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Mathematical Problems in Engineering
Volume 2013, Article ID 636374, 6 pages
Research Article

Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine

1College of Information and Telecommunication, Harbin Engineering University, Harbin 150001, China
2Department of Electronic and Electrical Engineering, Huaiyin Institute of Technology, Huai’an 223001, China

Received 13 September 2013; Accepted 15 October 2013

Academic Editor: Zhiguang Feng

Copyright © 2013 Lutao Liu and Zujun Ding. 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.


A new method of transformer fault diagnosis based on relevance vector machine (RVM) is proposed. Bayesian estimation is applied to support vector machine (SVM) in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP) neural networks and general SVM methods.