Two fast metaheuristic-based MPPT techniques for partially shaded photovoltaic system

The photovoltaic output power varies according to the solar radiation and ambient temperature because the operation of the photovoltaic arrays depends on them. Therefore, achieving the maximum photovoltaic power under different shading patterns is a key factor in the performance improvement of photovoltaic systems. An efficient Maximum Power Point Tracking technique is needed to distinguish the global maximum power point from the local ones, as the traditional techniques are prone to fail. This paper proposes two hybrid meta-heuristic algorithms to improve the maximum power point tracking technique of partially shaded photovoltaic systems. The first proposed maximum power point tracking technique is based on the Whale Optimization Algorithm and Differential Evolution algorithms. The second proposed maximum power point tracking technique is an improved version of the first proposed technique. Both proposed techniques are highly proficient in enhancing the photovoltaic system's efficiency in shaded and unshaded conditions. The maximum power point tracking technique is studied for evaluating the performance based on two traditional algorithms, Whale Optimization Algorithm and Differential Evolution, and two hybrid proposed algorithms. The simulation results show the second proposed maximum power point tracking technique finds the global power points faster and offers better performance than the first proposed technique, which itself outperforms two traditional maximum power point tracking techniques concerning a faster rate of convergence and higher efficiency.