Disturbance observer-based finite-time adaptive neural control scheme of DFIG-wind turbine

This paper introduces a novel disturbance observer-based finite-time adaptive neural control approach to optimize wind power conversion in a doubly fed induction generator-based wind turbines (DFIG-WT). This control strategy offers appealing features including rapid finite-time convergence, both transient and steady-state performance enhancements, and robustness against external disturbances and inherent model uncertainties. The control strategy integrates the neural networks estimation capability with the interesting proprieties of the finite-time control method to achieve efficient wind power conversion. Closed-loop finite-time stability is conducted using the finite-time Lyapunov stability concept of nonlinear systems. The developed control strategy's effectiveness is confirmed through numerical simulation.