Performance Evaluation of a Nature-Inspired Three-Particle Swarm Optimization Algorithm in PV Systems with a Modified SEPIC Converter

The increase in world population, the development of industrialization and people's use of more advanced technologies increase energy demands day by day. People have started to turn to alternative energy sources to avoid the energy crisis that will occur when existing energy sources are exhausted. Photovoltaic (PV) systems are an advantageous option among sustainable energy sources. PV systems are affected by irradiance intensity and temperature. To overcome this problem, Maximum power point tracking (MPPT) algorithms are used. In this study, in order to maximize the efficiency of PV systems, the performance of Particle swarm optimization (PSO), Deterministic Particle swarm optimization (DPSO), Enhance Autonomous Group Particle swarm optimization (EAGPSO) algorithms were evaluated by using a high gain modified SEPIC converter. PSO, DPSO and EAGPSO algorithms were evaluated for three different scenarios under normal irradiance and partial shading conditions. It has been observed that the EAGPSO algorithm has the highest MPPT efficiency of 98.9% and a convergence time of 0.37s for different scenarios. In addition, it has been found that the power oscillation of the EAGPSO algorithm is reduced by approximately half compared to DPSO and by approximately two thirds compared to PSO.