Classification of events in distribution networks using autonomous neural models

• Autor(a) principal: Lazzaretti, Andre Eugênio
• Data de Publicação: 2009
• Outros Autores: Ferreira, Vitor Hugo, Vieira Neto, Hugo, Riella, Rodrigo Jardim, Omori, Julio Shigeaki
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UTFPR (da Universidade Tecnológica Federal do Paraná (RIUT))
• Texto Completo: http://repositorio.utfpr.edu.br/jspui/handle/1/658; http://dx.doi.org/10.1109/ISAP.2009.5352812
• topic: Energia elétrica - Distribuição; Redes neurais (Computação); Wavelets (Matemática); Electric power distribution; Neural networks (Computer science); Wavelets (Mathematics)

This paper presents a method for automatic classification of faults and events related to quality of service in electricity distribution networks. The method consists in preprocessing event oscillographies using the wavelet transform and then classifying them using autonomous neural models. In the preprocessing stage, the energy present in each sub-band of the wavelet domain is computed in order to compose input feature vectors for the classification stage. The classifiers investigated are based in Multi-Layer Perceptron (MLP) feed-forward artificial neural networks and Support Vector Machines (SVM), which automatically promote input selection and structure complexity control simultaneously. Experiments using simulated data show promising results for the proposed application.