Articles | Volume 6, issue 5
https://doi.org/10.5194/wes-6-1117-2021
© 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-6-1117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2021
Research article |
| 09 Sep 2021
| 09 Sep 2021
Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
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
Thomas Herges
Wind Energy Technologies, Sandia National Laboratories, Albuquerque, New Mexico 87123, USA
Related authors
No articles found.
Sinem Uluocak, Andreas Rott, Alfredo Peña, and Martin Kühn
EGUsphere, https://doi.org/10.5194/egusphere-2026-825, https://doi.org/10.5194/egusphere-2026-825, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Wind lidar turbulence measurements are limited by large probe volume of lidar, which attenuates turbulence scales smaller than the probe volume. This process is known as probe volume averaging. This wind tunnel study enabled an experimental investigation of probe volume averaging for the first time, using 32 distinct probe volumes over integral length scale ratios. The study demonstrated the capability of lidar to measure along-wind turbulence statistics.
Bruno Rodrigues Faria, Nikolay Dimitrov, Nikhil Sudhakaran, Matthias Stammler, Athanasios Kolios, W. Dheelibun Remigius, Xiaodong Zhang, and Asger Bech Abrahamsen
Wind Energ. Sci., 11, 1583–1606, https://doi.org/10.5194/wes-11-1583-2026, https://doi.org/10.5194/wes-11-1583-2026, 2026
Short summary
Short summary
This study presents continuous lifetime assessments of wind turbine structural (tower) and rotating components (main bearings) over nearly a decade, combining controller data, calibrated load measurements, and a virtual load sensor at the tower bottom. The components' estimated lifetimes exceeded the design lifetime. Contrary to expectations, lower turbulence intensity at rated wind speed increased fatigue loads of the locating main bearing due to turbulence averaging.
Jens Peter Schøler, Frederik Peder Weilmann Rasmussen, M. Paul van der Laan, Alfredo Peña, and Pierre-Elouan Réthoré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-54, https://doi.org/10.5194/wes-2026-54, 2026
Revised manuscript under review for WES
Short summary
Short summary
As offshore wind farms are built closer together, predicting how they affect each other becomes critical. We compared two AI approaches for this task, training both on cheap approximate data before refining them with expensive high-accuracy simulations. One predicts wake boundaries better, while the other estimates wind speeds more accurately, offering complementary tools for future wind farm design.
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Revised manuscript under review for WES
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
Andreas Vad, Adrien Guilloré, Abhinav Anand, Vasilis Pettas, Anik H. Shah, Ion Lizarraga-Saenz, Maria Aparicio-Sanchez, Irene Eguinoa, Nicolau Conti Gost, Iasonas Tsaklis, Ariane Frère, Koen W. Hermans, Joseph K. Kamau, Nikolay Dimitrov, Tuhfe Göçmen, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-45, https://doi.org/10.5194/wes-2026-45, 2026
Preprint under review for WES
Short summary
Short summary
A modular, computationally efficient framework for wind‑farm response modeling is presented. It combines an engineering wake model with surrogate models trained on extensive aeroelastic simulations generated using a novel method for synthetic waked and clean inflows. The wind‑farm‑agnostic framework supports multiple turbine types and layouts, enabling accurate, low‑cost predictions for design, operation, and control.
Moritz Johann Gräfe, Azélice Ludot, Matt Shields, Athanasios Kolios, Rajasekhar Pulikollu, and Nikolay Dimitrov
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-31, https://doi.org/10.5194/wes-2026-31, 2026
Revised manuscript under review for WES
Short summary
Short summary
This study examines the risk drivers of the economic success of wind energy projects and how interacting risks threaten that success. We combined expert opinions, survey results, and literature findings to identify key challenges in planning and operation. The results show that risks interact and can cause uneven financial losses, highlighting the need for integrated decision support tools.
Sima Hamzeloo, Xiaoli Guo Larsén, Alfredo Peña, Jana Fischereit, and Oscar García-Santiago
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-267, https://doi.org/10.5194/wes-2025-267, 2026
Revised manuscript under review for WES
Short summary
Short summary
We studied how winds and ocean waves affect each other during a North Sea storm. Using a multiscale approach that captures processes from kilometers down to meters, we linked wind and wave models and compared the results with real measurements. Our aim was to improve current simulation methods, and the findings show that this detailed approach provides more accurate storm predictions up to 100 m height.
Büsra Yildirim, Nikolay Dimitrov, Athanasios Kolios, and Asger Bech Abrahamsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-115, https://doi.org/10.5194/wes-2025-115, 2025
Revised manuscript not accepted
Short summary
Short summary
A surrogate-based design optimization framework has been implemented for a floating wind turbine. By integrating surrogate modeling and analytical design constraints, computationally efficient exploration of design spaces is ensured. This integration provides a connection between conceptual and detailed design. The proposed methodology achieved a reduction of 3.7 % in the Levelized Cost of Energy, considering ultimate, fatigue, and serviceability limit states.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci., 10, 733–754, https://doi.org/10.5194/wes-10-733-2025, https://doi.org/10.5194/wes-10-733-2025, 2025
Short summary
Short summary
This study analyses wind speed data at heights up to 500 m to support the design of future large offshore wind turbines and airborne wind energy systems. We compared three wind models (ERA5, NORA3, and NEWA) with lidar measurements at five sites using four performance metrics. ERA5 and NORA3 performed equally well offshore, with NORA3 typically outperforming the other two models onshore. More generally, the optimal choice of model depends on site, altitude, and evaluation criteria.
Alfredo Peña, Ginka G. Yankova, and Vasiliki Mallini
Wind Energ. Sci., 10, 83–102, https://doi.org/10.5194/wes-10-83-2025, https://doi.org/10.5194/wes-10-83-2025, 2025
Short summary
Short summary
Lidars are vastly used in wind energy, but most users struggle when interpreting lidar turbulence measures. Here, we explain the difficulty in converting them into standard measurements. We show two ways of converting lidar to in situ turbulence measurements, both using neural networks: one of them is based on physics, while the other is purely data-driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Wei Fu, Feng Guo, David Schlipf, and Alfredo Peña
Wind Energ. Sci., 8, 1893–1907, https://doi.org/10.5194/wes-8-1893-2023, https://doi.org/10.5194/wes-8-1893-2023, 2023
Short summary
Short summary
A high-quality preview of the rotor-effective wind speed is a key element of the benefits of feedforward pitch control. We model a one-beam lidar in the spinner of a 15 MW wind turbine. The lidar rotates with the wind turbine and scans the inflow in a circular pattern, mimicking a multiple-beam lidar at a lower cost. We found that a spinner-based one-beam lidar provides many more control benefits than the one on the nacelle, which is similar to a four-beam nacelle lidar for feedforward control.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Short summary
Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Kenneth A. Brown and Thomas G. Herges
Atmos. Meas. Tech., 15, 7211–7234, https://doi.org/10.5194/amt-15-7211-2022, https://doi.org/10.5194/amt-15-7211-2022, 2022
Short summary
Short summary
The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
Short summary
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022, https://doi.org/10.5194/wes-7-875-2022, 2022
Short summary
Short summary
The power performance of a wind turbine is often tested with the turbine standing in a row of several wind turbines, as it is assumed that the performance is not affected by the neighbouring turbines. We test this assumption with both simulations and measurements, and we show that the power performance can be either enhanced or lowered by the neighbouring wind turbines. Consequently, we also show how power performance testing might be biased when performed on a row of several wind turbines.
Wei Fu, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 7, 831–848, https://doi.org/10.5194/wes-7-831-2022, https://doi.org/10.5194/wes-7-831-2022, 2022
Short summary
Short summary
Measuring the variability of the wind is essential to operate the wind turbines safely. Lidars of different configurations have been placed on the turbines’ nacelle to measure the inflow remotely. This work found that the multiple-beam lidar is the only one out of the three employed nacelle lidars that can give detailed information about the inflow variability. The other two commercial lidars, which have two and four beams, respectively, measure only the fluctuation in the along-wind direction.
Cited articles
International Standard IEC61400-13: Wind turbines – Part 13: Measurement of
mechanical loads, Standard, International Electrotechnical Commission (IEC), 2015. a
International Standard IEC61400-12-1: Wind energy generation systems – Part
12-1: Power performance measurements of electricity producing wind turbines,
Standard, International Electrotechnical Commission (IEC), 2017. a
Ainslie, J. F.: Calculating the flowfield in the wke of wind turbines, J. Wind. Eng. Ind. Aerod., 27, 213–224, 1987. a
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.:
Quantifying wind turbine wake characteristics from scanning remote sensor
data, J. Atmos. Ocean. Technol., 31, 765–787,
https://doi.org/10.1175/JTECH-D-13-00104.1, 2014. a
Angelou, N. and Sjöholm, M.: UniTTe WP3/MC1: Measuring the inflow towards a
Nordtank 500 kW turbine using three short-range WindScanners and one
SpinnerLidar, DTU Wind Energy, Roskilde, Denmark, 2015. a
Berg, J., Bryant, J., LeBlanc, B., Maniaci, D. C., Naughton, B., Paquette,
J. A., Resor, B. R., White, J., and Kroeker, D.: Scaled Wind Farm Technology
Facility Overview, 32nd Asme Wind Energy Symposium, 13–17 January 2014,
National Harbor, Maryland,
https://doi.org/10.2514/6.2014-1088, 2014. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging
measurements of wake dynamics Part I: One-dimensional Scanning, Wind Energy,
13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Box, G. and Tiao, G.: Bayesian inference in statistical analysis, Addison
Wesley, Voorburg, the Netherlands, 1973. a
Braunbehrens, R. and Segalini, A.: A statistical model for wake meandering
behind wind turbines,
J. Wind. Eng. Ind. Aerod., 193, 103954, https://doi.org/10.1016/j.jweia.2019.103954, 2019. a
Chamorro, L. P. and Porte-Agel, F.: A Wind-Tunnel Investigation of Wind-Turbine
Wakes: Boundary-Layer Turbulence Effects, Bound.-Lay. Meteorol., 132,
129–149, https://doi.org/10.1007/s10546-009-9380-8, 2009. a
Churchfield, M. J., Moriarty, P. J., Hao, Y., Lackner, M. A., Barthelmie, R.,
Lundquist, J. K., and Oxley, G. S.: A comparison of the dynamic wake
meandering model, large-eddy simulation, and field data at the egmond aan Zee
offshore wind plant, 33rd Wind Energy Symposium, 5–9 January 2015,
Kissimmee, Florida, 20 pp., 2015. a, b
Conti, D., Dimitrov, N., and Peña, A.: Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements, Wind Energ. Sci., 5, 1129–1154, https://doi.org/10.5194/wes-5-1129-2020, 2020b. a, b
Conti, D., Pettas, V., Dimitrov, N., and Peña, A.: Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals, Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, 2021. a, b, c
Debnath, M., Doubrawa, P., Herges, T., Martínez-Tossas, L. A., Maniaci, D. C.,
and Moriarty, P.: Evaluation of Wind Speed Retrieval from Continuous-Wave
Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques,
J. Phys. Conf. Ser., 1256, 012008,
https://doi.org/10.1088/1742-6596/1256/1/012008, 2019. a, b
Dimitrov, N. K.: Surrogate models for parameterized representation of
wake-induced loads in wind farms, Wind Energy, 22, 1371–1389,
https://doi.org/10.1002/we.2362, 2019. a, b, c
Doubrawa, P., Debnath, M., Moriarty, P. J., Branlard, E., Herges, T. G.,
Maniaci, D. C., and Naughton, B.: Benchmarks for Model Validation based on
LiDAR Wake Measurements, J. Phys. Conf. Ser., 1256,
012024, https://doi.org/10.1088/1742-6596/1256/1/012024, 2019. a, b, c
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M.,
Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., Hsieh, A. S.,
Blaylock, M. L., van der Laan, P., Andersen, S. J., Krueger, S., Cathelain,
M., Schlez, W., Jonkman, J., Branlard, E., Steinfeld, G., Schmidt, S.,
Blondel, F., Lukassen, L. J., and Moriarty, P.: Multimodel validation of
single wakes in neutral and stratified atmospheric conditions, Wind Energy,
23, 2027–2055, https://doi.org/10.1002/we.2543, 2020. a, b, c, d, e
Espana, G., Aubrun, S., Loyer, S., and Devinant, P.: Spatial study of the wake
meandering using modelled wind turbines in a wind tunnel, Wind Energy, 14,
923–937, https://doi.org/10.1002/we.515, 2011. a
Frandsen, S. T.: Turbulence and turbulence-generated structural loading in wind
turbine clusters, Risø National Laboratory, Roskilde, Denmark, 2007. a
Fuertes, F. C., Markfort, C. D., and Porteacute-Agel, F.: Wind Turbine Wake
Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model
Validation, Remote Sens., 10, 668, https://doi.org/10.3390/rs10050668, 2018. a, b
Galinos, C., Dimitrov, N. K., Larsen, T. J., Natarajan, A., and Hansen, K. S.:
Mapping Wind Farm Loads and Power Production – A Case Study on Horns Rev 1,
J. Phys. Conf. Ser., 753, 032010,
https://doi.org/10.1088/1742-6596/753/3/032010, 2016. a, b
Heisel, M., Hong, J., and Guala, M.: The spectral signature of wind turbine
wake meandering: A wind tunnel and field-scale study, Wind Energy, 21,
715–731, https://doi.org/10.1002/we.2189, 2018. a
Herges, T. G. and Keyantuo, P.: Robust Lidar Data Processing and Quality
Control Methods Developed for the SWiFT Wake Steering Experiment, J. Phys. Conf. Ser., 1256, 012005,
https://doi.org/10.1088/1742-6596/1256/1/012005, 2019. a, b, c
Herges, T. G., Berg, J. C., Bryant, J. T., White, J. R., Paquette, J. A., and
Naughton, B. T.: Detailed analysis of a waked turbine using a high-resolution
scanning lidar, J. Phys. Conf. Ser., 1037, 072009,
https://doi.org/10.1088/1742-6596/1037/7/072009, 2018. a, b, c, d
Hoffman, M. D. and Gelman, A.: The no-U-turn sampler: Adaptively setting path
lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15,
1593–1623, 2014. a
Högström, U.: Non-dimensional wind and temperature profiles in the
atmospheric surface-layer – A re-evaluation, Bound.-Lay. Meteorol., 42,
55–78, https://doi.org/10.1007/bf00119875, 1988. a
Iungo, G. V. and Porté-Agel, F.: Volumetric lidar scanning of wind turbine
wakes under convective and neutral atmospheric stability regimes, J. Atmos. Ocean. Technol., 31, 2035–2048,
https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a
Iungo, G. V., Wu, Y. T., and Porté-Agel, F.: Field measurements of wind
turbine wakes with lidars, J. Atmos. Ocean. Technol., 30,
274–287, https://doi.org/10.1175/JTECH-D-12-00051.1, 2013. a, b
Ivanell, S. S. A., Henningson, D. P., and Joachim, P. P.: Numerical
computations of wind turbine wakes, KTH, Stockholm, 2009. a
Keck, R.-E., de Mare, M. T., Churchfield, M. J., Lee, S., Larsen, G. C., and
Aagaard Madsen, H.: On atmospheric stability in the dynamic wake meandering
model, Wind Energy, 17, 1689–1710, https://doi.org/10.1002/we.1662,
2014a. a, b, c
Keck, R.-E., Mikkelsen, R. F., Troldborg, N., de Maré, M., and Hansen, K. S.:
Synthetic atmospheric turbulence and wind shear in large eddy simulations of
wind turbine wakes, Wind Energy, 17, 1247–1267, https://doi.org/10.1002/we.1631,
2014b. a
Keck, R. E., De Maré, M., Churchfield, M. J., Lee, S., Larsen, G., and Madsen,
H. A.: Two improvements to the dynamic wake meandering model: Including the
effects of atmospheric shear on wake turbulence and incorporating turbulence
build-up in a row of wind turbines, Wind Energy, 18, 111–132,
https://doi.org/10.1002/we.1686, 2015. a, b, c, d, e, f
Käsler, Y., Rahm, S., Simmet, R., and Kühn, M.: Wake measurements of a
multi-MW wind turbine with coherent long-range pulsed doppler wind lidar,
J. Atmos. Ocean. Technol., 27, 1529–1532,
https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Larsen, G., Ott, S., Liew, J., van der Laan, M., Simon, E., R.Thorsen, G., and
Jacobs, P.: Yaw induced wake deflection – a full-scale validation study,
J. Phys. Conf. Ser., 1618, 062047,
https://doi.org/10.1088/1742-6596/1618/6/062047, 2020. a
Larsen, G. C., Madsen Aagaard, H., Bingöl, F., Mann, J., Ott, S., Sørensen,
J., Okulov, V., Troldborg, N., Nielsen, N. M., Thomsen, K., Larsen, T. J.,
and Mikkelsen, R.: Dynamic wake meandering modeling, DTU Wind Energy, Roskilde, Denmark, 2007. a
Larsen, G. C., Madsen Aagaard, H., Thomsen, K., and Larsen, T. J.: Wake
meandering: A pragmatic approach, Wind Energy, 11, 377–395,
https://doi.org/10.1002/we.267, 2008. a, b, c, d
Larsen, G. C., Machefaux, E., and Chougule, A.: Wake meandering under
non-neutral atmospheric stability conditions – Theory and facts, J. Phys. Conf. Ser., 625, 012036,
https://doi.org/10.1088/1742-6596/625/1/012036, 2015. a, b
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the user's manual, Risø National
Laboratory, Roskilde, Denmark, 2007. a
Liew, J., Raimund Pirrung, G., and Meseguer Urbán, A.: Effect of varying
fidelity turbine models on wake loss prediction, J. Phys. Conf. Ser., 1618, 062002, https://doi.org/10.1088/1742-6596/1618/6/062002,
2020. a
Machefaux, E., Larsen, G. C., Troldborg, N., Gaunaa, M., and Rettenmeier, A.:
Empirical modeling of single-wake advection and expansion using full-scale
pulsed lidar-based measurements, Wind Energy, 18, 2085–2103,
https://doi.org/10.1002/we.1805, 2015. a, b
Machefaux, E., Larsen, G. C., Koblitz, T., Troldborg, N., Kelly, M. C.,
Chougule, A., Hansen, K. S., and Rodrigo, J. S.: An experimental and
numerical study of the atmospheric stability impact on wind turbine wakes,
Wind Energy, 19, 1785–1805, https://doi.org/10.1002/we.1950, 2016. a, b, c, d
Madsen, H. A., Larsen, G. C., Larsen, T. J., Troldborg, N., and Mikkelsen,
R. F.: Calibration and Validation of the Dynamic Wake Meandering Model for
Implementation in an Aeroelastic Code, J. Sol. Energ.,
132, 041014, https://doi.org/10.1115/1.4002555, 2010. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994. a
Mann, J., Pena Diaz, A., Bingöl, F., Wagner, R., and Courtney, M.: Lidar
Scanning of Momentum Flux in and above the Atmospheric Surface Layer, J. Atmos. Ocean. Technol., 27, 959–976,
https://doi.org/10.1175/2010jtecha1389.1, 2010. a, b
Meyer Forsting, A. R., Troldborg, N., and Borraccino, A.: Modelling lidar
volume-averaging and its significance to wind turbine wake measurements:
Paper, J. Phys. Conf. Ser., 854, 012014,
https://doi.org/10.1088/1742-6596/854/1/012014, 2017. a
Monin, A. and Obukhov, A.: Osnovnye zakonomernosti turbulentnogo
peremeshivanija v prizemnom sloe atmosfery (Basic laws of turbulent mixing in
the atmosphere near the ground), Trudy Geofiz. Inst, 24, 163–187, 1954. a
Muller, Y. A., Aubrun, S., and Masson, C.: Determination of real-time
predictors of the wind turbine wake meandering, Exp. Fluids, 56,
1–11, https://doi.org/10.1007/s00348-015-1923-9, 2015. a
Peña, A.: Østerild: A natural laboratory for atmospheric turbulence, J. Renew. Sustain. Ener., 11, 063302, https://doi.org/10.1063/1.5121486,
2019. a
Peña, A., Mann, J., and Dimitrov, N.: Turbulence characterization from a forward-looking nacelle lidar, Wind Energ. Sci., 2, 133–152, https://doi.org/10.5194/wes-2-133-2017, 2017. a, b, c, d
Peña, A., Sjöholm, M., Mikkelsen, T. K., and Hasager, C. B.: Inflow
characterization using measurements from the SpinnerLidar: the ScanFlow
experiment, J. Phys. Conf. Ser., 1037, 052027,
https://doi.org/10.1088/1742-6596/1037/5/052027, 2018. a, b, c
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola,
S.: Variance based sensitivity analysis of model output. Design and estimator
for the total sensitivity index, Comput. Phys. Commun., 181,
259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010. a
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C.: Probabilistic programming in
Python using PyMC3, Peerj, 2016, e55,
https://doi.org/10.7717/peerj-cs.55, 2016. a
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models and
their Monte Carlo estimates, Math. Comput. Simulat., 55,
271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001.
a
Subramanian, B., Chokani, N., and Abhari, R.: Impact of atmospheric stability
on wind turbine wake evolution, J. Wind Eng. Ind. Aerod., 176, 174–182, https://doi.org/10.1016/j.jweia.2018.03.014, 2018. a
Thomsen, K., Madsen Aagaard, H., Larsen, G. C., and Larsen, T. J.: Comparison
of methods for load simulation for wind turbines operating in wake, J. Phys. Conf. Ser., 75, 012072,
https://doi.org/10.1088/1742-6596/75/1/012072, 2007. a
Trabucchi, D., Trujillo, J.-J., and Kuehn, M.: Nacelle-based Lidar Measurements
for the Calibration of a Wake Model at Different Offshore Operating
Conditions, 14th Deep Sea Offshore Wind R&d Conference, Eera Deepwind '2017,
137, 77–88, https://doi.org/10.1016/j.egypro.2017.10.335, 2017. a
Troldborg, N. and Meyer Forsting, A. R.: A simple model of the wind turbine
induction zone derived from numerical simulations, Wind Energy, 20,
2011–2020, https://doi.org/10.1002/we.2137, 2017. a, b
Vermeer, L. J., Sørensen, J. N., and Crespo, A.: Wind turbine wake
aerodynamics, Prog. Aerosp. Sci., 39, 467–510,
https://doi.org/10.1016/S0376-0421(03)00078-2, 2003. a, b
Vollmer, L., Steinfeld, G., Heinemann, D., and Kühn, M.: Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study, Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, 2016. a
Walker, K., Adams, N., Gribben, B., Gellatly, B., Nygaard, N. G., Henderson,
A., Marchante Jimémez, M., Schmidt, S. R., Rodriguez Ruiz, J., Paredes, D.,
Harrington, G., Connell, N., Peronne, O., Cordoba, M., Housley, P., Cussons,
R., Håkansson, M., Knauer, A., and Maguire, E.: An evaluation of the
predictive accuracy of wake effects models for offshore wind farms, Wind
Energy, 19, 979–996, https://doi.org/10.1002/we.1871, 2016. a
Yang, X. and Sotiropoulos, F.: Wake characteristics of a utility-scale wind
turbine under coherent inflow structures and different operating conditions,
Physical Review Fluids, 4, 024604, https://doi.org/10.1103/PhysRevFluids.4.024604, 2019. a
Zhan, L., Letizia, S., and Iungo, G. V.: Optimal tuning of engineering wake models through lidar measurements, Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, 2020. a
Zhan, L., Letizia, S., and Valerio Iungo, G.: LiDAR measurements for an onshore
wind farm: Wake variability for different incoming wind speeds and
atmospheric stability regimes, Wind Energy, 23, 501–527,
https://doi.org/10.1002/we.2430, 2020b. a, b, c
Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using...
Altmetrics
Final-revised paper
Preprint