Otimização multiobjetivo baseada na análise em elementos finitos e aplicada sobre o sistema de acionamento de um motor a relutância variável

• Ano de defesa: 2023
• Autor(a) principal: Miranda, Breno Brito
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Análise computacional; Computational analysis; Meta-heurísticas; Meta-heuristics; Método dos elementos finitos; Finite element method; Motor a relutância variável; Switched reluctance motor; Otimização multiobjetivo; Multiobjective optimization; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIA; Engenharia elétrica; Motores elétricos - Controle eletrônico; Energia elétrica - Transmissão - Corrente contínua; Veículos elétricos
• Link de acesso: https://repositorio.ufu.br/handle/123456789/37410; http://doi.org/10.14393/ufu.te.2022.678

The electrical motors are electro-mechanical energy converters mostly used in the industrial sector, although even with modest presence in the transportation sector. The need for reducing the energy consumption by industries, as well as the emerging market demand for vehicle electrification require the manufacturing of low-cost and high-efficiency electrical motors. The Switched Reluctance Motors (SRMs) are attractive relating to induction and permanent magnet motors, having characteristics as robustness, simpler construction, low-cost and higher allowable operating temperature due to the absence of magnets on the rotor. However, the SRMs present drawbacks as poor torque quality, mechanical vibration and acoustic noise, usually related to the salient teeth, switching components and control strategies imposed by the power converter. The multi-objective optimization techniques, in particular meta-heuristics, coupled to the finite element analysis form a robust tool for either searching the solution, or mitigating such problems. The objective of this thesis is to provide easy implementation of appropriate methods for designing SRMs and controlling some drive system’s parameters by numerical optimization in an efficient way. Two optimization problems are formulated: search for the geometric dimensions that maximize the magnetic flux linkage per magnetic material bulk relation; search for the commutation angles that minimize the torque ripple and maximize the mean torque value for a given operating point. The problems are solved by using the Differential Evolution (DE) and the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithms. The obtained results indicate that the optimization algorithms were capable to solve the problems satisfactorily, given the inherit limitations to the proposed methodology, improving the torque quality and production capability, however with motor efficiency and power factor reduction. Finally, an advanced speed and torque technique control, Torque Sharing Function (TSF), with higher improvement capability of the torque quality and efficiency, is proposed to be applied to the converter as future work.