Fuzzy ART neural network algorithm for classifying the power system faults

被引：79

作者：

Vasilic, S ^{[1
]}

Kezunovic, M ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2005年 / 20卷 / 02期

基金：

美国安德鲁·梅隆基金会;

关键词：

adaptive resonance theory; clustering methods; fuzzy logic; learning systems; neural networks; pattern classification; power system faults; protective relaying; testing; training;

D O I：

10.1109/TPWRD.2004.834676

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：

This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.

引用

页码：1306 / 1314

页数：9

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