Multi-objective model predictive control of doubly-fed induction generators for wind energy conversion

As large-scale integration of wind systems into the power grid is on the rise, advanced control techniques for wind power generators are highly desired. This paper proposes a simple but effective control technique for doubly fed induction generators (DFIGs) based on the multi-objective model predictive control (MOMPC) scheme. The future behaviors of the DFIGs are predicted by using the system model and the possible converter switching states. The most appropriate vector is then determined by a cost function. By properly modifying the cost function with active and reactive powers as the control objectives, fast grid synchronisation, smooth grid connection, flexible power regulation and maximum power point tracking (MPPT) can be achieved, respectively. In order to reduce the switching frequency for switching loss reduction, a nonlinear constraint is integrated into the cost function. The controller is simple without using any Proportion Integration (PI) regulators, current loops, and switching tables. A numerical simulation of a 2MW system based on MATLAB/Simulink is built to verify the effectiveness of the proposed method. The results show that the proposed method can achieve quicker transient response, better steady-state performance, and lower switching frequency compared to the conventional switching table based direct power control (DPC).