Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer

Abstract

This paper proposes the proper kernel function in a support vector machine (SVM), which is used to classify the internal fault type in power transformer. The Gaussian kernel and polynomial kernel that are two types of non-linear kernel parameter are compared in terms of the average accuracy and time of training process. The results are shown that the polynomial kernel parameter of SVM algorithm is able to classify the internal fault type with satisfactory accuracy, and it takes the average time less than the other. The benefit of using polynomial kernel can be applied for fault diagnosis in future.