Model predictive control using multi-step prediction model for electrical yaw system of horizontal-axis wind turbines

This study presents an advanced model predictive control (MPC) using multi-step prediction model for the electrical motor-based yaw system of an industrial wind turbine. The proposed method introduces a finite control set under constraints for the demanded yaw rate, predicts the multi-step yaw error using the control set element and the prediction wind directions, and employs an exhaustive search method to search the control output candidate giving the minimal value of the objective function. As the objective function is designed for a joint power and actuator usage optimization, the weighting factor in the objective function is optimally selected using the Pareto curve-based tuning method. Finally, the proposed method is demonstrated by simulation tests using Bladed software and the achievable performance is investigated among six MPC controllers with different prediction steps. Investigation results show that, along with the extended prediction horizon, the power production is improved while maintaining the same yaw actuator usage. Meanwhile, the MPC-based yaw controllers have certain effects on the fatigue loads of the tower and yaw bearing, while their effects on the blade root and hub are slight. Besides, it is observed that the potential performance achieved by the MPC controllers is obviously affected by wind conditions.