Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Abstract For modern horizontal axis wind turbines (WTs), a yaw drive mechanism is utilized to adjust the nacelle position to face the wind direction. Depending on historical signals from wind direction sensors, conventional yaw control methods could not provide sufficient performance in tracking winds, and thus result in a reduction of wind power extraction. This issue needs to be tackled using advanced control solutions. Taking advantage of predicted wind directions, a novel control solution is proposed in this study. Specifically, the proposed solution refers to a novel control structure that consists of a wind direction predictive model and a novel yaw control method. Under the proposed control structure, a hybrid autoregressive integrated moving average method-based Kalman filter (ARIMA-KF) model is used to predict the wind direction, and two novel yaw control methods are proposed: one created by using the predicted wind direction as the tracking reference, and the other based on a model predictive control (MPC) using a finite control set. To demonstrate the feasibility and the superiority of the proposed solution, two novel yaw controllers are developed and tested through some simulation tests using industrial data. Their performance is compared to the one of two industrial yaw controllers. Comparison results show that the two novel yaw controllers are capable of reducing yaw error, and thus increase wind power extraction for the WTs. Meanwhile, it is noticeable that the MPC-based controller has an advantage in the aspect of reducing yaw actuator usage.

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