Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1491-2021
© 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-6-1491-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On turbulence models and lidar measurements for wind turbine control
Department of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000, Roskilde, Denmark
Wai Hou Lio
Department of Wind Energy, Technical University of Denmark (DTU), Frederiksborgvej 399, 4000, Roskilde, Denmark
Eric Simley
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, Colorado 80401, USA
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Kenneth Brown, Gopal Yalla, Lawrence Cheung, Joeri Frederik, Dan Houck, Nathaniel deVelder, Eric Simley, and Paul Fleming
Wind Energ. Sci., 10, 1737–1762, https://doi.org/10.5194/wes-10-1737-2025, https://doi.org/10.5194/wes-10-1737-2025, 2025
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This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
Joeri A. Frederik, Eric Simley, Kenneth A. Brown, Gopal R. Yalla, Lawrence C. Cheung, and Paul A. Fleming
Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, https://doi.org/10.5194/wes-10-755-2025, 2025
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In this paper, we present results from advanced computer simulations to determine the effects of applying different control strategies to a small wind farm. We show that when there is variability in wind direction over height, steering the wake of a turbine away from other turbines is the most effective strategy. When this variability is not present, actively changing the pitch angle of the blades to increase turbulence in the wake could be more effective.
David Onnen, Gunner Christian Larsen, Alan Wai Hou Lio, Paul Hulsman, Martin Kühn, and Vlaho Petrović
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-188, https://doi.org/10.5194/wes-2024-188, 2025
Revised manuscript under review for WES
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Neighbouring wind turbines influence each other, as they leave a complex footprint of reduced wind speed and changed turbulence in the flow, called wake. Modern wind farm control sees the turbines as team players and aims to mitigate the negative effects of such interaction. To do so, the exact flow situation in the wind farm must be known. We show, how to use wind turbines as sensors for waked inflow, test this in the field and compare with independent laser measurements of the flow field.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
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Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
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Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
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We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
Paul Fleming, Michael Sinner, Tom Young, Marine Lannic, Jennifer King, Eric Simley, and Bart Doekemeijer
Wind Energ. Sci., 6, 1521–1531, https://doi.org/10.5194/wes-6-1521-2021, https://doi.org/10.5194/wes-6-1521-2021, 2021
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The paper presents a new validation campaign of wake steering at a commercial wind farm. The campaign uses fixed yaw offset positions, rather than a table of optimal yaw offsets dependent on wind direction, to enable comparison with engineering models of wake steering. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions.
Eric Simley, Paul Fleming, Nicolas Girard, Lucas Alloin, Emma Godefroy, and Thomas Duc
Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, https://doi.org/10.5194/wes-6-1427-2021, 2021
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Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their low-velocity wakes away from downstream turbines, increasing overall power production. Here, we present results from a two-turbine wake-steering experiment at a commercial wind plant. By analyzing the wind speed dependence of wake steering, we find that the energy gained tends to increase for higher wind speeds because of both the wind conditions and turbine operation.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
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Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
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This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio
Wind Energ. Sci., 6, 111–129, https://doi.org/10.5194/wes-6-111-2021, https://doi.org/10.5194/wes-6-111-2021, 2021
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Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Jason Roadman, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci., 5, 1253–1272, https://doi.org/10.5194/wes-5-1253-2020, https://doi.org/10.5194/wes-5-1253-2020, 2020
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A wind turbine can actively influence its wake by turning the rotor out of the wind direction to deflect the wake away from a downstream wind turbine. This technique was tested in a field experiment at a wind farm, where the inflow and wake were monitored with remote-sensing instruments for the wind speed. The behaviour of the wake deflection agrees with the predictions of two analytical models, and a bias of the wind direction perceived by the yawed wind turbine led to suboptimal power gains.
Cited articles
Bak, C., Zahle, F., Bitsche, R., Yde, A., Henriksen, L. C., Natarajan, A., and Hansen, M. H.: Description of the DTU 10 MW Reference Wind Turbine, Tech. rep., DTU Wind Energy Report-I-0092, DTU Wind Energy, Roskilde, Denmark, 2013. a
Bossanyi, E., Kumar, A., and Hugues-Salas, O.: Wind turbine control
applications of turbine-mounted LIDAR, J. Phys.: Conf. Ser., 555, 012011, https://doi.org/10.1088/1742-6596/555/1/012011, 2014. a
Chougule, A., Mann, J., Kelly, M., Sun, J., Lenschow, D. H., and Patton, E. G.: Vertical cross-spectral phases in neutral atmospheric flow, J. Turbulence, 13, N36, https://doi.org/10.1080/14685248.2012.711524, 2012. a
Eliassen, L. and Obhrai, C.: Coherence of Turbulent Wind under Neutral Wind
Conditions at FINO1, in: Energy Procedia, vol. 94, Elsevier Ltd, Trondheim, Norway, 388–398, https://doi.org/10.1016/j.egypro.2016.09.199, 2016. a
Fleming, P. A., Scholbrock, A., Jehu, A., Davoust, S., Osler, E., Wright,
A. D., and Clifton, A.: Field-test results using a nacelle-mounted lidar for
improving wind turbine power capture by reducing yaw misalignment, J. Phys.: Conf. Ser., 524, 012002, https://doi.org/10.1088/1742-6596/524/1/012002, 2014. a
Haizmann, F., Schlipf, D., and Cheng, P. W.: Correlation-model of rotor-effective wind shears and wind speed for lidar-based individual pitch
control, Proceedings of the Twelfth (2015) German Wind Energy Conference DEWEK, Bremen, Germany, https://doi.org/10.18419/opus-3976, 19–20 May 2015. a, b
Hansen, M. H., Henriksen, L. C., Tibaldi, C., Bergami, L., Verelst, D.,
Pirrung, G., and Riva, R.: HAWCStab2: User Manual, Tech. Rep. October, DTU
Wind Energy, available at: https://www.hawcstab2.vindenergi.dtu.dk/ (last access: 24 November 2021), 2018. a
Jonkman, B. J. and Buhl Jr., M. L.: TurbSim user's guide, Tech. rep., Technical Report NREL/TP-500-39797, National Renewable Energy Laboratory, Golden, Colorado, USA, 2006. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5 MW Reference Wind Turbine, Tech. rep., Technical Report NREL/TP-500-38050,
National Renewable Energy Laboratory, Golden, Colorado, USA, 2009. a
Kumar, A. A., Bossanyi, E. A., Scholbrock, A. K., Fleming, P., Boquet, M., and Krishnamurthy, R.: Field Testing of LIDAR-Assisted Feedforward Control
Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600-kW
Wind Turbine, Tech. rep., NREL – National Renewable Energy Lab., Golden, CO, USA, 2015. a
Mann, J.: The Spatial Structure of Neutral Atmospheric Surface-Layer Turbulence, J. Fluid Mech., 273, 141–168, https://doi.org/10.1017/S0022112094001886, 1994. a
Mann, J.: Wind field simulation, Probabil. Eng. Mech., 13, 269–282, https://doi.org/10.1016/s0266-8920(97)00036-2, 1998. a
Nybø, A., Nielsen, F. G., Reuder, J., Churchfield, M. J., and Godvik, M.:
Evaluation of different wind fields for the investigation of the dynamic
response of offshore wind turbines, Wind Energy, 23, 1810–1830,
https://doi.org/10.1002/we.2518, 2020. a, b
Pena, A., Eds, C. B. H., Bischoff, O., Frandsen, S. T., Mann, J., and Trujillo, J. J.: Remote Sensing for Wind Energy, Tech. Rep. May, DTU Wind Energy, Roskilde, Denmark. No. 0084, available at: https://orbit.dtu.dk/en/publications/remote-sensing-for-wind-energy-4 (last access: 24 November 2021), 2015. a
Schlipf, D., Schuler, S., Grau, P., Allgöwer, F., and Kühn, M.:
Look-ahead cyclic pitch control using lidar, in: Proceedings of the Science of Making Torque from Wind, Heraklion, Greece, 28–30 June 2010, https://doi.org/10.18419/opus-4538, 2010. a
Schlipf, D., Kapp, S., Anger, J., Bischoff, O., Hofsäß, M., Rettenmeier, A., and Kuhn, M.: Prospects of Optimization of Energy Production by LIDAR Assisted Control of Wind Turbines, in: EWEA 2011 conference
proceedings, 1–10, Brussels, Belgium, available at:
http://elib.uni-stuttgart.de/opus/volltexte/2013/8585/ (last access: 24 November 2021), 2011. a, b
Schlipf, D., Cheng, P. W., and Mann, J.: Model of the Correlation between Lidar Systems and Wind Turbines for Lidar-Assisted Control, J. Atmos. Ocean. Tech., 30, 2233–2240, https://doi.org/10.1175/JTECH-D-13-00077.1, 2013a. a, b, c
Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive
control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129, https://doi.org/10.1002/we.1533, 2013b. a, b
Schlipf, D., Fleming, P., Haizmann, F., Scholbrock, A., Hofsäß, M.,
Wright, A., and Cheng, P. W.: Field testing of feedforward collective pitch
control on the CART2 using a nacelle-based lidar scanner, J. Phys.: Conf. Ser., 555, 012090, https://doi.org/10.1088/1742-6596/555/1/012090, 2014. a
Schlipf, D., Fürst, H., Raach, S., and Haizmann, F.: Systems Engineering
for Lidar-Assisted Control: A Sequential Approach, J. Phys.: Conf. Ser., 1102, 012014, https://doi.org/10.1088/1742-6596/1102/1/012014, 2018. a, b, c
Scholbrock, A., Fleming, P., Schlipf, D., Wright, A., Johnson, K., and Wang,
N.: Lidar-enhanced wind turbine control: Past, present, and future, in: IEEE 2016 American Control Conference (ACC), 6–8 July, Boston, MA, USA, 1399–1406, 2016. a
Simley, E. and Pao, L. Y.: A longitudinal spatial coherence model for wind
evolution based on large-eddy simulation, in: IEEE 2015 American Control
Conference (ACC), 1–3 July, Chicago, IL, USA, 3708–3714, 2015. a
Simley, E., Pao, L., Frehlich, R., Jonkman, B., and Kelley, N.: Analysis of
wind speed measurements using continuous wave LIDAR for wind turbine control,
in: 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and
Aerospace Exposition, 4–7 January 2011, Orlando, Florida, USA, p. 263, 2011. a
Simley, E., Pao, L. Y., Frehlich, R., Jonkman, B., and Kelley, N.: Analysis of light detection and ranging wind speed measurements for wind turbine control, Wind Energy, 17, 413–433, https://doi.org/10.1002/we.1584, 2014. a, b
Simley, E., Bortolotti, P., Scholbrock, A., Schlipf, D., and Dykes, K.: IEA
Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering, J. Phys.: Conf. Ser., 1618, 042029, https://doi.org/10.1088/1742-6596/1618/4/042029, 2020. a
Veers, P. S.: Three-dimensional wind simulation, Tech. rep., Sandia National
Labs., Albuquerque, NM, USA, 1988. a
Short summary
This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
This paper suggests that the impacts of different turbulence models should be considered as...
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