Buck-boost Converter using GA-based MPPT for Solar Energy Optimization - Syafaah, Lailis; Faruq, Amrul; Noor Cahyadi, Basri; Hidayat, Khusnul; Setyawan, Novendra; Lestandy, Merinda; Zulfatman (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2023)
Energy optimization in the Solar Power Plant system needs to have more attention. Indonesia is a tropical country that has two seasons, where the weather and cloud movements are frequently unpredictable, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) was used. However, the current MPPT switching system was still not optimal with an efficiency of around 90%. In this study, the installation of MPPT was carried out in order to optimize the power in solar photovoltaic (PV) system due to the fluctuations of solar irradiation at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this study, the modeling of solar PV system was made using MATLAB software, where the design of the solar PV system consisted of a PV module with capacity 240W, DC to DC converter, battery and MPPT. Genetic Algorithm (GA)-based MPPT had been tested and compared to Particle Swarm Optimization (PSO)-based MPPT and conventional MPPT, where the GA-based MPPT worked well in finding the maximum power point in the solar photovoltaic system. It was found that GA-based MPPT produced a maximum power point close to PV power with an efficiency of 92%, while the effciciency of PSO-based MPPT and conventional MPPT were 85% and 79% respectively. In selecting the method for designing MPPT, a method with a wide range of sample data is required. This is due to the fluctuation of solar irradiance received by the solar PV.