Processador inteligente de alarmes e modelos de programação matemática para diagnóstico de faltas em sistemas elétricos de potência

• Ano de defesa: 2016
• Autor(a) principal: Oliveira, Aécio de Lima
• Tipo de documento: Tese
• 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
• Palavras-chave em Português: Diagnóstico de faltas; Estimação da seção em falta; Processamento inteligente de alarmes; Proteção de sistemas elétricos; Fault diagnosis; Fault section estimation; Intelligent alarm processing; Power system protection; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/3699

This thesis proposes an Intelligent Alarm Processor for fault diagnosis in electrical power systems. The objective is to develop a methodology for automatic fault analysis using reported alarms from Supervisory Control and Data Acquisition (SCADA) to allow the use of diagnosis systems in large power systems. The proposal can be used in real-time decision support systems to assist control center‟s operators during the decision-making after unscheduled contingencies with relevant information to power system restoration. This work expects to contribute to the development of advanced alarm management logics that allow modifying the chronological sequence of reported alarms, event mapping and the generation of operating patterns of protection systems according to topology network. Still, mathematical programming models have been formulated as a parsimonious set covering problem to fault section estimation and identification of protective devices with improper operation. Among these models, it stands out the model that deals with integrated analysis of reported alarms, events and diagnosis that better explain the alarms. The proposed approach has been tested in different portions of the Southern Brazilian power system. The results show that alarm processing allows the practical implementation of intelligent diagnosis methods in existing supervisory systems. The proposed diagnosis methods show better performance and accurate solutions than other methods presented in literature.