Adaptive model predictive control for LCL three-phase grid-connected inverter

Traditional model predictive control has excellent dynamic and static performance, but it depends on precise mathematical model of system and is difficult to adapt to the grid impedance fluctuation caused by the connection of grid-connected inverter in weak grid condition and some other factors. Thus, an adaptive periodic model parameter adjustment strategy is proposed. The predicted and measured values of system states at each sampling time were saved to form an error calculation matrix. The minimum error between the predicted states and the actual statesis was solved periodically to accurately identify the system model parameters. Then, the modified system model parameters were updated into the discrete mathematical model of the system and the state variable reference value calculation equations. This strategy can quickly correct the model parameter error, eliminate the inaccuracy of model state prediction caused by model parameter error, and improve the performance of model predictive control. Finally, a 3 kW three-phase LCL grid-connected inverter experimental prototype was built to verify the correctness of the analysis and the performance of this control strategy. © 2021, Harbin University of Science and Technology Publication. All right reserved.