Estimação de intervalos de tempo ótimos para a inspeção e manutenção de escovas em unidades geradoras da copel

• Ano de defesa: 2020
• Autor(a) principal: Salles, Gisele Maria de Oliveira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Sistemas de Energia; UTFPR
• Palavras-chave em Português: Energia elétrica - Conservação; Confiabilidade (Engenharia) - Métodos estatísticos; Geradores hidrelétricos - Manutenção; Monte Carlo, Método de; Métodos de simulação; Companhia Paranaense de Energia; Electric power - Conservation; Reliability (Engineering) - Statistical methods; Hydroelectric generators - Maintenance; Monte Carlo method; Simulation methods; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIA; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/23564

Consumers have increased their demand for reliability and quality in energy supply. Consequently, the Ag^encia Nacional de Energia El´etrica (ANEEL) has narrowed the power quality requirements for the electric power utility companies, application of strict requirements to improve the service, and use of strong penalties whereas limits associated with requirements are exceeded. In particular, these requirements have compelled power generation companies to improve their maintenance plans in order to reduce downtimes and meet market needs. The machine is scheduled to stop more frequently due to brush measurement tasks with a period of three to four months. Based on these considerations, this work aims to develop a methodology to reduce the number of maintenance stops due to the inspection of brushes in electric power generation units. This study develops a modeling for the impact that defines the brush wear stochastic behavior on the cost of inspection and maintenance of the hydroelectric power plant generators of the Companhia Parananese de Energia (Copel). Today, for the preparation of maintenance plans, the company uses the qualitative methodology of Reliability Centered Maintenance (RCM), based on the experience of professionals. The proposed model uses a quantitative approach to assist in the preparation of maintenance plans. Results are obtained through a program coded in MATLAB, which allows the simulation of the stochastic behavior of brush wearing of the modeled assets using a sequential Monte Carlo simulation, to allow the estimation of conditional value-at-risk (CvaR) indicators associated with decisions, in order to assist on determining the optimized time to inspection and preventive maintenance.