Robust Predictive Control Design of Wind Power under Nonlinear Constraints

Abstract

This paper aims to develop a novel control architecture to enhance the performance of DFIG-based wind turbines. The proposed approach integrates an adaptive robust predictive control law to ensure stable power output despite stochastic wind variations and grid disturbances. The control design is formulated using Linear Matrix Inequality (LMI) optimization and Lyapunov stability theory, and its effectiveness is validated through numerical simulations. The results demonstrate faster convergence and improved system robustness, highlighting the potential of the proposed strategy as a scalable solution for the efficient integration of renewable energy into modern power systems.