Nonlinear model predictive control via intelligent optimization for maximum wind energy extraction of large-scale wind turbines

In this paper, a novel nonlinear model predictive control via intelligent optimization is proposed to extract maximum wind energy for large-scale wind turbines. A 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. Meanwhile, a new Yin-Yang grey wolf optimization algorithm is presented, which can achieve the efficient convergence and global optimum. Simulation results verify the effectiveness and superiority of the proposed nonlinear model predictive control via the new optimization algorithm.