A Novel Simplified Finite Control Set Repeat-Model Predictive Control for Grid-Connected Inverters

Due to fast dynamic response, multi-constraint control capability and strong robustness, the finite control set model predictive control (FCS-MPC) algorithm has been extensively studied. However, the traditional FCS-MPC algorithm has a heavy computational burden because of the need to traverse all possible voltage vectors. In order to reduce the candidate vectors, a new simplified finite control set repeat model predictive control algorithm (FCS-RMPC) is proposed in this paper. In the proposed algorithm, the optimal vectors at the same position in consecutive fundamental frequency periods are regarded as the same or adjacent. Based on this, the proposed algorithm takes the historical data of the optimal voltage vector into consideration for the selection of the optimal vector during the next time steps. Based on the proposed FCS-RMPC algorithm, the computational burden of the system can be significantly reduced. The experimental platform of a three-phase two-level grid-connected inverter (GCI) with LCL filter is established to validate the feasibility and effectiveness of the proposed control strategy.