Jia Chi

In different light intensities, photovoltaic (PV) arrays with multi-peak characteristics encounter numerous challenges in maximum power point tracking (MPPT) control, such as low computational efficiency, slow convergence speed, and susceptibility to local optima. To address these issues, this study proposes an improved quantum particle swarm optimization (QPSO) algorithm that combines the advanced features of the Whale Optimization Algorithm (WOA) with the concept of Lévy flight, thereby forming a novel mechanism. The convergence of this algorithm, together with the particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) algorithms, was simulated and evaluated using 10 single-peak and multi-peak test functions, and then applied to a simulation model of PV array partial shading. The results show that, compared with the traditional PSO method, the tracking accuracy of this algorithm is improved by 2.80% and the convergence speed is increased by 57.14%. Under both static and sudden shading conditions, this algorithm can effectively enhance the tracking ability of the maximum power point of the PV array and achieve stable maximum power output. The average tracking accuracy of this algorithm reaches 99.71% and the average tracking speed is 0.06 s in simulation, showing an obvious advantage over both the PSO and QPSO algorithms. This simulation-based validation confirms the algorithm’s effectiveness, though hardware validation remains for future work. These results fully demonstrate the unique advantages and innovation of this algorithm in dealing with complex optimization problems, laying a solid foundation for improving the efficiency and reliability of PV systems and providing strong support for related research in this field.