Finite Control Set Model Predictive Control with Kalman Filter Estimation for LCL-type Grid-Tied Inverter

As one of the most effective control strategies for LCL-type grid-tied inverters (GTIs), the finite control set model predictive control (FCS-MPC) strategy can achieve accurate tracking of the reference value and handle a variety of constraints at the same time. Therefore, it has been employed in industrial applications. However, in order to achieve a better control effect, the traditional FCS-MPC requires multiple voltage and current sensors, which greatly increases the system cost. In addition, sensor failures occur frequently in practical applications, which will significantly reduce the overall performance of the system. Therefore, this paper proposes an improved FCS-MPC strategy (KFE-MPC) based on Kalman filter (KF). The proposed KFE-MPC strategy can reduce the number of sensors and improve the system's ability to resist sensor failures effectively by utilizing a Kalman filter to estimate voltage and current. A three-phase / 110V LCL-type grid-tied inverter model is established to verify the feasibility and effectiveness of the proposed control strategy.