An implementation of a hybrid intelligent tool for distribution system fault diagnosis

Author

Momoh, J.A. ; Dias, L.G. ; Laird, D.N.

Author_Institution

Dept. of Electr. Eng., Howard Univ., Washington, DC, USA

Volume

12

Issue

2

fYear

1997

fDate

4/1/1997 12:00:00 AM

Firstpage

1035

Lastpage

1040

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. Recently, 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 ungrounded systems. 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

Keywords

diagnostic expert systems; distribution networks; earthing; fault location; neural nets; power system analysis computing; artificial intelligence techniques; artificial neural networks; computer simulation; distribution system fault diagnosis; fault classification; hybrid intelligent tool; rule-based schemes; single-line-to-ground faults; Artificial intelligence; Artificial neural networks; Conductors; Electrical fault detection; Fault detection; Fault diagnosis; Fault location; Impedance; Packaging; Testing;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.584434

Filename

584434