Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm

Highlights: • Novel nonlinear model predictive control via intelligent algorithm. • Maximum wind energy extraction formula considering torque fluctuation. • Fast convergence and high search accuracy by YYGWO algorithm. • Increased energy extraction efficiency under different wind conditions. Wind energy extraction for large-scale variable-speed wind turbines could be improved by nonlinear model predictive control. However, the latter entails a sequential global optimization problem with a nonconvex cost function that brings about the heavily computational burden and impedes its real-time application. In this paper, a novel nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm is proposed for maximum wind energy extraction of wind turbines. A dynamic optimization problem with both state and control constraints is constructed, and the single-objective nonlinear function is formulated by using a weighting factor to integrate the extracted wind energy and the generator torque variation within a prediction period of several seconds. On this basis, a complete framework of the nonlinear model predictive control via intelligent algorithm is developed to offer a new paradigm for the design and implementation of the nonlinear model predictive control for wind turbines. Specifically, a new Yin-Yang grey wolf optimization algorithm is proposed, in which the concept of balance between cooperation and competition inspired by Yin-Yang-pair optimization is adopted to achieve the efficient convergence and global optimum. Simulation results verify the superiority of the proposed nonlinear model predictive control via the new Yin-Yang grey wolf optimization algorithm.