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

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

doi:10.1109/igarss39084.2020.9323801

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…) is computed. The window length chosen is the wave period over the 10 min series, which is estimated by means of the L-dB method [38] (other wave-period estimation methods in the literature [39,40] yielded virtually identical results). The wind component of the state-vector is initialized by retaining the first-time sample of the proxy wind, U U U proxy k…”

Section: Filter Initializationmentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

2021

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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.

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Section: Filter Initializationmentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

2021

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“…Moreover, in contrast to metmasts, they can easily be redeployed and thus cover large areas [21]. On the other hand, FDWLs suffer 6 degrees of freedom (DoF) motion, induced by the waves [22][23][24], which increases the variance on the reconstructed wind vector by the LiDAR [25,26]. However, in wind energy standard averaging periods, typically 10 or 30 min, the motion-induced error on the retrieved mean wind vector can be neglected, as it is averaged out [25,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

et al. 2023

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This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating ZephIRTM 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed.

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

Salcedo-Bosch

Rocadenbosch

Gutierrez-Antunano

et al. 2021

Sensors

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This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, ρ = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.

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

Cited by 3 publications

References 6 publications

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction

A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

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

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