LiDAR-mast deviations in complex terrain and their simulation using CFD

Klaas, Tobias; Pauscher, Lukas; Callies, Doron

doi:10.1127/metz/2015/0637

Cited by 34 publications

(50 citation statements)

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“…To minimise the different volume averaging effects and to comply with other comparisons of LiDAR measurements and met mast anemometers [29][30][31][32][33], we applied filtering in clustered temporal segments of ∆T = 10 min. We have deliberately refrained a data availability pre-filtering for the calculation of the 10 min average velocity and velocity standard deviation.…”

Section: Evaluation Of Filtering Based On Staring Measurementsmentioning

confidence: 99%

“…Because the measurement conditions differ with each device, location and time, it seems sensible and necessary to filter LiDAR data in a dynamically adaptive way to ensure high data availability Remote Sens. 2017, 9, 561 3 of 31 and accuracy of the data set. The simultaneous use of different filter combinations is limited by the available computational power; thus, universal filters are favoured.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Beck

Kühn

2017

Remote Sensing

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Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm 'alpha ventus'. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.

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Section: Evaluation Of Filtering Based On Staring Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Beck

Kühn

2017

Remote Sensing

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“…As indicated in [17] there are two solutions to this problem, either to correct the data using flow models [16,18,19] or to develop new CDL instruments that do not demand the horizontal homogeneity of the flow to produce accurate retrievals. The correction methods can potentially reduce errors [17].…”

Section: Introductionmentioning

confidence: 99%

“…CDL data correction methods are more successful on sites with a simple to moderate complexity [16,20], than on sites with high complexity [16]. Also, the corrected data accuracy is heavily dependent on the flow model choice and parametrization [19]. As described in [19], besides the lidar data correction methodologies developed by research groups also companies such as Meteodyn WT, WindSim and Leosphere introduced commercial lidar data correction algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the corrected data accuracy is heavily dependent on the flow model choice and parametrization [19]. As described in [19], besides the lidar data correction methodologies developed by research groups also companies such as Meteodyn WT, WindSim and Leosphere introduced commercial lidar data correction algorithms. Specifically, Leosphere developed a real-time correction algorithm, known as the Flow Complexity Recognition (FCR), which is available as an add-on to their lidar software [21].…”

Section: Introductionmentioning

confidence: 99%

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Long-range WindScanner System

Vasiljević

Lea

Courtney

et al. 2016

Preprint

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Abstract:In this paper, the technical aspects of a multi-Doppler lidar instrument, the long-range WindScanner system, will be presented accompanied by an overview of the results from several field campaigns. The long-range WindScanner system consists of three spatially separated coherent Doppler scanning lidars and a remote master computer that coordinates them. The lidars were carefully engineered to perform arbitrary and time controlled scanning trajectories. Their wireless coordination via the master computer allows achieving and maintaining lidars' synchronization within ten milliseconds. As a whole, the long-range WindScanner system can measure an entire wind field by emitting and directing three laser beams to intersect, and then by moving the beam intersection over the points of interest. The long-range WindScanner system was developed to tackle the need for high-quality observations of wind fields from scales of modern wind turbine and wind farms. It has been in operation since 2013.

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Quellen für Winddaten

Emeis¹

2022

Windenergie Meteorologie

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LiDAR-mast deviations in complex terrain and their simulation using CFD

Cited by 34 publications

References 0 publications

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Long-range WindScanner System

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