Three-Phase Model-Based Predictive Control Methods With Reduced Calculation Burden for Modular Multilevel Converters

Model predictive control (MPC) has been widely investigated in modular multilevel converters (MMCs) due to its superiority in achieving multiple control objectives. The three-phase model-based MPC, which contains the common-mode voltage in the output current dynamic model and considers interaction among phases, shows better performance than the conventional per-phase model-based predictive control in a three-phase MMC system. However, it suffers from a heavy computational burden as the number of submodules (SMs) increases. To address this issue, this article first analyzes the relationship among the numbers of inserted SMs, the controllability of dc-link current, and circulating currents. Then, according to this analysis, two simplified MPC methods based on the three-phase model with reduced computational burden are proposed. Specifically, fewer insertion index combinations are selected in advance to ensure good output currents, controllable dc-link, and circulating currents. The effectiveness of the proposed methods is verified through experimental results.