Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 9670290, 6 pages
https://doi.org/10.1155/2017/9670290
Research Article

A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

1Department of Computer, North China Electric Power University, Baoding, China
2College of Information Science & Technology, Agricultural University of Hebei, Baoding, China

Correspondence should be addressed to Yan Wang

Received 8 December 2016; Accepted 27 March 2017; Published 18 May 2017

Academic Editor: Yaguo Lei

Copyright © 2017 Yan Wang and Liguo 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. Alves, D. Martins, U. Bezerra, and A. Klautau, “A Hybrid approach for big data outlier detection from electric power SCADA system,” IEEE Latin America Transactions, vol. 15, no. 1, pp. 57–64, 2017. View at Publisher · View at Google Scholar
  2. J. Lang, S. Pascoe, J. Thompson, J. Woyak, K. Rahimi, and R. Broadwater, “Smart grid big data: automating analysis of distribution systems,” in Proceedings of the 60th Annual IEEE Rural Electric Power Conference, (REPC '16), pp. 96–101, usa, May 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Abu-Siada and S. Hmood, “A new fuzzy logic approach to identify power transformer criticality using dissolved gas-in-oil analysis,” International Journal of Electrical Power and Energy Systems, vol. 67, pp. 401–408, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Liao, H. Zheng, S. Grzybowski, L. Yang, Y. Zhang, and Y. Liao, “An integrated decision-making model for condition assessment of power transformers using fuzzy approach and evidential reasoning,” IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 1111–1118, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Yuyun, Research on Intelligent Fault Diagnosis System for transformer based on DGA Technology [Ph.D. thesis], Wuhan University, China, 2004.
  6. K. Bacha, S. Souahlia, and M. Gossa, “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine,” Electric Power Systems Research, vol. 83, no. 1, pp. 73–79, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. L. V. Ganyun, C. Haozhong, Z. Haibao et al., “Fault diagnosis of power transformer based on multi-layer SVM classifier,” Electric Power Systems Research, vol. 74, no. 1, pp. 1–7, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Jianbai, The application research of Support Vector Machines in transformer fault diagnosis [MA. Thesis], North China Electric Power University, 2007.
  9. C. Wei, W. Tang, and Q. Wu, “Dissolved gas analysis method based on novel feature prioritisation and support vector machine,” IET Electric Power Applications, vol. 8, no. 8, pp. 320–328, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. H. A. Illias, X. R. Chai, A. H. A. Bakar, and H. Mokhlis, “Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques,” PLoS ONE, vol. 10, no. 6, Article ID e0129363, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Jun and H. Junjia, “Application of quantum genetic ANNs in transformer dissolved gas-in-oil analysis,” in Proceedings of the CSEE, vol. 30, pp. 121–127, 2010.
  12. H. Malik, A. K. Yadav, S. Mishra, and T. Mehto, “Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil- immersed power transformer,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 256–271, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Bhalla, R. K. Bansal, and H. O. Gupta, “Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis,” International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 1196–1203, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. W. H. Tang, Z. Lu, and Q. H. Wu, “A bayesian network approach to power system asset management for transformer dissolved gas analysis,” in Proceedings of 3rd International Conference on Deregulation and Restructuring and Power Technologies, (DRPT '08), pp. 1460–1466, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. W. H. Tang, J. Y. Goulermas, Q. H. Wu, Z. J. Richardson, and J. Fitch, “A probabilistic classifier for transformer dissolved gas analysis with a particle swarm optimizer,” IEEE Transactions on Power Delivery, vol. 23, no. 2, pp. 751–759, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. 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 Part C-Applications And Reviews, vol. 39, no. 1, pp. 69–79, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Abu-Siada and S. Islam, “A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 19, no. 3, pp. 1007–1012, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Wu, Z. Dong, G. Yu, and Y. Zhu, “Transformer fault diagnosis based on factor analysis and gene expression programming,” Journal of North China Electric Power University, vol. 39, no. 4, pp. 47–51, 2012. View at Publisher · View at Google Scholar
  19. N. Bakar, A. Abu-Siada, and S. Islam, “A review of dissolved gas analysis measurement and interpretation techniques,” IEEE Electrical Insulation Magazine, vol. 30, no. 3, pp. 39–49, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Bhalla, R. K. Bansal, and H. O. Gupta, “Integrating AI based DGA fault diagnosis using dempster-shafer theory,” International Journal of Electrical Power & Energy Systems, vol. 48, no. 1, pp. 31–38, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Yang, W. H. Tang, A. Shintemirov, and Q. H. Wu, “Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 39, no. 6, pp. 597–610, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. D.-E. A. Mansour, “Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, no. 5, pp. 2507–2512, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Zuo, H. Yuan, Y. Shang, Y. Liu, and T. Chen, “Calculation of a health index of oil-paper transformers insulation with binary logistic regression,” Mathematical Problems in Engineering, vol. 2016, Article ID 6069784, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. X. Shi, Y. Zhu, X. Ning, L. Wang, G. Sun, and G. Chen, “Transformer fault diagnosis based on deep auto-encoder network,” Electric Power Automation Equipment, vol. 36, no. 5, pp. 122–126, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Shi, Y. Zhu, Sa C., Wang L., and Sun G., “Power transformer fault classifying model based on deep belief network,” Power System Protection and Control, vol. 44, no. 1, pp. 71–76, 2016. View at Google Scholar
  26. P. Tamilselvan and P. F. Wang, “Failure diagnosis using deep belief learning based health state classfication,” Reliability Engineering & System Safety, vol. 115, pp. 124–135, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. Q. M. Cui, K. Liang, H. Y. Gao et al., “Research of the transformer fault diagnosis expert system based on esta and deep learning neural network programmed in MATLAB,” in Proceedings of the 2016 International Conference on Civil, Transportation and Environment, pp. 772–778, 2016.
  28. Z.-L. Wu, N. Yuan, Z. Gong, and Y.-L. Zhu, “Improved matter-element model based on cloud model and its application in power transformers fault diagnosis,” in Proceedings of the International Conference on Electronics, Communications and Control, ICECC 2011, pp. 3758–3762, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. “Spark:Choose your real-time weapon [EB/OL],” 2014 http://www.infoworld.com/article/2854894/application-development/spark-and-storm-for-real-time-computation.html.
  30. L. Maoliu and C. Chunyu, “A performance comparison of SVMs based on Fourier kernel and RBF kernel,” Journal of Chongqing University of Posts and Telecommunications (Natural Science), vol. 17, no. 6, pp. 647–650, 2005. View at Google Scholar
  31. M. Shunan, Research on Transformer Remote Faults Diagnosis Based on Support Vector Machine [MA. Thesis], Beijing Jiaotong University, 2010.
  32. W. Youyuan, Study on Prediction Models of Power Transformer Fault Based on Genetic Algorithm [MA. Thesis], Chongqing University, 2003.