COMPARISON OF PARTICLE SWARM OPTIMIZATION (PSO) AND PERTURB AND OBSERVE (P&O) METHODS FOR MAXIMUM POWER POINT TRACKING IN PHOTOVOLTAIC SYSTEMS UNDER CHANGING CLIMATIC CONDITIONS WITH OPTIMAL PARAMETER SELECTION

: Photovoltaic (PV) systems have low efficiency in electricity generation. In PV systems, Maximum Power Point Tracking (MPPT) algorithms must track the MPPT efficiently. There are both conventional and meta-heuristic methods available to track the maximum power point(MPP) of a PV system. Under Partial Shading Conditions (PSC), local maximum power points (LMPP) and a global maximum power point (GMPP) can occur. The Particle Swarm Optimization (PSO) algorithm can easily and quickly track the global maximum point. Under partial shading condition, the Perturb and Observe (P&O) method tends to get stuck at local maximum power point while tracking maximum power point. As a result, the global maximum power point of the PV system cannot be detected using the perturb and observe algorithm. In this paper, a comparative analysis was performed based on simulation results obtained from Matlab/Simulink circuit models under PSC conditions. It was observed that when the parameters of the particle swarm optimization algorithm are appropriately chosen, the particle swarm optimization method outperforms the perturb and observation method in terms of efficiency in tracking the global maximum power point. According to the simulation results, no oscillations around the global maximum power point were observed with the particle swarm optimization method. Furthermore, in rapidly changing climatic conditions, the particle swarm optimization algorithm tracks the global maximum power point more efficiently, stably, and effectively compared to the perturb and observe algorithm.