Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine

Abstract

Support Vector Machines (SVM) is a machine-learning algorithm based on statistical learning theory. The method for power transformer fault diagnosis based on SVM is proposed in this paper. The principle and algorithm of this method are introduced. Through a finite learning sample the relation is established between the transformer fault signature and the quantity of its dissolved gas. A faults classifier is constructed by using the dissolved gas data of the fault transformer. The testing results show that this method can successfully be applied to the diagnosis of gear faults.