Floating Doppler Wind Lidar Measurement of Wind Turbulence: A Cluster Analysis

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

doi:10.1109/igarss39084.2020.9323578

Cited by 7 publications

(11 citation statements)

References 5 publications

(5 reference statements)

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“…This was because buoy motion added an apparent variance to the HWS measurements, which increased the LiDAR-measured turbulence. The linear regression (LR, red dashed-dot line) offset of −0.0185 indicated the amount of added turbulence [14]. The LR slope of 1.0358, which is virtually identical to the ideal unity slope, indicates that the apparent turbulence equally affected all HWS measurements.…”

Section: Ukf Resultsmentioning

confidence: 87%

“…Multiple validation campaigns have shown the robustness and reliability of horizontal wind speed (HWS) and wind direction (WD) FDWL measurements at the ten-minute level [9][10][11][12][13]. However, FDWLs measure an increased turbulence intensity (TI), in contrast to fixed Doppler Wind LiDARs (DWLs), due to wave-induced motion [14]. TI is defined as the ratio between the standard deviation of the HWS to the mean HWS.…”

Section: Introductionmentioning

confidence: 99%

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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: Ukf Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Moreover, the wave-induced buoy motion causes an apparent turbulence addition to the TI measurements by FDWLs in comparison to fixed DWLs. [4].…”

Section: Introductionmentioning

confidence: 99%

On Adaptive Unscented Kalman Filtering for Floating Doppler Wind-Lidar Motion Correction: Effect of the Number of Lidar Measurement Heights

Salcedo-Bosch

Rocadenbosch

Sospedra

2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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This work studies the influence of the number of lidar measurement heights on the performance of the floating Doppler wind lidar motion-correction algorithm, recently published by the authors. The work is in the context of offshore wind energy and continuous-wave focusable Zephir T M 300 lidar. A down-sampling technique applied over the lidar-measured wind speed time-series is used to simulate different height-sounding configurations. The operation of the filter under one, three, and five measurement heights of the lidar is studied by using data from El Pont del Petroli measurement campaign. The filter is proved to remove apparent turbulence addition in all three cases, showing a deterioration of statistical indicators as the number of sounding heights increase.

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“…When measuring wind turbulence intensity (TI), FDWLs measure higher TI due to the wave-induced variance addition [14,17]. TI, defined as the ratio of the HWS standard deviation to the mean HWS, is one of the most relevant parameters to be measured in wind energy due to its relevance on wind turbine operation and power production [18,19].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models

2022

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An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to −0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2) improved from 0.90 to 0.96.

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Floating Doppler Wind Lidar Measurement of Wind Turbulence: A Cluster Analysis

Cited by 7 publications

References 5 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

On Adaptive Unscented Kalman Filtering for Floating Doppler Wind-Lidar Motion Correction: Effect of the Number of Lidar Measurement Heights

Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models

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