Detect and classify faults using neural nets

Author

Kezunovic, Mladen ; Rikalo, Igor

Author_Institution

Texas A&M Univ., College Station, TX, USA

Volume

9

Issue

4

fYear

1996

fDate

10/1/1996 12:00:00 AM

Firstpage

42

Lastpage

47

Abstract

The analysis of transmission line faults is essential to the proper performance of a power system. It is required if protective relays are to take appropriate action and in monitoring the performance of relays, circuit breakers and other protective and control elements. The detection and classification of transmission line faults is a fundamental component of such fault analysis. Here, the authors describe how a neural network, trained to recognize patterns of transmission line faults, has been incorporated in a PC-based system that analyzes data files from substation digital fault recorders

Keywords

fault location; microcomputer applications; neural nets; pattern classification; power system analysis computing; power transmission lines; PC; circuit breakers; data files; fault analysis; fault classification; fault detection; neural nets; pattern recognition; relay performance; substation digital fault recorders; transmission line faults; Circuit faults; Distributed parameter circuits; Electrical fault detection; Fault detection; Neural networks; Performance analysis; Power system protection; Power system relaying; Power transmission lines; Protective relaying;

fLanguage

English

Journal_Title

Computer Applications in Power, IEEE

Publisher

ieee

ISSN

0895-0156

Type

jour

DOI

10.1109/67.539846

Filename

539846