A Novel High-Performance Predictive Control Formulation for Multilevel Inverters

This article proposes a novel high-performance predictive control with long prediction horizons to improve the control performance of the multilevel inverters. The finite control set model predictive control (FCS-MPC) has obtained a lot of attention for power converters due to its advantages of high dynamic performance, multi objective capability, no need for PI regulators and PWM modulators. However, the MPC method requires a high number of computations especially for higher-level power converter topologies due to the existence of a huge amount of switching combinations and redundancies. Real-time searching for the optimal switching state among a large candidate pool at a high sampling rate is sometimes impossible with the standard commercial processors. This limitation also prevents real-time implementation of an MPC for multilevel converters with step prediction more than one. To solve the aforementioned issues, this paper presents a novel high-performance FCS-MPC scheme. The proposed FCS-MPC is reformulated mathematically MPC approach to an l 2 norm optimization problem, which can be solved on-line through matrix theory. Compared with the existing MPC optimization algorithms, the proposed MPC formulation has the advantage of a substantial reduction in computational burden, no need for the weighting factors or cost functions, and thus can operate at long horizon prediction length. The proposed method is verified experimentally on a seven-level CHB inverter prototype with a three-step prediction length to demonstrate the ability and performance of the proposed method for multilevel inverters.