Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment

Autor(es)

Inomoto, Roberto ; Sguarezi Filho, Alfeu J. ; Monteiro, Jose Roberto ; da Costa, Eduardo C. Marques

• Tipo de documento: Artigo Científico
• Fonte: RESULTS IN CONTROL AND OPTIMIZATION; v. 14, p. 15-pg., 2024-03-01.

Resumo

This paper proposes a novel controller optimization of boost converter by tunning two controllers of voltage and current in PV (Photovoltaic) boost converters: Sliding Mode Control (SMC) or Sliding Mode plus Proportional-Integrative. Genetic Algorithm (GA) optimization is applied in a Internet of Things (IoT) context, in which the server side consists of running the GA and thereafter used to tune the SMC and SMPIC of the PV plant boost converter. Communication between the IoT (PV plant) and cloud server comprises to the acquired currents and voltages from PV to the server and controllers parameters from server to IoT. Data from the IoT is applied to calculate the fitness function for a given solution, which learns the solar plant (machine learning). Experimental results using hardware are considered, in order to evaluate the performance, and results are compared between heuristic and deterministic parameters from SMC or SMPIC, proving the reduction of overshoot and settling time. (AU)

• Processo FAPESP: 22/00323-3 - Desenvolvimento de técnicas de controle aplicado ao conversor da rede em um carregador bidirecional de bateria para veículos elétricos
• Beneficiário: Alfeu Joãozinho Sguarezi Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular