Diagnóstico automático de eventos e distúrbios em unidades geradoras e usinas utilizando lógica fuzzy

• Ano de defesa: 2015
• Autor(a) principal: Cieslak, Dionatan Augusto Guimarães
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Pato Branco; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Sistemas difusos; Lógica difusa; Sistemas de controle digital; Fuzzy Systems; Fuzzy logic; Digital control systems; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/1755

The electric power systems are getting more complex and covering larger areas day by day. This fact has been contribuiting to the development of monitoring techniques that aim to help the analysis, control and planning of power systems. Supervisory Control and Data Acquisition (SCADA) systems, Wide Area Measurement Systems and disturbance record systems. Unlike SCADA and WAMS, disturbance record systems are mainly used for offilne analysis in occurrences where a fault resulted in tripping of and apparatus such as a transimission line, transformer, generator and so on. The device responsible for record the disturbances is called Digital Fault Recorder (DFR) and records, basically, electrical quantities as voltage and currents and also, records digital information from protection system devices. Generally, in power plants, all the DFRs data are centralized in the utility data centre and it results in an excess of data that difficults the task of analysis by the specialist engineers. This dissertation shows a new methodology for automated analysis of disturbances in power plants. A fuzzy reasoning system is proposed to deal with the data from the DFRs. The objective of the system is to help the engineer resposnible for the analysis of the DFRs’s information by means of a pre-classification of data. For that, the fuzzy system is responsible for generating unit operational state diagnosis and fault classification.