DocumentCode :
1394774
Title :
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
Link To Document :
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