Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

Abstract

Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO) algorithm and relevance vector machine(RVM). RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.