Wavelet neural networks based recognition of swing and fault in power system

被引：0

作者：

Mao, Peng ^{[1
]}

Zhang, Zhaoning ^{[1
]}

Lin, Xiangning ^{[1
]}

Sun, Yaming ^{[1
]}

机构：

[1] Group of Dongfang Electron. Info., Yantai 264001, China

来源：

Dianli Xitong Zidonghue/Automation of Electric Power Systems | 2002年 / 26卷 / 11期

关键词：

Circuit oscillations - Electric fault currents - Neural networks - Pattern recognition - Wavelet transforms;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

All of the existing power swing blocking elements would cause, at different extent, delayed and blind elimination during the power swing. This paper presents a new type of wavelet neural networks (WNN) model with the integration of the outstanding characteristics of Wavelet transform (WT) and Neural Networks (NN), and its corresponding algorithm. Based on the WNN, a new principle for power swing block using transient signal could be designed in the distance protection devices. Theoretical analysis and lots of EMTP simulation results show that WNN after enough learning can quickly and correctly recognize the fault during power swing. Even under the unfavorable conditions, satisfactory results can be achieved. And the method has many advantages of fast computation and response, high reliability etc.

引用

页码：9 / 13