Estudo comparativo entre controladores PI e FGS-PI aplicados ao rastreamento da máxima potência em sistemas fotovoltaicos

• Ano de defesa: 2025
• Autor(a) principal: Bigai, Mario Henrique
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Ponta Grossa; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Energia solar; Energia - Fontes alternativas; Recursos naturais renováveis; Sistemas de energia fotovoltaica; Sistemas difusos; Sistemas de controle inteligente; Controladores elétricos; Eletrônica de potência; Solar energy; Renewable energy sources; Renewable natural resources; Photovoltaic power systems; Fuzzy Systems; Intelligent control systems; Electric controllers; Power electronics; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/37073

Climate change and the depletion of fossil resources are driving the transition to renewable energy sources, with solar photovoltaics playing an important role worldwide. In this context, tracking the maximum power point is essential to increase the system’s efficiency due to environmental conditions that impact the power generated by the photovoltaic modules. Tracking is done by controlling the DC-DC converter connected to the photovoltaic module. However, converters have non-linear dynamics, as does the photovoltaic module. Linearization is usually carried out at the maximum power point under standard test conditions, and the controller is designed for the linearization point. However, due to variations in environmental conditions, the system operates mainly outside the linearization point, which can compromise control and maximum power tracking performance. This work aims to design and compare PI and FGS-PI controllers applied to the maximum power tracking process under different environmental conditions in photovoltaic systems. Specifically, a battery charger using solar energy was studied, consisting of a Buck converter interfacing a photovoltaic panel and a battery. The proposed FGS-PI controller makes improving the system’s dynamic performance possible due to its adaptability to the system’s non-linearity by adjusting the controller’s gains. Genetic algorithm optimization was applied to the FGS system to adapt the PI controller gains more efficiently to optimize control performance without compromising system stability. The optimized FGS-PI controller outperformed the PI controller in terms of control performance, and when applied to maximum power point tracking, it showed improvements in tracking time. The results were observed both in the PSIM simulation environment and in practice.