Low-Complexity Double-Vector Model Predictive Control With Minimum Root Mean Square Error for Three-Phase Three-Level Inverters

The conventional finite-control-set model predictive control (FCS-MPC) approach for controlling three-phase three-level inverters is plagued by significant current ripple. To minimize the root mean square error (RMSE) of the current control, this article proposes a more straightforward and efficient solution using dual vectors. First, a dual-vector algebraic preselection method is presented to reduce computational complexity. Second, the continuous current reference is adopted for the first time instead of the discretized value to improve the accuracy of the current control error calculation, which leads to a more precise RMSE calculation. Third, the impact of the sequence and duty cycles of the vectors on the RMSE is quantified for the first time, and the optimal solution is proposed to minimize RMSE. Compared with the existing dual-vector optimization algorithms, the proposed algorithm reduces the RMSE and total harmonic distortion (THD) of the output current by more than 25% and 10%, respectively. Experimental results are presented to validate the advantages of the proposed algorithm.