Abstract. Wind farm control concepts require awareness and observation methods of the inner-farm flow field. The relative location of the wake, to which a downstream turbine is exposed, is of great interest. It can be used as feedback to support closed-loop wake-steering control, ultimately leading to higher power extraction and fatigue load reduction. With increasing fidelity, not only time-averaged wakes but also instantaneous wake conditions, subject to meandering and wind direction changes, are considered within a controller. This paper presents a quantitative field comparison of two independently applied wake centre estimation methods: a scanning lidar and an extended Kalman filter (EKF) based on the rotor loads of the waked turbine. No ground truth is available in the field environment, therefore the methodology accounts for the fact that two uncertain estimates are compared. The lidar estimates, with a derived uncertainty in the order of 0.05 rotor diameters D, can be used as a suitably precise reference to draw conclusions about the load-based EKF. The EKF uses Coleman-transformed blade root bending moments, linked to the wake centre position via an analytical model with a low number of tuning parameters. The model can easily be trained with aeroelastic simulations, including the dynamic wake meandering model. The formulation adds robustness to the tracking and allows the user to determine the confidence in the wake position estimate, which can be used for wake impingement detection or for a wake-steering controller to judge whether a yaw manoeuvre is adequate. The results indicate agreement of the methods with root mean square errors of 0.2D for low and moderate turbulence intensity, and 0.3D for turbulence intensities above 12 %. The paper focuses on wake position estimation but also outlines a methodology for validating wind farm models and wind field reconstruction techniques with complementary lidar data.
Onnen et al. (Fri,) studied this question.