A Refined Classification Method for Transformer Fault Diagnosis

Abstract Dissolved gas analysis is one of the most effective methods for diagnosing transformer faults. The traditional method for oil-immersed transformer fault diagnosis can only recognize several types of defects and has a low accuracy rate. In order to improve the classification effectiveness, a refined classification method for transformer fault diagnosis is proposed. It can detect more types of faults with a higher accuracy rate. The proposed method is based on the probability-output relevance vector machine, and a three-layer four-classifier model is constructed to analyse the different diagnostic results of different kinds of input data. In this model, a binomial tree is used to transfer the multi-classification problem to four binary classification problems; each classifier is a binary classifier used to distinguish the transformer type between two types of error. The proposed method is employed for analysis of 100 DGA samples consisting of characteristic gas content. The experimental result shows that this method has a high diagnostic rate and can diagnose 11 kinds of operation state.