Fault Diagnosis Model of Power Transformer Based on an Improved Binary Tree and the Choice of the Optimum Parameters of Multi-class SVM

摘要

An improved binary tree algorithm is proposed for the practical problem of the relativity position of the data sets for oil-immersed transformer in the pattern feature space. And a fault diagnosis model of Dissolved Gas Analysis (DGA) based on an improved binary tree multi-class support vector machine (SVM) is constructed. This method overcomes the disadvantage that the traditional binary tree, which doesn't consider the distributing situation of the data sets, constructs directly the SVM classifier. At the same time, the two-divided method presented by the paper is applied in the choice of the optimal parameters of SVM. The experiment is performed and this method acquires a better performance.