Modeling analysis of power transformer fault diagnosis based on improved relevance vector machine

Abstract

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. © 2013 Lutao Liu and Zujun Ding.