Synthesized Diagnosis on Transformer Fault Based on Principle Component Analysisand Support Vector Machine

被引：0
|
作者：
Li, Tangbing [1 ]
Wang, Peng [1 ]
Zhou, Qiukuan [1 ]
Liu, Yuting [1 ]
机构：
[1] Jiangxi Elect Power Testing & Res Inst, Nanchang, Jiangxi, Peoples R China
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON HIGH VOLTAGE ENGINEERING AND APPLICATION (ICHVE) | 2016年
关键词：
Principle Component Analysis; Support Vector Machine; Transformer; Fault Diagnosis;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A synthesized method is presented in this paper for transformer fault diagnosis, This model combines the principal component analysis and the support vector machine. Firstly, by principal component analysis, the characteristics of the sample data are extracted, the main information is be retrieved, a new sample set is created. Then, a support vector machine model is created and the new sample set is used to train the support vector machines. This method achieves the advantages of the two algorithms. The accuracy of transformer fault diagnosis based on this method is improved when the sample information is noisy or incomplete. Experimental results show that the method is valid and feasible and has better diagnostic accuracy.
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收藏
页数：5
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