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

被引：1
|
作者：
Sun, Xiaoyun [1 ]
An, Guoqing [1 ]
Fu, Ping [1 ]
Bian, Jianpeng [2 ]
机构：
[1] Hebei Univ Sci & Technol, Sch Elect Engn & Informat Sci, Shijiazhuang 050018, Peoples R China
[2] Chongqing Univ, Chongqing 400030, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 4 | 2009年
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; improved SVM binary tree; two-divided;
D O I：
10.1109/ICICISYS.2009.5357614
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：567 / +
页数：2
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