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Mathematical Problems in Engineering
Volume 2017, Article ID 9670290, 6 pages
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; moc.621@6021naygnaw

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.


The fault diagnosis method based on dissolved gas analysis (DGA) is of great significance to detect the potential faults of the transformer and improve the security of the power system. The DGA data of transformer in smart grid have the characteristics of large quantity, multiple types, and low value density. In view of DGA big data’s characteristics, the paper first proposes a new combined fault diagnosis method for transformer, in which a variety of fault diagnosis models are used to make a preliminary diagnosis, and then the support vector machine is used to make the second diagnosis. The method adopts the intelligent complementary and blending thought, which overcomes the shortcomings of single diagnosis model in transformer fault diagnosis, and improves the diagnostic accuracy and the scope of application of the model. Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment.