Comparative Analysis of MPPT Based on PSO, SSA and ISSA Algorithms under Partial Shade Conditions

Maximum power point tracking (MPPT) technology is the core means to improve the efficiency of photovoltaic power generation system. However, under the shadow condition, the existence of local maximum power points may make it difficult for traditional MPPT algorithm to accurately find the global maximum power point (GMPP). In order to solve this problem, this paper studies and compares the applications of particle swarm optimization (PSO), Sparrow Search algorithm (SSA) and Improved Sparrow search algorithm (ISSA) in MPPT. In this paper, the principles and processes of three artificial intelligence algorithms are described in detail, and their performance under static shadow occlusion and shadow mutation is analyzed by building simulation models in Matlab/Simulink. The results show that the three algorithms can effectively track the maximum power point of photovoltaic arrays, among which the particle swarm optimization algorithm has the highest tracking accuracy, the improved sparrow search algorithm has the fastest convergence speed, while the sparrow search algorithm has relatively low tracking accuracy and the slowest convergence speed. This study provides an important reference for MPPT optimization of photovoltaic systems in complex environments.