Improved White Shark Optimizer Based Maximum Power Point Tracking Algorithm for Photovoltaic Systems Under Partial Shading Conditions

Photovoltaic arrays present multiple peaks characteristic under partial shading conditions (PSCs), bringing challenge of finding the global maximum power point (GMPP). Recently, the bio-inspired metaheuristics have been popularly applied to find the GMPP under PSCs, while they usually suffer from long convergence time and large power oscillations. Therefore, this paper proposes an improved white shark optimizer (IWSO) based MPPT algorithm under PSCs. The WSO algorithm is a novel metaheuristic and can find the GMPP quickly and accurately due to its flexibility and robustness. Furthermore, an improved method has been proposed, which dynamically adjusts the hierarchy in the white sharks during global exploration and local exploitation. Through this, the search scope for the GMPP is increased and the local exploitation is accelerated. To verify the performance and superiority of the IWSO based MPPT algorithm under PSCs, it is compared with existing metaheuristic based MPPT algorithms (particle swarm optimization, cuckoo search, grey wolf optimizer, and salp swarm optimization) by simulations and then evaluated by experiments. The proposed IWSO based MPPT algorithm shows excellent performance under different PSCs regarding tracking time, tracking efficiency and power oscillations.