Adaptive Neural Performance-guaranteed VSG Control for DFIG-based Wind Power Generators

The penetration of renewable energies has been growing rapidly in recent years. Thus, the need to utilize renewables in grid primary/secondary frequency regulation has become very urgent. Within this context, the virtual synchronous generator (VSG)technique is proposed. With VSG, renewable generators are endued with the ability of actively participating in grid frequency regulation; however, there exists a risk of the rotor currents exceeding the limit of rotor-side converter (RSC)during dynamics when the VSG is applied to doubly-fed induction generator (DFIG)-based wind turbines. A performance-guaranteed VSG controller for DFIGs is proposed. An error mapping function is used to transform the system with output constraints into an equivalent unconstrained one, and performance-guaranteed controllers are designed using Lyapunov synthesis. The controller can guarantee that the rotor current is within any predefined limits during strong dynamics, such as the frequency regulating process and fault ride-through. Moreover, neural network adaptive methods are utilized to compensate uncertain DFIG dynamics; thus, satisfactory control effects are achieved. Finally, through numerous simulations, the frequency regulating ability, the rotor current control effect, and the performance against parameter deviations of our controller are verified.