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The Scientific World Journal
Volume 2015, Article ID 459268, 8 pages
Research Article

A Dynamic Integrated Fault Diagnosis Method for Power Transformers

1Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2State Grid Energy Research Institute, Beijing 102209, China
3Electric Power Research Institute, CSG, Guangzhou 510080, China

Received 5 August 2014; Revised 10 December 2014; Accepted 18 December 2014

Academic Editor: Martin Riera-Guasp

Copyright © 2015 Wensheng Gao et al. 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.


In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.