Title :
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
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381126
