Maximum Power Extraction of Solar PV System using Meta-Heuristic MPPT techniques: A Comparative Study

This paper presents a comparative analysis of four different maximum power point tracking technique (MPPT) techniques to maximize the solar power produced under different environmental conditions. Different environmental conditions produce a partial shading effect that significantly reduces solar PV Power. This paper includes meta- heuristic approaches such as differential evolution (DE), particle swarm optimization (PSO), ant colony optimization (ACO) and the most common perturb and observe (P&O) method for comparative analysis of performance. The main purpose of utilizing these meta-heuristic techniques is that it provides an effective path to find the best solution and the solution is iterated until we obtain the optimum results. The parameters used for comparison of the above algorithms are the number of iterations required to reach maximum power point (MPP) and the convergence time. These algorithms are implemented and their performance is evaluated by using the MATLAB/Simulink under different solar irradiation and constant ambient temperature. The simulation results show that ACO has the best convergence property and requires the least iteration steps to reach a global maximum power point (GMPP) that ameliorate the solar system performance.