Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering

Abstract

In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.