Posters

Abstract

Introduction Accurate wind measurements are vital for assessing wind resources, but turbulence complicates data collection. Turbulence intensity (TI) impacts turbine performance, making precise TI data critical. Ground-based lidars offer an alternative to meteorological masts, though its adoption is limited by uncertainty in turbulence readings. Reducing these uncertainties is key to improving site assessments, turbine control, and the use of lidar technology.  Approach This study investigates a novel method for adjusting ground-based lidar turbulence intensity measurements to reduce systematic uncertainty. The approach is based on a newly derived turbulence intensity equation using perturbation theory, which forms the basis of an adjustment model. The method is validated against established standards and evaluated using both virtual test environments and measurements from an operational onshore wind site in the Netherlands. Additional insights from a joint-industry project are used to benchmark and compare the proposed method against existing adjustment approaches. Main Body of the Abstract The results of this research demonstrate that unadjusted ground-based lidar measurements systematically underestimate turbulence intensity when compared to reference meteorological mast data at this site. Application of the perturbation-based adjustment significantly reduced this discrepancy across a range of wind speeds and turbulence regimes. In controlled virtual environments, the adjusted lidar turbulence intensity showed improved agreement with reference values, confirming the theoretical validity of the approach. Field measurements at the onshore test site further demonstrated a consistent reduction in measurement uncertainty, with improved alignment between lidar-derived and mast-based turbulence. Compared to alternative adjustment techniques, the proposed method showed promising results across varying atmospheric conditions. The study highlights that the perturbation-based formulation captures key physical effects that are neglected in conventional correction methods. Incorporating findings from the joint-industry project allowed for a framework to test the method against industry standards, further improving the quality of the results. Overall, the results indicate that the proposed adjustment method improves the reliability of lidar-based turbulence measurements for wind energy applications. Conclusions The study demonstrates that perturbation-based adjustment of lidar turbulence intensity measurements can significantly reduce uncertainty and improve consistency with traditional mast-based observations. For wind farm developers, turbine manufacturers, and operators, this enhances the reliability of lidar data for control optimization, load assessment, and wind resource analysis. Improved accuracy also supports wider acceptance of lidar as a standalone measurement technology in wind energy applications. Learning Objectives After this presentation, delegates will be able to (1) understand the sources of uncertainty in lidar-based turbulence intensity measurements, (2) assess the benefits of perturbation-based adjustment methods compared to conventional approaches, and (3) apply practical insights to improve lidar data quality and decision-making in wind energy projects.