Adaptive Model Predictive Control of DFIG-based Wind Farm: A Model-Free Control Approach

In this paper a new control strategy of doubly-fed induction generator based wind farms (DFIG-based WFs) is proposed. Since DFIG has an inherent nonlinear behaviour along with condition variant parameters, the system model cannot easily be extracted. Therefore, the conventional model predictive control (MPC) of DFIG-based WFs cannot perform accurately. In this paper a novel model-free adaptive MPC structure is presented to adaptively update the system model by utilizing model identification and auto-regressive moving average (AR-MAX) model, for each sampling time. Simulation results verify the performance of the proposed control structure of DFIG-based WFs compared to the conventional control strategies.