Estimation of the Motion-Induced Horizontal-Wind-Speed Standard Deviation in an Offshore Doppler Lidar

Abstract

This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to −0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected).

Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

“…Gutiérrez-Antuñano et al [33] focused on motion correction of data from a floating vertical profiling coherent continuous wave lidar observing in the Netherlands. The comparison of the observations from the floating lidar uncorrected and corrected were done to sonic anemometer measurements and data from a fixed vertical wind-profiling lidar nearby during 60 days of observations.…”

Estimation of Wave Period from Pitch and Roll of a Lidar Buoy

“…On the other hand, floating DWLs suffer wave-induced errors on wind measurements [ 8 ]. Sea waves induce translational (sway, surge, and heave for the x , y , and z axes, respectively) and rotational (roll, pitch, and yaw for the x , y , and z axes, respectively) motion to the floating DWL, which accounts for 6 degrees of freedom (DoF), creating a Doppler effect over the wind vector retrieval and turbulence intensity (TI) [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ], with errors of about 10% in horizontal wind speed (HWS). and 40% in TI [ 11 ].…”

“…The most prominent peak of these 2 FFTs was chosen as the most relevant spectral component, and the period was estimated as the inverse of the frequency corresponding to it. In [ 11 ], the roll and pitch tilt periods were virtually correlated ( ); thus, 1 DoF was considered informative of the buoy’s motional wave period. In [ 26 ], two estimation methods to assess the wave period from pitch and roll measurements based on Blackman–Tukey power-spectral-density (PSD) estimation method were presented.…”

Taking the Motion out of Floating Lidar: Turbulence Intensity Estimates with a Continuous-Wave Wind Lidar

“…By contrast, estimates of turbulence intensity (TI) require advanced motion compensation because floating lidar systems show stronger wind velocity fluctuations than non-moving lidars [5]. The magnitude of this motion-induced error depends on the amplitude and period of the motion which result from the floating platform type used and the prevailing sea state [7]. Trusting in such erroneously high TI values could, for example, result in extra costs caused by choosing overdesigned wind turbines.…”

“…For many setups with currently available hardware, this is not the case. Gutiérrez-Antuñano et al [7] presented a simulation tool for more advanced motion compensation. Based on amplitude and period of the buoy rotation, and mean wind conditions, the simulator estimates the motion-induced error in the turbulence measurements.…”