Research on Transformer Fault Diagnosis Method Based on Rough Set Optimization BP Neural Network

被引：0
|
作者：
Zhang, Xin [1 ]
Zhu, Mingzheng [1 ]
Zhu, Xuliang [1 ]
Yao, Chuang [1 ]
Wen, Qingfeng [1 ]
Duan, Minghui [1 ]
机构：
[1] State Grid Tianjin Elect Power Corp, Elect Power Res Inst, Tianjin, Peoples R China
来源：
2018 2ND IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2) | 2018年
关键词：
transformer; fault diagnosis; rough set; BP neural network;
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：
The intelligent transformer fault diagnosis method can timely and accurately discover the equipment fault, which plays an important role in the safe operation of the transformer. This paper analyzes the research status of transformer fault diagnosis, and creatively introduces rough set theory and BP neural network into the field of transformer fault diagnosis. The rough set is used to filter and clear the redundant data as the pre-component, and effective data is normalized and processed. Then BP neural network is trained by sample data and a new type of transformer fault diagnosis method is established. The effectiveness and accuracy of the proposed method are verified by practical examples.
引用
收藏
页数：5
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