Análise de Ocorrências em Transformadores do SDEE usando Redes Neurais Artificiais MLP.

• Ano de defesa: 2012
• Autor(a) principal: NINA, Diogo Luis Figueiredo
• 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: Automação de Subestações; Faltas em Subestações; SDEE; Rede Neural Artificial; Sistema de apoio à decisão; Sistema SCADA
• Palavras-chave em Inglês: Substations Automation; Faults on Substations; SDEE; Artificial Neural Network; Decision Support System; SCADA System
• Área do conhecimento CNPq: Sistemas de Informação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1863

Power system operation and maintenance require attention, precise diagnostics on failure and agility on system recovery. On the other hand, power systems involve high risks, where each operation needs to be carefully planned and executed, once errors can be fatal. Power system satisfactory operation and maintenance consist on finding equilibrium between these extremes, acting on a cautious, but agile, way. For this purpose, we propose the development of an intelligent system with the ability of detecting abnormal patterns on the electrical signal, providing support for decisions on Power Distribution System real time operation, from the analysis of power substation transformers primary and secondary currents, including learning at each new information acquired by the system. The challenge of this study is to research and develop a method based on ANN for classifying patterns and providing support for decisions, aiming fault detection and/or fault recovery. The method di↵erentiates disturbances that will lead to faults from disturbances generated by transients on power system (for example an undervoltage caused by powering on an engine). A SCADA supervisory system was developed to contain ANN implementation code and also to provide an interface for Operators, generating visual and sound alarms and messages guiding system recovery. The proposed method was evaluated using real data collected from transformers protection digital relays of CEMAR system substations, achieving excellent results. The ANN developed on this study presented satisfactory performance classifying signals and detecting faults properly.