Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-841-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-841-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2021
Research article |
| 07 Jun 2021
| 07 Jun 2021
Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Vasilis Pettas
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Nikolay Dimitrov
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Alfredo Peña
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Cited
21 citations as recorded by crossref.
- Impact of rotor size on aeroelastic uncertainty with lidar-constrained turbulence J. Rinker 10.1088/1742-6596/2265/3/032011
- Influence of nacelle-lidar scanning patterns on inflow turbulence characterization W. Fu et al. 10.1088/1742-6596/2265/2/022016
- Wind field reconstruction using nacelle based lidar measurements for floating wind turbines M. Gräfe et al. 10.1088/1742-6596/2265/4/042022
- Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines M. Gräfe et al. 10.5194/wes-8-925-2023
- Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics D. Conti et al. 10.5194/wes-6-1117-2021
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
- Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review W. Li et al. 10.3390/su142417051
- Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach Z. Luo et al. 10.1016/j.renene.2024.121552
- Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel H. Kalhori et al. 10.1016/j.ijimpeng.2023.104576
- Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation P. Meyer & J. Gottschall 10.1088/1742-6596/2265/2/022107
- Dependence of turbulence estimations on nacelle lidar scanning strategies W. Fu et al. 10.5194/wes-8-677-2023
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques Y. Wang et al. 10.1016/j.energy.2021.121825
- An open-source Python-based tool for Mann turbulence generation with constraints and non-Gaussian capabilities N. Dimitrov et al. 10.1088/1742-6596/2767/5/052058
- Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks J. Zhang & X. Zhao 10.1088/1742-6596/2767/9/092017
- Investigating Suppression of Cloud Return with a Novel Optical Configuration of a Doppler Lidar L. Jin et al. 10.3390/rs14153576
- Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements M. Gräfe et al. 10.5194/wes-9-2175-2024
- Turbulence statistics from three different nacelle lidars W. Fu et al. 10.5194/wes-7-831-2022
- Prediction of wind fields in mountains at multiple elevations using deep learning models H. Gao et al. 10.1016/j.apenergy.2023.122099
- One-year-long turbulence measurements and modeling using large-eddy simulation domains in the Weather Research and Forecasting model A. Peña & J. Mirocha 10.1016/j.apenergy.2024.123069
- Validation of new and existing methods for time-domain simulations of turbulence and loads P. Doubrawa et al. 10.1088/1742-6596/2767/5/052057
21 citations as recorded by crossref.
- Impact of rotor size on aeroelastic uncertainty with lidar-constrained turbulence J. Rinker 10.1088/1742-6596/2265/3/032011
- Influence of nacelle-lidar scanning patterns on inflow turbulence characterization W. Fu et al. 10.1088/1742-6596/2265/2/022016
- Wind field reconstruction using nacelle based lidar measurements for floating wind turbines M. Gräfe et al. 10.1088/1742-6596/2265/4/042022
- Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines M. Gräfe et al. 10.5194/wes-8-925-2023
- Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics D. Conti et al. 10.5194/wes-6-1117-2021
- Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
- Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review W. Li et al. 10.3390/su142417051
- Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach Z. Luo et al. 10.1016/j.renene.2024.121552
- Randomized Kaczmarz and Landweber algorithms for impact force identification on a composite panel H. Kalhori et al. 10.1016/j.ijimpeng.2023.104576
- Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation P. Meyer & J. Gottschall 10.1088/1742-6596/2265/2/022107
- Dependence of turbulence estimations on nacelle lidar scanning strategies W. Fu et al. 10.5194/wes-8-677-2023
- Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
- Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques Y. Wang et al. 10.1016/j.energy.2021.121825
- An open-source Python-based tool for Mann turbulence generation with constraints and non-Gaussian capabilities N. Dimitrov et al. 10.1088/1742-6596/2767/5/052058
- Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks J. Zhang & X. Zhao 10.1088/1742-6596/2767/9/092017
- Investigating Suppression of Cloud Return with a Novel Optical Configuration of a Doppler Lidar L. Jin et al. 10.3390/rs14153576
- Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements M. Gräfe et al. 10.5194/wes-9-2175-2024
- Turbulence statistics from three different nacelle lidars W. Fu et al. 10.5194/wes-7-831-2022
- Prediction of wind fields in mountains at multiple elevations using deep learning models H. Gao et al. 10.1016/j.apenergy.2023.122099
- One-year-long turbulence measurements and modeling using large-eddy simulation domains in the Weather Research and Forecasting model A. Peña & J. Mirocha 10.1016/j.apenergy.2024.123069
- Validation of new and existing methods for time-domain simulations of turbulence and loads P. Doubrawa et al. 10.1088/1742-6596/2767/5/052057
Latest update: 20 Nov 2024
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment...
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