Services on Demand
Sba: Controle & Automação Sociedade Brasileira de Automatica
Print version ISSN 0103-1759
MELO E SILVA, Kleber et al. Detecção e classificação de faltas a partir da análise de registros oscilográficos via redes neurais artificiais e transformada wavelet. Sba Controle & Automação [online]. 2007, vol.18, n.2, pp. 163-172. ISSN 0103-1759. http://dx.doi.org/10.1590/S0103-17592007000200003.
This paper presents a method for fault detection and classification in transmission lines, based on analysis of oscillographic data using artificial neural networks and wavelet transform. The fault detection and its clearing time are determined based on a set of heuristic rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power quality disturbances such as voltage sags and oscillatory transients, which are common in power systems operation. An ANN classifies the fault by the voltage and current waveforms pattern recognition in time domain. The method was used for fault detection and classification from both simulated and real oscillographic data of Chesf, a Brazilian utility company, with excellent results.
Keywords : Artificial neural networks; wavelet transform; fault detection and classification in transmission lines.