Detecção e classificação de faltas em linhas de transmissão utilizando análise funcional e inteligência computacional

• Ano de defesa: 2011
• Autor(a) principal: Andre de Souza Gomes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Classificação de faltas; Detecção de faltas; Linha de transmissão; Engenharia elétrica; Energia eletrica Transmissão
• Link de acesso: http://hdl.handle.net/1843/BUOS-99GHSW

The transmission line is the most vulnerable element of the Electric Power System due to its great physical dimension. Several fault diagnosis algorithms have been proposed in the literature, especially methods that use signal analysis and computational intelligence. This work proposes a new modeling that functionally represents the transmission line phases. In the proposed model, the nominal behavior of voltage and current signals are projected onto a two-dimensional space, represented by a conic equation. The detection and classification strategy is developed from the analysis of the models parameters. The proposed methodology is evaluated using a set of simulated faults, obtained by varying the fault parameters. The results are satisfactory and demonstrate the viability of the proposed methodology to detect the moment of failure and your classification.