Event-Triggered Robust Optimized Scheme with Impulse Control for DFIG Based Wind Turbine

This paper proposes an event-triggered impulse optimized control strategy for a model-free doubly-fed induction generator (DFIG) based wind turbine. Ignite from the Hamilton–Jacobi–Bellman (HJB) optimal control conditions, the stable and robust controls of DFIG under various uncertain conditions, covering generator parameters altering or grid perturbations, are realized. Bypassing the knotty model, a model-free local rotor current loop control model is proposed. Time-varying perturbations in the current (state) and voltage (control signal) of both single-cage and double-cage generators (SCIG and DCIG) are employed in simulating unstructured uncertainties to improve control performance. Unlike the traditional event-triggered optimization control, besides the general robust control conditions of the model-free DFIG, the accurate impulse compensation is synchronously derived with the adaptive neural network of the HJB optimization. Therefore, computation and transmission costs are minimized without affecting the system's stability, and adaptive impulse control is implemented for real-time tuning. Finally, the performance of the proposed controller is verified with a 1.5-MW DFIG model. Superior to proportional-integral resonant control and sliding mode control, better dynamic performance and anti-interference ability, and improved robustness under divergent uncertain conditions such as generator parameter changes or grid disturbances are comprehensively demonstrated.