Posters

Abstract

Floating Lidar systems (FLS) are an industry accepted solution for offshore wind resource assessment. Their accuracy and reliability to measure the 10-minute average wind speed has been widely tested. However, it is known that, under specific conditions, FLS might overestimate high velocities at high heights. In this study, efforts are made to neutralize this effect. In particular, we attempt to reduce the average horizontal wind speed measurement error of the EOLOS-FLS200, at high wind speeds and heights. The EOLOS-FLS200 is a floating lidar system incorporating several other metocean instruments, which has been certified with stage-3 full commercial maturity. To that end, the wind profile power law is used to predict the wind speed at higher heights, where an overestimation of the wind speed is susceptible to happen. The relative error between the predicted and measured wind speeds is computed and used to flag measurements with high errors. Furthermore, the predicted values are used as a corrective substitute of the flagged value. To validate the proposed method, we use datasets of several FLS200 validation campaigns at the Lichteiland Goeree (LEG) platform, which mounts a fixed reference LiDAR (FRL). Measurements from the FRL are correlated with concurrent measurements from the FLS200 both corrected and uncorrected. The correlation KPIs (r2, slope, offset) of the corrected case are found to be better than those for the uncorrected data. The improvement is higher for higher heights. The results suggest that this correction method could be applied to measurement campaigns, to obtain more accurate results for high wind speeds. An extension of this work would consist of refining the wind profile characterization to predict wind speeds at higher heights more accurately, for example with the help of machine learning techniques.