Real-time and off-line transmission line fault classification using neural networks

被引：0

作者：

Kezunovic, M ^{[1
]}

Rikalo, I ^{[1
]}

Sobajic, DJ ^{[1
]}

机构：

[1] ELECT POWER RES INST,PALO ALTO,CA 94304

来源：

ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS | 1996年 / 4卷 / 01期

关键词：

fault classification; neural networks; supervised clustering; EMTP simulations;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with application of Neural Networks (NNs) to fault classification for both the real-time applications such as protective relaying of transmission lines and the off-line applications such as post-mortem study of fault events recorded with Digital Fault Recorders (DFRs). A supervised learning NN of the same type is utilized for both applications. It has been demonstrated that the NN approach reaches performance of the existing techniques in both application areas and yet shows some additional benefits.

引用

页码：57 / 63

页数：7