Comparison between backpropagation and RPROP algorithms applied to fault classification in transmission lines

Author

Souza, Benemar A. ; Brito, NúS D. ; Neves, Washington L A ; Silva, Kleber M. ; Lima, Ricardo B V ; da Silva, S.S.B.

Author_Institution

Dept. of Electr. Eng., Fed. Univ. of Campina Grande, Brazil

Volume

4

fYear

2004

fDate

25-29 July 2004

Firstpage

2913

Abstract

The computed results from implemented artificial intelligence algorithms, used to identify and classify faults in transmission lines, are discussed in this paper. The proposed methodology uses sampled data of voltage and current waveforms obtained from analog channels of digital fault recorders (DFRs) installed in the field to monitor transmission lines. The performances of resilient propagation (RPROP) and backpropagation algorithms, implemented in batch mode, are addressed for single, double and three-phase fault types.

Keywords

artificial intelligence; backpropagation; condition monitoring; fault diagnosis; fault tolerant computing; power engineering computing; power transmission lines; artificial intelligence algorithm; backpropagation; digital fault recorder; resilient propagation; transmission line fault; Artificial intelligence; Artificial neural networks; Backpropagation algorithms; Electronic mail; Fault diagnosis; Monitoring; Performance analysis; Power transmission lines; Transmission lines; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1381126

Filename

1381126