Model Predictive Control With a Reduced Number of Considered States in a Modular Multilevel Converter for HVDC System

This paper proposes a model predictive control (MPC) method for modular-multilevel-converter (MMC) high-voltage direct current (HVDC). To control the MMC-HVDC system properly, the ac current, circulating current, and sub-module (SM) capacitor voltage are taken into consideration. The existing MPC methods for the MMC-HVDC system utilize weighting factors to configure the cost function in combinations of the SM capacitor voltage balancing algorithm, ac current control, and circulating current control. Because all combinations of the switch states are considered in order to minimize the cost function, their possible combinations increase geometrically according to the increase of the level of the MMC, which is a significant disadvantage. This paper proposes a new MPC method with a reduced number of states for ac current control, circulating current control, and the SM capacitor voltage-balancing algorithm. The proposed cost functions are divided into three types according to their control purposes. Each cost function determines the minimum number of states for controlling the ac current, circulating current, and SM capacitor voltage. The efficacy of the proposed controlling method is verified through simulation results using PSCAD/EMTDC.