Remote sensing sensitivities workshop
Deutsche WindGuard and Leosphere co-organized a workshop dedicated to remote sensing sensitivities. As this topic has appeared in a fragmented manner in the past, it is an excellent opportunity to learn about some relevant results and to work with the community towards a common understanding. The discussion was supported by several presentations:
Deutsche WindGuard: Observed Reduction of Sensitivities of Windcube Measurements by Vector Averaging
In a recent case of a commercial power curve test, unexpectedly high deviations of LiDAR measurement with an adjacent met mast occurred under some specific conditions. These conditions had not been seen with any frequency prior to this specific campaign. The same LiDAR unit had shown high accuracy at a prior calibration carried out at another site. A deep analysis of the measurement data at the power curve test and the calibration as well as of data from further LiDAR of some type at both sites has led to larger sensitivities of the LiDAR measurements to the turbulence intensity than known from a previous classification test. This large sensitivity was found to be reduced to nearly zero by vector averaging, instead of the default scalar averaging procedure.
Leosphere: Influence of Environmental Parameters on Windcube Accuracy for Different Averaging Method
Given the critical importance of wind measurements for wind energy projects, wind measurement sensors must achieve high performance, including high predictability and repeatability. This requires that all the deviations of the sensor from the current reference must be, as much as possible, captured and understood. More specifically, gaining this understanding would imply that the deviations should not just be considered as random errors due to the site or unit but integrated into the measurement. Although the classification test permits the capture of the impact of environmental variables on deviations, it does not provide reasons why a remote sensor may have a particular sensitivity. For example, the turbulence intensity, the most influential environmental variable, impacts the accuracy through the averaging method. The presentation will explain why.
