Transformer Fault Diagnosis Based on Back-Propagation Neural Network Optimized by Cuckoo Search Algorithm

被引：0
|
作者：
Wang, Yan [1 ]
Zhang, Liguo [2 ]
机构：
[1] North China Elect Power Univ, Dept Comp, Baoding, Hebei, Peoples R China
[2] Univ Agr Univ Hebei, Coll Informat Sci & Technol, Baoding, Hebei, Peoples R China
来源：
CONFERENCE PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE) | 2017年
关键词：
transformer fault diagnosis; dissolved gases in oil; back-propagation neural network; cuckoo search algorithm;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The operation reliability of power transformer will influence the safety and stability of power system directly. As a widely used technique, the dissolved gas analysis in oil is an effective measure to diagnose the abnormal condition of transformer. In this paper, a new dissolved gas analysis based transformer fault diagnosis method is proposed, which use meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird's behavior to train BP in achieving fast convergence rate and to avoid local minima problem. The simulation results show that this method is effective.
引用
收藏
页码：383 / 386
页数：4
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