Estimação fasorial aplicada a relés de proteção numéricos utilizando os métodos de ajuste de curvas e redes neurais artificiais

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Silva, Chrystian Dalla Lana da
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
BR
Engenharia Elétrica
UFSM
Programa de Pós-Graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufsm.br/handle/1/8536
Resumo: This dissertation proposes two methodologies for the phasor estimation on protective relays in Electrical Power Systems. Firstly, a theoretical introduction of signal processing, the structure of a protective relay and phasor estimation algorithms is presented, including some of the algorithms used on the electrical system, as well as the two proposed methodologies. The first one makes use of the concept of curve-fitting, while the other uses Artificial Neural Networks, both with the goal of performing the real-time estimation of the signal amplitude and phase angle. Secondly, it is made a comparative analysis of the two proposed methods with four well-known and currently used algorithms. This comparison is made through the creation of several test signals with different simulation parameters. From these simulations, six performance indexes are used for the quantitative evaluation of each algorithm, from where the most effective method can be determined through the arithmetic mean of these indexes. Lastly, after all the simulation cases have been presented, a summary of the characteristics of each algorithm is made, based in their numerical results. Then, based on the values obtained on each performance index, the strong and weak points are highlighted, as well as the general classification of each method.