Wind turbine inflow estimation via nested, self-calibrating EKF: Design and field test

• Robust estimation of wind turbine inflow and structural dynamics. • Field test with a modern, 4.2 MW utility-scale wind turbine. • Reference measurements of inflow via hub mounted spinner lidar. • Reference measurements of structural displacements via digital image correlation. • Wind estimate has accuracy of 0.3m/s (RMSE) and follows expected turbulence spectra. Modern wind turbines need high situational awareness for decision making and control scheduling. It allows for a trade-off between greedy power maximisation and further control objectives such as load alleviation or grid compliance. The paper formulates a nested Extended Kalman Filter that is able to precisely reconstruct and distinguish the inflow wind and load-relevant structural dynamics of a wind turbine. The formulation is directly motivated by the demands of field applicability, thus robust, low in computational costs and flexible with respect to sensor availability or calibration drifts. The estimator is field-tested on a utility-scale commercial turbine. It shows good agreement with independent reference measurements, namely a hub-mounted scanning lidar for the spatially resolved inflow wind field and camera-based digital image correlation for the structural movements. The paper documents the estimator behaviour at different control regimes, including dynamic pitch actuation, where the onset of dynamic inflow effect becomes visible.