Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
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 du...
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doaj-08df931b296f4c9db4112c9697df20782020-11-24T21:55:30ZengMDPI AGRemote Sensing2072-42922017-06-019656110.3390/rs9060561rs9060561Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed MeasurementsHauke Beck0Martin Kühn1ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyDoppler 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.http://www.mdpi.com/2072-4292/9/6/561data densityspatial normalisationtemporal normalisationcarrier-to-noise-ratioline-of-sight velocityradial velocitythreshold filter |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hauke Beck Martin Kühn |
spellingShingle |
Hauke Beck Martin Kühn Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements Remote Sensing data density spatial normalisation temporal normalisation carrier-to-noise-ratio line-of-sight velocity radial velocity threshold filter |
author_facet |
Hauke Beck Martin Kühn |
author_sort |
Hauke Beck |
title |
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements |
title_short |
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements |
title_full |
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements |
title_fullStr |
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements |
title_full_unstemmed |
Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements |
title_sort |
dynamic data filtering of long-range doppler lidar wind speed measurements |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2017-06-01 |
description |
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. |
topic |
data density spatial normalisation temporal normalisation carrier-to-noise-ratio line-of-sight velocity radial velocity threshold filter |
url |
http://www.mdpi.com/2072-4292/9/6/561 |
work_keys_str_mv |
AT haukebeck dynamicdatafilteringoflongrangedopplerlidarwindspeedmeasurements AT martinkuhn dynamicdatafilteringoflongrangedopplerlidarwindspeedmeasurements |
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1725862270302420992 |