DESENVOLVIMENTO DE UM SISTEMA BASEADO EM REDUNDÂNCIA ANALÍTICA E REDES NEURONAIS ARTIFICIAIS PARA RECUPERAÇÃO DE FALHAS NA INSTRUMENTAÇÃO DE SUBESTAÇÕES DE ENERGIA ELÉTRICA.

• Ano de defesa: 2012
• Autor(a) principal: LOUREIRO, Ronnie Santiago
• Orientador(a): FONSECA NETO, João Viana da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Relés Digitais; Redundância Analítica; Rede Neural Artificial e detecção e diagnostico de falhas
• Palavras-chave em Inglês: Digital Relay; Analytical Redundancy; Artificial Neural Networks; Fault Detection and Diagnosis
• Área do conhecimento CNPq: Medição, Controle, Correção e Proteção de Sistemas Elétricos de Potência
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1865

This work aims to monitor and analyze the data from the instrumentation system of a substation as a way to identify false alarms, which can result in a decision by the mistaken maintenance and operation. This project was conceived because of the need for a research and development project which is called Maintenance Management Center (MMC) whose overall objective is to assist in the maintenance of their equipment operational intervention. Data is extracted from the automation system that has digital relay protection function and measurement of the electric grid, passing through a sequence of data processing to achieve the results that will serve for the detection and diagnosis of faults. We applied methods based on quantitative model by transforming the data system of continuous variables (SVC) and qualitative data by transforming the system of discrete event (SDE) applying analytical redundancy techniques and neural networks respectively, thus aiming a simplified model for detection and diagnosis fault (DDF). The model has been designed taking into account the characteristics DDF due to its stages, thereby providing a good system failure recovery. Know filter if certain event is real or a false alarm is not an easy task, but this system will have to meet this purpose. Technological resources are used fairly consolidated in the industrial process for the integration of the solution, because the time factor and information processing are critical in the results generated by the system recovery. Another key point of this trial was to have developed a system based on experiential knowledge, because it has higher robustness in results.