Abstract
This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity–azimuth display algorithm, and a wind model describing the LiDAR-retrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at “El Pont del Petroli”, Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from −1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93.
Highlights
The data set used for validation of the motion-correction algorithm comprised data from 6 to 30 June of 2013, with both LiDARs measuring at a fixed height of 100 m; (i) wind-LiDAR data from the floating Doppler Wind-LiDAR (FDWL), (ii) FDWL internal status parameters, and (iii)
The filter converged in most cases, achieving successful motion correction when compared to the reference fixed LiDAR
An adaptive method for 6-degrees of freedom (DoF) motion compensation of ZephIR 300 FDWL wind measurements was presented in this paper
Summary
The wind energy (WE) industry has shown a rising interest in deploying offshore wind farms, due to the higher and more homogeneous winds that can be found in open-sea environments [1,2]. Important investments have been made in Europe, in terms of deploying and operating offshore wind farms [3]. One of the main concerns is to obtain trustable data to assess the viability of future offshore wind farm projects [6]. As offshore wind farms are deployed further offshore into deeper waters [8], metmasts are not a feasible solution. FDWLs are a cost-effective alternative to metmasts, which can assess the wind resource in a more flexible way [5]
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