Study on Fault Diagnosis Method of Transformer Using Multi-neural Network and Evidence Theory

被引：0

作者：

Wang, Wei ^{[1
]}

Zhang, Na ^{[1
]}

Liu, Xing-Ting ^{[1
]}

Han, Yu ^{[1
]}

Tao, Wen-Biao ^{[1
]}

机构：

[1] State Grid Shanxi Elect Power Res Inst, Taiyuan, Shanxi, Peoples R China

来源：

2017 2ND IEEE INTERNATIONAL CONFERENCE ON INTEGRATED CIRCUITS AND MICROSYSTEMS (ICICM) | 2017年

关键词：

transformer; multi-neural network; evidence theory; synthetic diagnosis;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In order to solve the problems of power transformer such as the fault can be reflected by different characteristic signal from different side and complexity of fault reason and phenomenon, a synthetic diagnosis method using multi-neural network and evidence theory for transformer fault diagnosis is presented. Various kinds of data are dealt by using neural network's excellent abilities of learning, memory and recognition. Integrating data fusion methods, neural network's preliminary results are diagnosed by evidence theory. It has been shown by experiments that the accuracy rate of transformer fault diagnosis is up to 73%.

引用

页码：218 / 222

页数：5

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