A Developed Model Predictive Control Algorithm for Modular Multilevel Converter with Reduced Execution Time

In this paper, a developed technique of Model Predictive Control (MPC) is proposed to decrease the execution time of the control algorithm for Modular Multilevel Converter (MMC) based on grouping the switching states. There are two stages in the control strategy to get the optimal switching state for the converter in the next sampling interval. Within the first stage, the allowable switching states of MMC are divided equally into (M) groups based on the number of sub-modules (SMs) per arm of a single-phase line. Therefore, the output of second-stage obtains the optimal state from each group of the switching states. In addition, all M groups are running simultaneously to reduce the execution time. Unlike the previous algorithm, the second stage of the proposed algorithm uses directly the optimal state and the corresponding cost function for each group from the first stage, then the optimal switching state is selected according to the minimum cost function from the M groups. Therefore, a reduction in the computational time of the MMC algorithm is achieved. The control objectives here are the injected grid currents, balancing the capacitor voltages of SMs at their set point, and minimizing the circulating currents in the MMC. The effectiveness of the proposed algorithm is verified by using the nonlinear simulation of MATLAB/SIMULINK.