Inductance Parameter Identification of VIENNA Rectifier Based on MRAS

Finite control set-model predictive control(FCS-MPC), as an efficient multi-objective nonlinear control algorithm, can consider a variety of constraints, so it is suitable for the strongly nonlinear VIENNA rectifier system. However, as one of the key factors affecting the control performance of FCS-MPC, the mismatch of model parameters may directly affect the performance of the grid-current. To improve the robustness of the system parameters and improve the accuracy of the traditional inductance observer, the influence mechanism of the mismatch of model parameters on grid-current is revealed by constructing an analytical equation of model parameters and grid-current performance. Finally, the results of comparison with the traditional inductance observer show that the proposed MRAS identification method has higher precision and smaller static error. At the same time, the validity of the theory is demonstrated from the aspects of steady state, transient state and parameter mismatch.