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Author Momoh, J.A. ♦ Dias, L.G. ♦ Laird, D.N.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1996
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Fault diagnosis ♦ Impedance ♦ Artificial intelligence ♦ Artificial neural networks ♦ Fault detection ♦ Conductors ♦ Packaging ♦ Electrical fault detection ♦ Testing ♦ Fault location
Abstract The common fault in distribution systems due to line outages consists of single-line-to-ground (SLG) faults, with low or high fault impedance. The presence of arcing is commonplace in high impedance SLG faults. Artificial intelligence (AI) based techniques have been introduced for low/high impedance fault diagnosis in ungrounded distribution systems and high impedance fault diagnosis in grounded distribution systems. So far no tool has been developed to identify, locate and classify faults on grounded and fault distribution. This paper describes an integrated package for fault diagnosis in either grounded or ungrounded distribution systems. It utilizes rule based schemes as well as artificial neural networks (ANN) to detect, classify and locate faults. Its application on sample test data as well as field test data are reported in the paper.
Description Author affiliation: Dept. of Electr. Eng., Howard Univ., Washington, DC, USA (Momoh, J.A.)
ISBN 0780335228
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-09-15
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 661.28 kB
Page Count 6
Starting Page 123
Ending Page 128

Source: IEEE Xplore Digital Library