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 doesnt 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.

关键词： Fault diagnosis improved SVM binary tree two-divided

作者: Xiaoyun Sun Guoqing An Ping Fu Jianpeng Bian

作者单位: School of Electrical Engineering and Information Science Hebei University of Science & Technology Sh Chongqing University Chongqing,China,400030

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 3096-3100

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）