Use este identificador para citar ou linkar para este item: http://repositorio.utfpr.edu.br/jspui/handle/1/654
Título: Autonomous neural models for the classification of events in power distribution networks
Autor(es): Lazzaretti, Andre Eugênio
Ferreira, Vitor Hugo
Vieira Neto, Hugo
Riella, Rodrigo Jardim
Omori, Julio Shigeaki
Palavras-chave: Energia elétrica - Distribuição
Redes neurais (Computação)
Visão por computador
Wavelets (Matemática)
ATP (Programa de computador)
Electric power distribution
Neural networks (Computer science)
Computer vision
Wavelets (Mathematics)
ATP (Computer program)
Data do documento: Out-2013
Câmpus: Curitiba
Citação: LAZZARETTI, André Eugênio et al. Autonomous neural models for the classification of events in power distribution networks. Journal of Control, Automation and Electrical Systems, v. 24, n. 5, p. 612-622, out. 2013. Disponível em: <http://link.springer.com/article/10.1007%2Fs40313-013-0064-8>. Acesso em: 11 nov. 2013.
Abstract: This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: multilayer perceptrons, radial basis function networks, and support vector machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program (ATP). Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented.
URI: http://repositorio.utfpr.edu.br/jspui/handle/1/654
ISSN: 2195-3899
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