SILVA, K. M.; http://lattes.cnpq.br/8795500242651581; SILVA, Kleber Melo e.
Résumé:
A method based on artificial neural networks and wavelet transforms for fault detection and
classification in transmission lines is proposed. The analysis is accomplished in current and
voltage waveforms obtained from digital fault recorders. The detection step and the fault
interval are achieved by means of a set of rules obtained from the current waveform analysis
in the time and wavelet domains. In this step, a fault is distinguished from power quality
disturbances such as voltage sags and switching transients. In the case of fault, its classification
is accomplished by a neural network, responsible for voltage and current waveforms pattern
recognition in time domain. The method has been evaluated for real and simulated faults in
transmission lines of CHESF's transmission system, good results were obtained in both cases.