Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

Reliable control and optimal operation of the doubly fed induction generator (DFIG) is necessary to ensure high efficiency and high load-following capability in modern wind power plants. This is often difficult to achieve using conventional linear controllers, as wind power plants are nonlinear and contain many uncertainties. Furthermore, unbalanced conditions often exist on the power network, which can degrade DFIG system performance. Considering the nonlinear DFIG dynamics, this paper proposes a nonlinear modeling technique for DFIG, meanwhile taking into account unbalanced grid conditions. Then, a nonlinear model predictive controller is derived for power control of DFIG. The prediction is calculated based on the input-output feedback linearization (IOFL) scheme. The control is derived by optimization of an objective function that considers both economic and tracking factors under realistic constraints. The simulation results show that the proposed controller can effectively reduce wear and tear of generating units under normal grid conditions, and reduce the rotor over-current under unbalanced grid conditions, thereby improving the ability of grid-connected wind turbines to withstand grid voltage faults.