Research on improved adaptive particle swarm optimization maximum power point tracking algorithm based on energy-stored quasi-Z-source photovoltaic grid-connected inverter
ZHU Honghui, QU Aiwen, ZHOU Yangzhong
College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108
Abstract：Traditional particle swarm optimization (PSO) maximum power point tracking (MPPT) algorithm has some disadvantages, such as poor stability, serious oscillation, slow tracking speed and so on when solving the problem of multi-peaks optimization of photovoltaic array under local shadow conditions. Given the advantages of quasi-Z-source inverter, an improved adaptive PSO maximum power point tracking algorithm based on energy-stored quasi-Z-source photovoltaic grid-connected inverter is proposed to solve this problem, in which the energy storage unit is paralleled with the quasi-Z-source impedance network capacitor. This algorithm no longer depends on the number of iterations, but directly updates the inertia weight and learning factor with the individual optimal power and the global optimal power,and the voltage window limitation is inserted. The tracking speed is improved and the power oscillation is reduced effectively. Simulation results show that the proposed algorithm has good global MPPT ability of multi-peaks photovoltaic curve in the application of energy-stored quasi-Z-source photovoltaic grid-connected inverter, improves the generation efficiency of photovoltaic array and has good feasibility.
2021, Vol. 22 
