Vertical Azimuth Display simulator for wind-Doppler lidar error assessment

Tiana-Alsina, Jordi; Rocadenbosch, Francesc; Gutierrez-Antunano, M. A.

doi:10.1109/igarss.2017.8127282

Cited by 6 publications

(12 citation statements)

References 5 publications

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“…In previous works [40], we have presented the formulation of the VAD motion simulator under the assumption of a time-invariant and horizontally-homogeneous wind field. The simulator uses Euler's angles to compute the rotated line-of-sight vector at a given time in response to simultaneous pitch, roll, and yaw tilting angles (three degrees of freedom).…”

Section: The Velocity-azimuth-display Motion Simulatormentioning

confidence: 99%

“…In previous works [40] limited to one degree of freedom in angular motion (i.e., roll or pitch only), the authors have shown that wind direction has a relevant impact on the HWS error. In addition, under one-degree-of-freedom harmonic motion, it has been shown that the HWS error exhibits sinusoidal dependence with wind direction.…”

Section: Wind Direction Exclusionmentioning

confidence: 99%

“…A plausible explanation is as follows: the fact that roll and pitch are approximately linearly correlated in amplitude and period enables an equivalent one-degree-of-freedom treatment of buoy motion (buoy tilt "amplitude" and buoy tilt "period" ). Because the HWS error standard deviation follows a sinusoidal variation with wind direction [40] and roll and pitch axes are orthogonal (π/2 phase shift between roll and pitch sinusoidal variation with wind direction), the error standard deviation, which is the quadratic sum of roll and pitch error standard deviations, remains constant with wind direction. Similar simulations were carried out for other HWSs and angular amplitude conditions, showing analogous behaviour with wind direction.…”

Section: Wind Direction Exclusionmentioning

confidence: 99%

“…The increasing use of floating lidar systems in the offshore wind energy sector motivates the need to assess and compensate the effect of motion on floating lidar measurements [34,35]. It has been shown that both the static [36] and dynamic tilt [37][38][39][40] of the lidar instrument induce errors in the retrieved horizontal wind speed (HWS). Different approaches can be considered to reduce the impact of sea-waves-induced motion on the wind speed measured by floating lidar devices: mechanical [41,42] and numerical compensation methods [43,44].…”

Section: Introductionmentioning

confidence: 99%

“…Roll phase (φ r ) is equal to 0 deg (a), 90 deg (b), 180 deg (c), and 270 deg (d). Roll amplitude is 3.5 deg, wind vector is (0, 10, 0) m/s, and measurement height is 100 m in all panels[40].…”

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confidence: 99%

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

et al. 2018

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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).

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Section: The Velocity-azimuth-display Motion Simulatormentioning

confidence: 99%

Section: Wind Direction Exclusionmentioning

confidence: 99%

Section: Wind Direction Exclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2018

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Motional Behavior Estimation Using Simple Spectral Estimation: Application to The Off-Shore Wind Lidar

Salcedo-Bosch

Rocadenbosch

Gutierrez-Antunano

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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This paper proposes two methods to estimate the so-called "characteristic motional period" of a floating wind-lidar buoy. These techniques aim to characterise the buoy's pitch and roll tilting as simple harmonic motions by estimating their period. The "peak method" (PM) and the "3-dB method" are introduced as two different aproaches to study the multi-modality of the wave motion. Additionally, the offshore wind measurement campaign at Ijmuiden's is briefly introduced to contextualize where data comes from and the added value of this study.

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

et al. 2020

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Due to their motion, floating wind lidars overestimate turbulence intensity ( T I ) compared to fixed lidars. We show how the motion of a floating continuous-wave velocity–azimuth display (VAD) scanning lidar in all six degrees of freedom influences the T I estimates, and present a method to compensate for it. The approach presented here uses line-of-sight measurements of the lidar and high-frequency motion data. The compensation algorithm takes into account the changing radial velocity, scanning geometry, and measurement height of the lidar beam as the lidar moves and rotates. It also incorporates a strategy to synchronize lidar and motion data. We test this method with measurement data from a ZX300 mounted on a Fugro SEAWATCH Wind LiDAR Buoy deployed offshore and compare its T I estimates with and without motion compensation to measurements taken by a fixed land-based reference wind lidar of the same type located nearby. Results show that the T I values of the floating lidar without motion compensation are around 50 % higher than the reference values. The motion compensation algorithm detects the amount of motion-induced T I and removes it from the measurement data successfully. Motion compensation leads to good agreement between the T I estimates of floating and fixed lidar under all investigated wind conditions and sea states.

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Vertical Azimuth Display simulator for wind-Doppler lidar error assessment

Abstract: UPCommonsPortal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints

Cited by 6 publications

References 5 publications

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

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

Motional Behavior Estimation Using Simple Spectral Estimation: Application to The Off-Shore Wind Lidar

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

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