Abstract:  The output power of a photovoltaic array exhibits a multi-peak state under partial shading, and traditional MPPT(Maximum Power Point Tracking) control can not solve the multi-peak problem, resulting in the system being trapped in a local optimum affecting the photovoltaic power generation efficiency. To address this issue, a hybrid algorithm is proposed for photovoltaic maximum power point tracking. This method optimizes the initial population of the sparrow algorithm and combines with reverse learning strategy to enhance the algorithm's global search ability. When the algorithm searches near the maximum power point of photovoltaic power generation, the perturbation observation method is used to quickly search the maximum power point by utilizing its fast convergence characteristics. Using Simulink simulation and hardware experimentation, the global search ability and fast convergence ability of the proposed hybrid algorithm are verified. Compared with the sparrow algorithm and perturbation observation method, the hybrid algorithm has significantly improved accuracy and speed

Key words: maximum power point tracking ( MPPT), local shadows, reverse learning, sparrow algorithm, perturbation observation method