Wind-field estimation for lidar-assisted control: A comparison of proper orthogonal decomposition and interpolation techniques

Abstract. This study presents and evaluates three wind field reconstruction methods for real-time inflow characterization, with potential applications in lidar-assisted wind turbine control. The first method applies a least-squares fit of proper orthogonal decomposition (POD) modes to lidar measurements (POD-LSQ). The second uses inverse distance weighting (IDW) interpolation across the rotor plane. The third, POD-IDW, applies the POD-LSQ fit to the interpolated field. The methods are tested under semi-realistic conditions derived from large-eddy simulations (LES), using a hub-mounted lidar sensor implemented in HAWC2 on the DTU 10MWreference turbine. Measurements are extracted under varying inflow conditions. A rotor-effective wind speed estimate, combined with the known vertical shear profile from LES, serves as the baseline for comparison. Reconstruction performance is quantified using a global mean absolute error, evaluated across combinations of scan count, POD mode number, and lidar beam angle. Optimal parameters are selected based on the minimum error. To assess physical accuracy, reconstructions are compared against true wind speeds, evaluating the effects of probe volume averaging, multi-distance measurement selection, cross-contamination, and other sources of error. For optimal inputs, POD-IDW achieves the highest accuracy, reducing error by 45.5 % compared with the baseline estimation, at 5.4 times the computational cost. IDW performs similarly (44.9 %) with optimal inputs, while POD-LSQ achieves a 39.4 % reduction with minimal overhead (7 %). Spectral analysis shows that volume averaging and scanning strategies introduce low-pass filtering that attenuates high-frequency turbulence, while preserving low-frequency content more accurately than the baseline. Reconstruction quality strongly depends on the number and spatial distribution of lidar measurements and the number of retained POD modes. Although demonstrated under idealized conditions, the methods show strong potential for real-time applications. Future work should integrate these reconstructions with flow-aware controllers to evaluate fatigue load reduction, particularly at tower level.