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

被引：4
|
作者：
Liu, Tao [1 ,2 ]
Wang, Zhijie [3 ]
机构：
[1] Suzhou Vocat Univ, Elect & Informat Engn Dept, 1158 Yuehu Rd, Suzhou, Peoples R China
[2] Jiangsu Res & Dev Ctr Modern Enterprise Informat, Suzhou, Peoples R China
[3] Shanghai Dianji Univ, Shanghai, Peoples R China
来源：
2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION | 2009年
关键词：
power transformer; fault diagnosis; support vector machines; model;
D O I：
10.1109/IUCE.2009.59
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：137 / +
页数：2
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