Comparative performance analysis of metaheuristic algorithms for maximum power point tracking under partial shading conditions in PV systems

Photovoltaic systems are one of the renewable energy systems that convert solar radiation directly into electricity. The relationship between current and voltage of PV system is nonlinear and it has only one point where power efficiency is greatest. Power efficiency mainly depends on atmospheric conditions such as irradiance and temperature. Therefore, various maximum power point tracking algorithms have been proposed in the literature to obtain maximum efficiency. The proposed traditional methods show high performance for maximum power point tracking under uniform irradiance and constant temperature. But another situation that affects the power efficiency is the partial shading condition and there are more maximum points on the output power curve in the partial shading conditions. For this reason, traditional methods are insufficient to reach global maximum power points. Metaheuristic algorithms have been proposed to solve this problem. In this paper, particle swarm optimization, gray wolf optimization and whale optimization algorithms were selected among the metaheuristic algorithms and comparative performance analysis were made in terms of convergence rate and efficiency under partial shading conditions. Obtained results were compared both graphically and numerically.