Control Strategy for Photovoltaic Array MPPT Based on an Improved Particle Swarm Optimization Algorithm

This paper proposes a photovoltaic MPPT (Maximum Power Point Tracking) control strategy based on an improved Particle Swarm Optimization algorithm. Due to the complex and varying real-world environments of photovoltaic systems, under partial shading conditions, the power-voltage (PV) characteristic curves of the photovoltaic arrays exhibit multiple peaks. Traditional MPPT controls are unable to effectively track the maximum power point, often getting trapped in local optima without reaching the global optimum. Based on an analysis of the output characteristics of photovoltaic cells, this paper introduces an MPPT control based on an Improved Particle Swarm Optimization (IPSO) algorithm. The distinctive feature of this approach is that it incorporates asynchronous learning factors, adaptive inertia weights, and mutation mechanisms into the PSO optimization process. During the optimization process, the learning factors and inertia weights are adjusted synchronously, and particle positions are mutated to enhance the convergence speed and accuracy of the algorithm, thus improving its global optimization capability. A simulation model was established using Matlab/Simulink, demonstrating that the improved particle swarm algorithm maintains high precision and speed in tracking the maximum power point under both uniform and shadowed lighting conditions. The proposed method significantly enhances the tracking speed and accuracy of the photovoltaic system's output power and improves the efficiency of photovoltaic cells.