Fault diagnosis of power electronic device based on wavelet and neural network

被引：0

作者：

Fu, Lijun ^{[1
]}

Yang, Qing ^{[1
]}

Wang, Guangxing ^{[1
]}

Ren, Huixuan ^{[1
]}

机构：

[1] Shenyang Ligong Univ, Sch Automat & Elect Engn, Shenyang 110159, Peoples R China

来源：

PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年

关键词：

PSO network; fault diagnosis; wavelet packet; fault feature;

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：

A novel method for fault diagnosis based on wavelet and neural network of three-phase bridge inverter circuit is proposed in this paper. In the proposed method, the wavelet packet is used to decompose the fault signal and extract fault characteristics effectively. BP neural network and PSO-optimized neural network are perform to the circuit fault diagnoses separately and the results of both method are compared. Finally, the proposed method is modeled and simulated on the MATLAB/Simulink platform. The result shows that the PSO-BP neural network has faster convergence speed and higher diagnostic accuracy and is more effective in fault diagnosis of power electronic circuits.

引用

页码：2946 / 2950

页数：5

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