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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.
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on (Volume:4 )
Date of Conference: 25-29 July 2004