Improved vector selection model predictive control strategy for quasi‐ <i>Z</i> ‐source inverter virtual synchronous generator grid‐connected system

Abstract Conventional inverter control methods reduce the grid inertia and are susceptible to parameter variations, resulting in a gradual weakening of grid stability. To address the above problems, an improved vector selection model predictive control strategy is proposed, with virtual synchronous generator technology to control the quasi‐ Z ‐source inverter. First, the influence of different operating states on the quasi‐ Z ‐source inverter state variables is analysed, and the established quasi‐ Z ‐source inverter‐virtual synchronous generator discrete‐time model predicts the input current, output capacitor voltage, and load current of the quasi‐ Z ‐source inverter network. Second, the inverter switching timing is analysed to establish the optimal switching vector set, and the optimal switching vector set is selected by defining the sub‐objective function, which reduces the average switching frequency of the system. Compared with the finite control set model predictive control, this strategy is to reduce the computation time of the system to obtain the optimal solution of the quasi‐ Z ‐source inverter, to improve the dynamic responsiveness of the system during the control process, and to improve the power quality. Finally, the effectiveness and correctness of the proposed algorithm are verified by experiments.