Transformer Fault Position Recognition Based on Probability Support Vector Machine

被引：0

作者：

Huang Song-bo ^{[1
]}

Zhao Wei-min ^{[1
]}

Zhang Tao ^{[1
]}

Sima Li-ping ^{[2
]}

Wang Bo ^{[2
]}

Shu Nai-qiu ^{[2
]}

机构：

[1] Guangdong Power Grid Corp, Foshan Power Supply Bur, Foshan, Peoples R China

[2] Wuhan Univ, Sch Elect Engn, Wuhan, Peoples R China

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC & MECHANICAL ENGINEERING AND INFORMATION TECHNOLOGY (EMEIT-2012) | 2012年 / 23卷

关键词：

transformer; fault recognition; fault position; SVM; posterior probability;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Based on posterior probability of support vector machine, the analysis of dissolved gases in oil and the data of electrical tests are used comprehensively to recognize internal fault positions of the power transformer. This method not only inherits the advantages of the support vector machine for small samples, strong generalization ability and so on, but also provides the fault information of the power transformer by the form of probability. The output results not only provide the breaking down probability of the inner winding, tap changer and leads, iron core, structure and magnetic shielding body, insulating barrier and other parts, but also express the degree of credibility of conclusions. It adapts to the uncertainty characteristics of the fault positions. After analysis of examples, the validity of the model is verified.

引用

页数：4

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