Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading
Fernando Marcos de Oliveira,
Marcelo Henrique Manzke Brandt,
Fabiano Salvadori,
José Enrique Eirez Izquierdo,
Marco Roberto Cavallari,
Oswaldo Hideo Ando Junior
Affiliations
Fernando Marcos de Oliveira
Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Foz do Iguaçu 85867-000, PR, Brazil
Marcelo Henrique Manzke Brandt
Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Foz do Iguaçu 85867-000, PR, Brazil
Fabiano Salvadori
Smart Grid Laboratory (LabREI), Center for Alternative and Renewable Research (CEAR), Federal University of Paraiba (UFPB), João Pessoa 58051-900, PB, Brazil
José Enrique Eirez Izquierdo
Faculdade de Engenharia Elétrica e de Computação (FEEC), Universidade Estadual de Campinas (UNICAMP), Av. Albert Einstein 400, Campinas 13083-852, SP, Brazil
Marco Roberto Cavallari
Research Group on Energy & Energy Sustainability (GPEnSE), Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil
Oswaldo Hideo Ando Junior
Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration—UNILA, Foz do Iguaçu 85867-000, PR, Brazil
Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.
