET Electric Power Applications

Volumn 5, Issue 9, 2011, Pages 691-696

  Zheng, H B ^a   Liao, R J ^a   Grzybowski, S ^b   Yang, L J ^a  

^a CHONGQING UNIVERSITY   (China)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; BENCHMARK CLASSIFICATION; BINARY CLASSIFIERS; DIAGNOSIS METHODS; ERROR CORRECTING OUTPUT CODE; FEATURE PARAMETERS; LEAST SQUARE SUPPORT VECTOR MACHINES; MULTI-CLASS; MULTI-CLASS CLASSIFICATION; PARTICLE SWARM OPTIMISATION; TRAINING AND TESTING; TRANSFORMER FAULT DIAGNOSIS;

FEATURE EXTRACTION; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); POWER TRANSFORMERS;

SUPPORT VECTOR MACHINES;

EID: 80155183216     PISSN: 17518660     EISSN: 17518679     Source Type: Journal    
DOI: 10.1049/iet-epa.2010.0298     Document Type: Article

References (28)

1. Wang, M.H.: 'Grey-extension method for incipient fault forecasting of oil-immersed power transformer', Electr. Power Compon. Syst., 2004, 32, (10), p. 959-97510.1080/15325000490257999 1532-5008
2. Tripathy, M., Maheshwari, R.P., and Verma, H.K.: 'Probabilistic neural-network-based protection of power transformer', IET Electr. Power Appl., 2007, 1, (5), p. 793-79810.1049/iet-epa:20070009
3. Wu, H.Y., Hsc, C.Y., Lee, T.F., and Fang, F.M.: 'Improved SVM and ANN in incipient fault diagnosis of power transformers using clonal selection algorithms', Int. J. Innov. Comput. Inf. Control, 2009, 5, (7), p. 1959-1974
4. Singh, J., Sood, Y.R., and Verma, P.: 'Condition monitoring of power transformers-bibliography survey', IEEE Electr. Insul. Mag., 2008, 24, (3), p. 11-2510.1109/MEI.2008.4591431 0883-7554
5. James, R.E., and Su, Q.: 'Condition assessment of high voltage insulation in power system equipment', (The Institution of Engineering and Technology, London 2008)
6. Meng, K., Dong, Z.Y., Wang, D.H., and Wong, K.P.: 'A self-adaptive RBF neural network classifier for transformer fault analysis', IEEE Trans. Power Syst., 2010, 25, (3), p. 1350-136010.1109/TPWRS.2010.2040491 0885-8950
7. Zhang, Y., Ding, X., Liu, Y., and Griffin, P.J.: 'An artificial neural network approach to transformer fault diagnosis', IEEE Trans. Power Del., 1996, 11, (4), p. 1836-184110.1109/61.544265
8. Wang, M.H.: 'A novel extension method for transformer fault diagnosis', IEEE Trans. Power Del., 2003, 18, (1), p. 164-16910.1109/TPWRD.2002.803838
9. Thang, K.F., Aggarwal, P.K., Mcgrail, A.J., and Esp, D.G.: 'Analysis of power transformer dissolved gas data using the self-organizing map', IEEE Trans. Power Del., 2003, 18, (4), p. 1241-124810.1109/TPWRD.2003.817733
10. Shintemirov, A., Tang, W.H., and Wu, Q.H.: 'Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming', IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2009, 39, (1), p. 69-7910.1109/TSMCC.2008.2007253 1094-6977
11. Yang, Z., Tang, W.H., Shintemirov, A., and Wu, Q.H.: 'Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers', IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2009, 39, (6), p. 597-61010.1109/TSMCC.2009.2021989 1094-6977
12. Tang, W.H., Goulermas, J.Y., Wu, Q.H., Richardson, Z.J., and Fitch, J.: 'A probabilistic classifier for transformer dissolved gas analysis with a particle swarm optimizer', IEEE Trans. Power Del., 2008, 23, (2), p. 751-75910.1109/TPWRD.2008.915812
13. Naresh, R., Sharma, V., and Vashisth, M.: 'An integrated neural fuzzy approach for fault diagnosis of transformers', IEEE Trans. Power Del., 2008, 23, (4), p. 2017-202410.1109/TPWRD.2008.2002652
14. Lin, C.H., Wu, C.H., and Huang, P.Z.: 'Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers', Expert Syst. Appl., 2009, 36, (2), p. 1371-137910.1016/j.eswa.2007.11.019 0957-4174
15. Pan, C., Chen, W., and Yun, Y.: 'Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network', IET Electr. Power Appl., 2008, 2, (1), p. 71-7610.1049/iet-epa:20070302
16. Lv, G.Y., Cheng, H.Z., Zhai, H.B., and Dong, L.X.: 'Fault diagnosis of power transformer based on multi-layer SVM classifier', Electr. Power Syst. Res., 2005, 74, (1), p. 1-710.1016/j.epsr.2004.07.008 0378-7796
17. Tang, W.H., Shintemirov, A., and Wu, Q.H.: 'Transformer dissolved gas analysis using least square support vector machine and bootstrap', Proc. 26th Chinese Control Conf., Zhangjiajie, Hunan, China, July 2007, p. 482-486
18. Fei, S.W., and Zhang, X.B.: 'Fault diagnosis of power transformer based on support vector machine with genetic algorithm', Expert Syst. Appl., 2009, 36, (8), p. 11352-1135710.1016/j.eswa.2009.03.022 0957-4174
19. Suykens, J.A.K., Van Gestel, T., de Brabanter, J., de Moor, B., and Vandewalle, J.: 'Least squares support vector machines', (World Scientific, Singapore 2002)
20. Vapnik, V.N.: 'The nature of statistical learning theory', (Springer-Verlag, New York 1998)
21. van Gestel, T., Suykens, J.A.K., and Baesens, B.: et al. 'Benchmarking least squares support vector machine classifiers', Mach. Learn., 2004, 54, (1), p. 5-3210.1023/B:MACH.0000008082.80494.e0 0885-6125
22. Bazi, Y., and Melgani, F.: 'Toward an optimal SVM classification system for hyperspectral remote sensing images', IEEE Trans. Geos. Remote Sens., 2006, 44, (11), p. 3374-338510.1109/TGRS.2006.880628
23. Kennedy, J., and Eberhart, R.C.: 'Particle swarm optimization', Proc. IEEE Int. Conf. Neural Networks, 1995, 4, p. 1942-1948
24. Clerc, M., and Kennedy, J.: 'The particle swarm-explosion, stability, and convergence in a multidimensional complex space', IEEE Trans. Evolut. Comput., 2002, 6, (1), p. 58-7310.1109/4235.985692 1089-778X
25. Hsu, C.W., and Lin, C.J.: 'A comparison of methods for multiclass support vector machines', IEEE Trans. Neural Netw., 2002, 13, (2), p. 415-42510.1109/72.991427 1045-9227
26. Mathur, A., and Foody, G.M.: 'Multiclass and binary SVM classification: implications for training and classification users', IEEE Geos. Remote Sens. Lett., 2008, 5, (2), p. 241-24510.1109/LGRS.2008.915597
27. Passerini, A., Pontil, M., and Frasconi, P.: 'New results on error correcting output codes of kernel machines', IEEE Trans. Neural Netw., 2004, 15, (1), p. 45-5410.1109/TNN.2003.820841 1045-9227
28. Frank, A., and Asuncion, A.: 'UCI machine learning repository', http://archive.ics.uci.edu/ml, 2010