Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-77-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/wes-2-77-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Feb 2017
Research article |
| 10 Feb 2017
An error reduction algorithm to improve lidar turbulence estimates for wind energy
Jennifer F. Newman
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
Andrew Clifton
Power Systems Engineering Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
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Cited
17 citations as recorded by crossref.
- Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimations F. Costa et al. 10.1088/1742-6596/2626/1/012020
- Combined wind lidar and cloud radar for high-resolution wind profiling J. Dias Neto et al. 10.5194/essd-15-769-2023
- Unlocking the potential: A review of artificial intelligence applications in wind energy S. Dörterler et al. 10.1111/exsy.13716
- Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements D. Conti et al. 10.5194/wes-5-1129-2020
- Caracterização do perfil vertical do vento em Iperó (São Paulo) com o uso de um lidar doppler C. Leme Beu & E. Landulfo 10.55761/abclima.v30i18.15582
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. 10.5194/wes-7-2231-2022
- Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake F. Carbajo Fuertes & F. Porté-Agel 10.3390/rs10050721
- A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation M. Pervez et al. 10.1016/j.asej.2021.09.004
- Accuracy Verification of Multiple Floating LiDARs at the Mutsu-Ogawara Site S. Uchiyama et al. 10.3390/en17133164
- Comparison of methods to derive radial wind speed from a continuous-wave coherent lidar Doppler spectrum D. Held & J. Mann 10.5194/amt-11-6339-2018
- Multirotor UAV-Based Platform for the Measurement of Atmospheric Turbulence: Validation and Signature Detection of Tip Vortices of Wind Turbine Blades F. Fuertes et al. 10.1175/JTECH-D-17-0220.1
- Lidar Methods and Tools for Studying the Atmospheric Turbulence at the Institute of Atmospheric Optics V. Banakh 10.1134/S1024856020010042
- Wind–Temperature Regime and Wind Turbulence in a Stable Boundary Layer of the Atmosphere: Case Study V. Banakh et al. 10.3390/rs12060955
- A Hybrid Genetic/Powell Algorithm for Wind Measurement in Doppler Lidar S. Jiang et al. 10.3390/photonics9110802
- Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity J. Gottschall et al. 10.1002/wene.250
- Lidar Estimates of the Anisotropy of Wind Turbulence in a Stable Atmospheric Boundary Layer V. Banakh & I. Smalikho 10.3390/rs11182115
- Improving lidar turbulence estimates for wind energy J. Newman et al. 10.1088/1742-6596/753/7/072010
16 citations as recorded by crossref.
- Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimations F. Costa et al. 10.1088/1742-6596/2626/1/012020
- Combined wind lidar and cloud radar for high-resolution wind profiling J. Dias Neto et al. 10.5194/essd-15-769-2023
- Unlocking the potential: A review of artificial intelligence applications in wind energy S. Dörterler et al. 10.1111/exsy.13716
- Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements D. Conti et al. 10.5194/wes-5-1129-2020
- Caracterização do perfil vertical do vento em Iperó (São Paulo) com o uso de um lidar doppler C. Leme Beu & E. Landulfo 10.55761/abclima.v30i18.15582
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. 10.5194/wes-7-2231-2022
- Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake F. Carbajo Fuertes & F. Porté-Agel 10.3390/rs10050721
- A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation M. Pervez et al. 10.1016/j.asej.2021.09.004
- Accuracy Verification of Multiple Floating LiDARs at the Mutsu-Ogawara Site S. Uchiyama et al. 10.3390/en17133164
- Comparison of methods to derive radial wind speed from a continuous-wave coherent lidar Doppler spectrum D. Held & J. Mann 10.5194/amt-11-6339-2018
- Multirotor UAV-Based Platform for the Measurement of Atmospheric Turbulence: Validation and Signature Detection of Tip Vortices of Wind Turbine Blades F. Fuertes et al. 10.1175/JTECH-D-17-0220.1
- Lidar Methods and Tools for Studying the Atmospheric Turbulence at the Institute of Atmospheric Optics V. Banakh 10.1134/S1024856020010042
- Wind–Temperature Regime and Wind Turbulence in a Stable Boundary Layer of the Atmosphere: Case Study V. Banakh et al. 10.3390/rs12060955
- A Hybrid Genetic/Powell Algorithm for Wind Measurement in Doppler Lidar S. Jiang et al. 10.3390/photonics9110802
- Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity J. Gottschall et al. 10.1002/wene.250
- Lidar Estimates of the Anisotropy of Wind Turbulence in a Stable Atmospheric Boundary Layer V. Banakh & I. Smalikho 10.3390/rs11182115
1 citations as recorded by crossref.
Latest update: 14 Dec 2024
Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure...
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