Space-vectors based hierarchical model predictive control for a modular multilevel converter

The modular multilevel converter (MMC) is a competitive candidate for medium/high-power applications, specifically for high-voltage direct current transmission systems. Model predictive control (MPC) is an advanced and flexible method for power converters. The existing MPC methods for the MMC 3-phase system treat whole system as a three independent single phase system, and the computational load increases geometrically according to the increase of the level of the MMC. This paper proposes a space-vectors based hierarchical model predictive control (HMPC) strategy for a 3-phase MMC system with independent cost functions. Three hierarchical mathematical models of the MMC are derived and discretized to predict the AC-side current, circulating current and capacitor voltage, respectively. By utilizing multilevel space-vectors and hierarchical model, the considered number of states can be reduced significantly with the highest DC voltage utilization ratio and good performance. In addition, this strategy doesn't need the complex capacitor voltage sorting method and reduces the power loss by avoiding the unnecessary switching state transitions. The performance of the proposed strategy for 11-level MMC is verified through simulation results.