Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 847623, 8 pages
http://dx.doi.org/10.1155/2014/847623
Research Article

Fault Diagnosis of Oil-Immersed Transformers Using Self-Organization Antibody Network and Immune Operator

School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China

Received 2 July 2014; Accepted 18 August 2014; Published 16 November 2014

Academic Editor: Yoshinori Hayafuji

Copyright © 2014 Liwei Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. H. Tang and Q. H. Wu, Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence, Springer, London, UK, 2011.
  2. Mineral Oil-Impregnated Electrical Equipment in Service—Guide to the Interpretation of Dissolved and Free Gases Analysis, IEC Publication, 2007.
  3. “Guide for the interpretation of gases generated in oil-immersed transformers,” Tech. Rep. IEEE Std C57.104-2008, 2009.
  4. C. E. Lin, J. M. Ling, and C. L. Huang, “An expert system for transformer fault diagnosis using dissolved gas analysis,” IEEE Transactions on Power Delivery, vol. 8, no. 1, pp. 231–238, 1993. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Tomsovic, M. Tapper, and T. Ingvarsson, “A fuzzy information approach to integrating different transformer diagnostic methods,” IEEE Transactions on Power Delivery, vol. 8, no. 3, pp. 1638–1645, 1993. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Zhang, X. Ding, Y. Liu, and P. J. Griffin, “An artificial neural network approach to transformer fault diagnosis,” IEEE Transactions on Power Delivery, vol. 11, no. 4, pp. 1836–1841, 1996. View at Publisher · View at Google Scholar · View at Scopus
  7. W. S. Lin, C. P. Hung, and M. H. Wang, “CMAC_based fault diagnosis of power transformers,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '02), pp. 986–991, May 2002. View at Scopus
  8. Y.-C. Huang, “A new data mining approach to dissolved gas analysis of oil-insulated power apparatus,” IEEE Transactions on Power Delivery, vol. 18, no. 4, pp. 1257–1261, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Chen, C. Pan, Y. Yun, and Y. Liu, “Wavelet networks in power transformers diagnosis using dissolved gas analysis,” IEEE Transactions on Power Delivery, vol. 24, no. 1, pp. 187–194, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. H. B. Zheng, R. J. Liao, S. Grzybowski, and L. J. Yang, “Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation,” IET Electric Power Applications, vol. 5, no. 9, pp. 691–696, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. X. Hao and S. Cai-Xin, “Artificial immune network classification algorithm for fault diagnosis of power transformer,” IEEE Transactions on Power Delivery, vol. 22, no. 2, pp. 930–935, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Liu, W. Gao, and K. Tan, “Fault diagnosis of power transformer using a combinatorial neural network,” Transactions of China Electrotechnical Society, vol. 18, no. 2, pp. 83–86, 2003. View at Google Scholar · View at Scopus
  13. L. Wu, Y. Zhu, and J. Yuan, “Novel method for transformer faults integrated diagnosis based on Bayesian network classifier,” Transactions of China Electrotechnical Society, vol. 20, no. 4, pp. 45–51, 2005. View at Google Scholar · View at Scopus
  14. J. Zhang, H. Zhou, and C. Xiang, “Application of super SAB ANN model for transformer fault diagnosis,” Transactions of China Electrotechnical Society, vol. 19, no. 7, pp. 49–58, 2004. View at Google Scholar · View at Scopus
  15. Y.-Q. Wang, F.-C. Lu, and H.-M. Li, “Synthetic fault diagnosis method of power transformer based on rough set theory and bayesian network,” Proceedings of the Chinese Society of Electrical Engineering, vol. 26, no. 8, pp. 137–141, 2006. View at Google Scholar · View at Scopus
  16. D. B. Zhang, Y. Xu, and Y. N. Wang, “Neural network ensemble method and its application in DGA fault diagnosis of power transformer on the basis of active diverse learning,” Proceedings of the Chinese Society of Electrical Engineering, vol. 30, no. 22, pp. 64–70, 2010. View at Google Scholar · View at Scopus
  17. M. Dong, D. K. Xu, M. H. Li, and Z. Yan, “Fault diagnosis model for power transformer based on statistical learning theory and dissolved gas analysis,” in Proceedings of the Conference Record of the IEEE International Symposium on Electrical Insulation, pp. 85–88, September 2004. View at Publisher · View at Google Scholar
  18. D. R. Morais and J. G. Rolim, “A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil,” IEEE Transactions on Power Delivery, vol. 21, no. 2, pp. 673–680, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Shintemirov, W. Tang, and Q. H. Wu, “Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming,” IEEE Transactions on Systems, Man and Cybernetics, vol. 39, no. 1, pp. 69–79, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Naresh, V. Sharma, and M. Vashisth, “An integrated neural fuzzy approach for fault diagnosis of transformers,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 2017–2024, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Du and S. Wang, “Data enriching based on art-artificial immune network,” Pattern Recognition and Artificial Intelligence, vol. 14, no. 4, pp. 401–405, 2001. View at Google Scholar · View at Scopus
  22. L. N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, London, UK, 2002.
  23. J. Timmis, M. Neal, and J. Hunt, “An artificial immune system for data analysis,” BioSystems, vol. 55, no. 1–3, pp. 143–150, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Watkins, J. Timmis, and L. Boggess, “Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm,” Genetic Programming and Evolvable Machines, vol. 5, no. 3, pp. 291–317, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. L. N. de Castro and J. Timmis, “An artificial immune network for multimodal function optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '02), pp. 699–704, May 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. Li, J. Yuan, and L. Zhang, “Fault diagnosis for power transformer based on the self-organization antibody net,” Transactions of China Electrotechnical Society, vol. 25, no. 10, pp. 200–206, 2010. View at Google Scholar · View at Scopus
  27. W. Lei and J. Licheng, “Immune evolutionary algorithm,” in Proceedings of the 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99), pp. 99–102, September 1999. View at Scopus
  28. T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics, vol. 3, pp. 1–27, 1974. View at Google Scholar · View at MathSciNet
  29. R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society B: Statistical Methodology, vol. 63, no. 2, pp. 411–423, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. S. Dudoit and J. Fridlyand, “A prediction-based resampling method for estimating the number of clusters in a dataset,” Genome Biology, vol. 3, no. 7, pp. 1–21, 2002. View at Google Scholar · View at Scopus
  31. C. Ding and X. He, “K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization,” in Proceedings of the ACM Symposium on Applied Computing, pp. 584–589, Nicosia, Cyprus, March 2004. View at Scopus
  32. T. K. Ho and E. M. Kleinberg, “Building projectable classifiers of arbitrary complexity,” in Proceedings of the 13th International Conference on Pattern Recognition (ICPR '96), pp. 880–885, August 1996. View at Publisher · View at Google Scholar · View at Scopus
  33. K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2013.