Evaluating Yawed Turbine Transfer Functions from SCADA Data

Abstract. While nacelle transfer functions (NTFs) have been applied to correct free-stream wind speed measurements at the nacelle of turbines steered into the wind, less is known about the relationship between unsteered (non-yawed) and steered (yawed) wind measurements on turbines performing wake steering. As wake steering becomes an important tool for maximizing collective wind farm power, determining and correcting bias caused by prolonged yaw misalignment on wind measurements is critical to improving collective wind farm control and analysis. We propose a new approach for evaluating NTFs using SCADA data. Using SCADA and wake steering controller 1-minute statistics recorded over 3.5 months at a large utility-scale wind plant, we apply several consensus methods to estimate unsteered turbine measurements for steered turbines using neighboring turbines. Then a bagged tree regressor algorithm is trained to predict the unsteered wind direction, wind speed, and generator power using the measured SCADA data during wake steering using the best consensus estimate as the target value. With the NTFs estimated through the ML model, we define experimentally determined non-linear sensor bias in the measured data as a function of yaw angle.