Alocação estocasticamente robusta de chaves automáticas em rede de distribuição de energia elétrica

• Ano de defesa: 2018
• Autor(a) principal: Sousa, Railson Severiano de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Elétrica; UFSM; Programa de Pós-Graduação em Engenharia Elétrica; Centro de Tecnologia
• Palavras-chave em Português: Planejamento da distribuição; Incerteza; Simulação de Monte Carlo; Alocação de chaves; Distribution planning; Uncertain; Monte Carlo simulation; Switch placement; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/16323

In distribution networks planning, proper selection of quantity and position of switches provides a significant reduction in reliability indices and operating costs. For that, historical failure data is used. However, it is a stochastic process, and future failures may diverge much from what has already occurred, causing off-programmed indices. In this sense, Monte Carlo simulation emerges as a tool to address uncertainties, creating a range of possible future scenarios. Although problem and technique are well established, few papers have dealt with them for equipment allocation problems. Thus, this dissertation proposes a robust methodology for the allocation of automatic switches in a distribution system, considering the stochastic effect of the failures. The methodology of switches allocation was based on a Genetic Algorithm that used a Logical-Structural Matrix associated to a non-sequential Monte Carlo simulation to diffuse different scenarios evaluated in its objective function. The results are presented in terms of costs considering the current Brazilian regulations. Two test systems were used. The first is widely recognized in the reliability studies, which allows the comparison of the results of this work with other future methodologies applied to the topic. This is important for the development of the field, considering some characteristics that were not addressed in this work, such as transient faults and distributed generation. The second one comprises a real system of the metropolitan region of Florianópolis, its characteristics show the suitability of the tool to large systems. To understand the future behavior of the system a sequential Monte Carlo simulation was included. Several reliability indices are predicted in terms of their mean value and confidence interval.