Development of a fuzzy inference system based on genetic algorithm for high-impedance fault detection

Author

Haghifam, M. -R ; Sedighi, A.-R. ; Malik, O.P.

Author_Institution

Dept. of Electr. Eng., Tarbiat Modares Univ., Tehran, Iran

Volume

153

Issue

3

fYear

2006

fDate

5/11/2006 12:00:00 AM

Firstpage

359

Lastpage

367

Abstract

A novel method for high-impedance fault (HIF) detection in distribution systems is presented. Using this method HIFs can be discriminated from isolator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no-load line switching. Wavelet transform and principal component analysis are used for feature extraction/selection. A fuzzy inference system is implemented for fault classification and a genetic algorithm is applied for input membership functions adjustment. HIF and ILC data was acquired from experimental tests and the data for other transients was obtained by simulation of a real 20 kV distribution feeder using EMTP. Results show that the proposed procedure is efficient in identifying HIFs from other events.

Keywords

EMTP; capacitor switching; fault location; feature extraction; fuzzy systems; genetic algorithms; inference mechanisms; leakage currents; power distribution faults; power system analysis computing; power system transients; principal component analysis; wavelet transforms; 20 kV; EMTP; capacitor switching; distribution feeder system; feature extraction; fuzzy inference system; genetic algorithm; ground fault; high-impedance fault detection; inrush current; isolator leakage current; load switching; principal component analysis; transients; wavelet transform;

fLanguage

English

Journal_Title

Generation, Transmission and Distribution, IEE Proceedings-

Publisher

iet

ISSN

1350-2360

Type

jour

DOI

10.1049/ip-gtd:20045224

Filename

1629542