A New Model Predictive Control Formulation for CHB Inverters

The finite control model predictive control (FCS-MPC) is considered one of the most important advances in power converter control. MPC offers the power converters with high dynamic performance, multi-objective capability, and no need for modulation schemes or tuning of PI parameters. It was reported that longer prediction horizon MPC yield better performance than short prediction horizon MPC. However, the number of computations increases significantly when real-time implementing long prediction horizon MPC on a multilevel power converter due to the existence of a huge number of switching combinations and redundancies. To overcome this bottleneck, this paper has presented a FCS-MPC scheme. In the proposed method, instead of estimation all the possible switching combinations in each sampling step, the multistep FCS-MPC is reformulated mathematically to an optimization problem, which can be solved through matrix theory. Compared with the existing MPC optimization algorithms, the proposed prediction formulation method has the advantage of reduced computational burden and no need for the cost function. The proposed method is finally verified on a seven-level CHB inverter.