Uma contribuição aos projetos de transformadores via algoritmos naturais e elementos finitos

• Ano de defesa: 2019
• Autor(a) principal: Maximiano Sobrinho, Adelicio
• 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: Evolução Diferencial; Otimização Multiobjetivo; Projeto de Máquinas Elétricas; Método de Elementos Finitos; Finite Element Method; Multiobjective Optimization; Differential Evolution; Electric Machines Design; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/26308; http://dx.doi.org/10.14393/ufu.te.2019.2179

The objective of this research is to present the studies carried out to design three-phase core type distribution transformers, with the aid of optimization techniques. Minimize losses through the use of a mono-objective function, and with the use of multi-objective function minimize losses and the total mass of the active part of the transformer. The algorithms used are: Differential Evolution (DE) and Particle Swarm Optimization (PSO), their performances are compared through the obtained results. In addition, is detailed the importance of a good estimation of the inrush current of the transformers from the elaboration of the project, due to its interference in the protection systems and in the electrical power system in general. The main parameters of the design, such as: core dimensions, total losses, no load current and the energizing current are estimated analytically through the OCTAVE software. The analysis of magnetic flux density in the core is simulated using the Finite Element Method (FEM). The inrush current is calculated, simulated by ATPDraw (Alternative Transient Program) software and measured in the field. Multiobjective optimization allows working with two or more conflicting objectives, and at each iteration it stores the various non-dominant Pareto Front solutions, helping designers to choose the solution that best meets their needs. The results obtained with the mono-objective and multiobjective optimization techniques were interesting to minimize the losses and/or cost of the project.