Reconfiguração e alocação de medidores em redes de distribuição

• Ano de defesa: 2020
• Autor(a) principal: RAPOSO, Antonio Adolpho Martins
• Orientador(a): RODRIGUES, Anselmo Barbosa
• Banca de defesa: RODRIGUES, Anselmo Barbosa , SILVA, Maria da Guia da , ALMEIDA, Madson Côrtes de , BEZERRA, Ubiratan Holanda , OLIVEIRA, Denisson Queiroz
• Tipo de documento: Tese
• 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: Estimação de estado; Redes de distribuição; Otimização multiobjetivo; Alocação de medidores; Algoritmo Genético de Chaves Aleatórias Viciadas; Teoria Pareto
• Palavras-chave em Inglês: State estimation; Distribution networks; Multiobjective optimization; Meter placement; Biased Random-Key Genetic Algorithm; Pareto theory
• Área do conhecimento CNPq: Transmissão da Energia Elétrica, Distribuição da Energia Elétrica
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/3130

This thesis presents a methodology for multiobjective Distribution Network Reconfiguration (DNR). The objectives considered in the proposed technique are the losses and accuracy of the state estimator. The degradation of the accuracy of the state estimator, caused by the DNR, is compensated by the installation of meters during the optimal topology generation. Due to this, it is also considered as one of the objectives of the reconfiguration problem the minimization of the number of meters installed to reduce the installation costs of the measuring equipment. The objective function components were minimized subject to the following constraints: radiality, active and reactive power flow, loading limits, intervals for the magnitudes of the nodal voltages, and risk levels for the accuracy of the state estimator. The optimization problem defined above was solved using a multiobjective formulation based on the combination of Pareto Theory (to model multiple criteria) with the genetic algorithm with biased random keys. The application of the proposed method for DNR was performed in a distribution network of 69 nodes. The results of the tests demonstrate that the solutions obtained by the proposed method are of good quality and establish an acceptable compromise among the objectives, that is, topologies with significant reductions in annual energy losses and measurement systems with high accuracy and low installation costs. In addition, the proposed methodology for allocating meters is flexible to take into account the number of topologies defined by the operation planning engineers. In this way, the main contributions of this thesis are: (i) the development of a methodology for the concomitant solution of the problems of minimization of the energy loss through DNR and maximization of the state estimation accuracy by the meter placement aiming to avoid the degradation of the state estimation accuracy by the network topology change; (ii) introduction of an analytical approach to assess the state estimation accuracy considering null injection nodes; (iii) proposition of a multiobjective version of BRKGA; (iv) presentation of a new codification for the candidate solutions generated by the BRKGA for the solution of the DNR problem.