Photovoltaic Maximum Power Tracking Based on Improved Sparrow Algorithm

When a photovoltaic array is under partial shading conditions, its output power may exhibit multi-peak characteristics. The conventional maximum power point tracking (Maximum Power Point Tracking, MPPT) method is difficult to find the global maximum power point quickly and accurately. In this paper, an improved sparrow algorithm is designed to improve the efficiency of the maximum power point tracking technique for PV systems. Firstly, chaotic mapping is utilized to enhance the exploration ability of the algorithm and solve the problem of low optimization accuracy of the sparrow search algorithm in low dimensions; secondly, an adaptive rule is designed to be applied to the joiner position update, and at the same time the sparrow position update is constrained by a boundary processing strategy, which ensures that the sparrow algorithm is able to effectively solve the optimization problem under the case of a small search range; lastly, the improved algorithm is applied to the maximum output point of PV arrays under non-uniform lighting Finally, the improved algorithm is applied to tracking the maximum output power of PV arrays under non-uniform illumination conditions for simulation. The results show that the improved sparrow algorithm performs well in convergence speed and tracking accuracy, can efficiently avoid the local extreme value problem, and quickly and accurately locates the maximum power point.