MPPT Control Strategy for Photovoltaic with Partial Shadow Based on Improved Squirrel Search Algorithm

To address the issue of the conventional Maximum Power Point Tracking (MPPT) control method frequently converging to local power maxima in the presence of partial shadow, an Improved Squirrel Search Algorithm (ISSA) MPPT control strategy is introduced. Initially, the improved Tent chaotic mapping is employed to enhance the uniformity of the SSA algorithm’s initialization population. Subsequently, an optimized differential mutation mechanism is applied to facilitate mutation and crossover within the initial population, thereby enhancing the global optimization and local convergence capabilities of the SSA algorithm. Lastly, a nonlinearly decreasing optimization of the predator probability and Levy flight factor within the SSA algorithm is utilized to enhance its optimization precision. Simulation results demonstrate that the ISSA algorithm efficiently identifies the optimal value under partial shadow conditions and outperforms other heuristic algorithms in effectiveness.