Localização de falhas em linhas de transmissão utilizando decomposição harmônica e redes neurais artificiais

• Ano de defesa: 2006
• Autor(a) principal: GOMES, Cristiane Ruiz
• Orientador(a): VIEIRA JÚNIOR, Petrônio
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Pará
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Elétrica
• Departamento: Instituto de Tecnologia
• País: Brasil
• Palavras-chave em Português: Potência elétrica; Linha de transmissão (Energia); Sistemas de telecomunicação; Localização de falhas (Engenharia); Energia elétrica
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES::TEORIA ELETROMAGNETICA, MICROONDAS, PROPAGACAO DE ONDAS, ANTENAS; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES::SISTEMAS DE TELECOMUNICACOES
• Link de acesso: http://www.repositorio.ufpa.br:8080/jspui/handle/2011/1645

This work proposes a new methodology for fails location in transmission lines. This methodology consists in using harmonic decomposition of the leakage current and in the application of an Artificial Neural Network (ANN), this is able to recognize patterns of normal and fails conditions of a transmission line. It was developed a Pi model capable to use real data of voltage and current of the three phases. In this model values of capacitance, inductance and resistance can be modified in agreement of weather conditions. Fails were generated in all the towers with different values of capacitance. The input/output data were used to train the neural network. The real voltage and current data acquisition were done by instruments installed in the two terminals of the Guamá-Utinga transmission line belonging to Centrais Elétricas do Norte - ELETRONORTE. The computation of the parameters was made by the well known matricial method and was improved by Finite Element Method. An ANN was developed with Matlab software. For training the ANN it was used the backpropagation resilient algorithm, which presented good performance, been fast and accurate. The ANN was trained by two different sets to analyze differences between outputs. In the two cases results were satisfactory.