Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability

Reactive power control of doubly fed induction generators (DFIGs) has been a heated topic in transient stability control of power systems in recent years. By using a new online supplementary learning control (OSLC) approach based on the theory of approximate dynamic programming (ADP), this paper develops an optimal and adaptive design method for the supplementary reactive power control of DFIGs to improve transient stability of power systems. To augment the reactive power command of the rotor-side converter (RSC), a supplementary controller is designed to reduce voltage sag at the common coupling point during a fault, and to mitigate active power oscillation of the wind farm after a fault. As a result, the transient stability of both DFIGs and the power system is enhanced. For the supplementary controller design, an action dependent cost function is introduced to make the OSLC model-free and completely data-driven. Furthermore, a least-squares based policy iteration algorithm is employed to train the supplementary controller with convergence and stability guarantee. By using such techniques, the supplementary reactive power controller can be trained directly from data measurements, and therefore, can adapt to system or external changes without an explicit offline system identification process. Simulations carried out in Power System Computer Aided Design/ Electro Magnetic Transient in DC System (PSCAD/EMTDC) show that the OSLC based supplementary reactive power controller can significantly improve the transient performance of the wind farm and enhance the transient stability of the power system after sever faults.