Design of MPPT for Buck-boost Converter based on GA to Optimize Solar Power Generation

Abstract

Maximizing power in the Solar Power Plant system was something that needs attention. Indonesia was a tropical country that has two seasons, where the changing weather or cloud movements are always erratic, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) is used. Currently the switching system in MPPT is still not optimal with a system efficiency of around 90%. In this study, the installation of MPPT was carried out which aims to maximize power in the solar photovoltaic (PV) system due to fluctuations or instability of sunlight 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 research, modeling of the solar PV system was made using MATLAB software, where the design of the solar PV system consists of a PV module with capacity 240W, DC to DC converter, battery and MPPT. The MPPT based on Genetic Algorithm (GA) has been tested and compared with MPPT based on Particle Swarm Optimization (PSO) and conventional MPPT, where MPPT based on GA works well in finding the maximum power point in the solar photovoltaic system. it was found that MPPT GA produces a maximum power point close to PV power with an efficiency of 92%, while MPPT PSO has an efficiency of 85% and for conventional MPPT it was 79%. In selecting the method for designing MPPT, a method is needed that has a wide range of sample data. This is due to the fluctuating characteristics of the irradiation value received by solar PV.