MWAO-Based Data-Driven Predictive Control for LC-Filtered Voltage Source Inverters

The conventional model predictive control (MPC) has been widely applied for LC-filtered voltage source inverters (VSIs) to regulate the output voltage. To reduce the number of required sensors of the VSI under conventional MPC, the load current is generally observed. However, the performance of MPC and load current observation is easily affected by the accuracy of the model parameters. To eliminate the parametric effect and observe the load current simultaneously, this article proposes a moving-window-adaptive-observer-based data-driven predictive control (MWAO-DDPC) for LC-filtered VSIs, where the proposed MWAO-DDPC not only obtains the observed load current by using the MWAO but also achieves the data-driven voltage prediction by using the observed data-driven model coefficients. The proposed MWAO-DDPC effectively reduces the number of applied sensor and eliminates the parametric effect on voltage prediction. The experimental prototype of LC-filtered VSIs is built to verify the feasibility and performance of the proposed MWAO-DDPC.