Comparison of artificial neural networks and conventional algorithms in ground fault distance computation

Author

Eberl, G. ; Hänninen, S. ; Lehtonen, M. ; Schegner, P.

Author_Institution

Tech. Univ. Dresden, Germany

Volume

3

fYear

2000

fDate

23-27 Jan 2000

Firstpage

1991

Abstract

This paper gives a comparison between an artificial neural network method and a differential equation algorithm and wavelet algorithm in transient based earth fault location in the 20 kV radial power distribution networks. The items discussed are earth fault transients. Signal pre-processing and the performance of the proposed distance estimation methods. The networks considered are either unearthed or resonant earthed. The comparison showed that the neural network algorithm was better than the conventional algorithms in the case of very low fault resistance. The mean error in fault location was about 1 km in the field tests using staged faults, which were recorded in real power systems. With higher fault resistances, the conventional algorithms worked better

Keywords

earthing; fault location; learning (artificial intelligence); neural nets; power distribution faults; power system analysis computing; wavelet transforms; 20 kV; artificial neural networks; differential equation algorithm; distance estimation methods; earth fault transients; fault location; ground fault distance computation; mean error; radial power distribution networks; resonant earthed networks; signal pre-processing; staged faults; training data; transient based earth fault location; unearthed networks; very low fault resistance; wavelet algorithm; Artificial neural networks; Differential equations; Earth; Fault location; Neural networks; Power system faults; Power system transients; Power systems; Resonance; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society Winter Meeting, 2000. IEEE

Print_ISBN

0-7803-5935-6

Type

conf

DOI

10.1109/PESW.2000.847659

Filename

847659