Articles | Volume 8, issue 11
https://doi.org/10.5194/wes-8-1693-2023
© Author(s) 2023. 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-8-1693-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Increased power gains from wake steering control using preview wind direction information
Balthazar Arnoldus Maria Sengers
CORRESPONDING AUTHOR
ForWind, Carl von Ossietzky Universität Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany
current address: Fraunhofer IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Andreas Rott
ForWind, Carl von Ossietzky Universität Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany
Eric Simley
National Wind Technology Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
Michael Sinner
National Wind Technology Center, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
Gerald Steinfeld
ForWind, Carl von Ossietzky Universität Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany
Martin Kühn
ForWind, Carl von Ossietzky Universität Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany
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Frederik Berger, Lars Neuhaus, David Onnen, Michael Hölling, Gerard Schepers, and Martin Kühn
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Balthazar Arnoldus Maria Sengers, Matthias Zech, Pim Jacobs, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, https://doi.org/10.5194/wes-7-1455-2022, 2022
Short summary
Short summary
Wake steering aims to redirect the wake away from a downstream turbine. This study explores the potential of a data-driven surrogate model whose equations can be interpreted physically. It estimates wake characteristics from measurable input variables by utilizing a simple linear model. The model shows encouraging results in estimating available power in the far wake, with significant improvements over currently used analytical models in conditions where wake steering is deemed most effective.
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Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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Sonja Krüger, Gerald Steinfeld, Martin Kraft, and Laura J. Lukassen
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Andreas Rott, Jörge Schneemann, Frauke Theuer, Juan José Trujillo Quintero, and Martin Kühn
Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, https://doi.org/10.5194/wes-7-283-2022, 2022
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Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
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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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Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
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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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Frederik Berger, David Onnen, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 6, 1341–1361, https://doi.org/10.5194/wes-6-1341-2021, https://doi.org/10.5194/wes-6-1341-2021, 2021
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Dynamic inflow denotes the unsteady aerodynamic response to fast changes in rotor loading and leads to load overshoots. We performed a pitch step experiment with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg. We measured axial and tangential inductions with a recent method with a 2D-LDA system and performed load and wake measurements. These radius-resolved measurements allow for new insights into the dynamic inflow phenomenon.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
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Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation depends on their location within the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
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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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.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
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A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Anantha Padmanabhan Kidambi Sekar, Marijn Floris van Dooren, Andreas Rott, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-16, https://doi.org/10.5194/wes-2021-16, 2021
Preprint withdrawn
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Turbine-mounted lidars performing inflow scans can be used to optimise wind turbine performance and extend their lifetime. This paper introduces a new method to extract wind inflow information from a turbine-mounted scanning SpinnerLidar based on Proper Orthogonal Decomposition. This method offers a balance between simple reconstruction methods and complicated physics-based solvers. The results show that the model can be used for lidar assisted control, loads validation and turbulence studies.
Frauke Theuer, Marijn Floris van Dooren, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 5, 1449–1468, https://doi.org/10.5194/wes-5-1449-2020, https://doi.org/10.5194/wes-5-1449-2020, 2020
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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
Bird, L., Lew, D., Milligan, M., Carlini, E. M., Estanqueiro, A., Flynn, D., Gomez-Lazaro, E., Holttinen, H., Menemenlis, N., Orths, A., Eriksen, P. B., Smith, J. C., Soder, L., Sorensen, P., Altiparmakis, A., Yasuda, Y., and Miller, J.: Wind and solar energy curtailment: A review of international experience, Renew. Sust. Energ. Rev., 65, 577–586, https://doi.org/10.1016/j.rser.2016.06.082, 2016. a
Bossanyi, E.: Optimising yaw control at wind farm level, J. Phys. Conf. Ser., 1222, 012023, https://doi.org/10.1088/1742-6596/1222/1/012023, 2019. a, b
Bossanyi, E. A., 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
Doekemeijer, B. M., Boersma, S., Pao, L. Y., Knudsen, T., and van Wingerden, J.-W.: Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control, Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, 2018. a
Doekemeijer, B. M., Kern, S., Maturu, S., Kanev, S., Salbert, B., Schreiber, J., Campagnolo, F., Bottasso, C. L., Schuler, S., Wilts, F., Neumann, T., Potenza, G., Calabretta, F., Fioretti, F., and van Wingerden, J.-W.: Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy, Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, 2021. a
Dörenkämper, M., Witha, B., Steinfeld, G., Heinemann, D., and Kühn, M.: The impact of stable atmospheric boundary layers on wind-turbine wakes within offshore wind farms, J. Wind Eng. Ind. Aerod., 144, 146–153, https://doi.org/10.1016/j.jweia.2014.12.011, 2015. a
Dunne, F., Pao, L. Y., Wright, A. D., Jonkman, B., and Kelley, N.: Adding feedforward blade pitch control to standard feedback controllers for load mitigation in wind turbines, Mechatronics, 21, 682–690, https://doi.org/10.1016/j.mechatronics.2011.02.011, 2011. a
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A., Murphy, P., Lundquist, J. K., Moriarty, P., Fleming, K., van Dam, J., Bay, C., Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and Brake, D.: Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, 2019. a
Fleming, P. A., Scholbrock, A. K., 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
Held, D. P. and Mann, J.: Detection of wakes in the inflow of turbines using nacelle lidars, Wind Energ. Sci., 4, 407–420, https://doi.org/10.5194/wes-4-407-2019, 2019. a
Houck, D.: Review of wake management techniques for wind turbines, Wind Energy, 25, 195–220, https://doi.org/10.1002/we.2668, 2021. a
Howland, M. F., González, C. M., Martínez, J. J. P., Quesada, J. B., Larrañaga, F. P., Yadav, N. K., Chawla, J. S., and Dabiri, J. O.: Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment, J. Renew. Sustain. Ener., 12, 063307, https://doi.org/10.1063/5.0023746, 2020. a
Howland, M. F., Ghate, A. S., Quesada, J. B., Pena Martínez, J. J., Zhong, W., Larrañaga, F. P., Lele, S. K., and Dabiri, J. O.: Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions, Wind Energ. Sci., 7, 345–365, https://doi.org/10.5194/wes-7-345-2022, 2022. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Tech. Rep. NREL/TP-500-38060, National Renewable Energy Laboratory, https://doi.org/10.2172/947422, 2009. a
Kanev, S.: Dynamic wake steering and its impact on wind farm power production and yaw actuator duty, Renew. Energ., 146, 9–15, https://doi.org/10.1016/j.renene.2019.06.122, 2020. a
Laks, J., Pao, L. Y., Wright, A., Kelley, N., and Jonkman, B.: Blade pitch control with preview wind measurements, in: 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlanda, Florida, USA, 4–7 January, https://doi.org/10.2514/6.2010-251, 2010. a
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. a
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
NREL: FLORIS. Version 3.2.1, GitHub [code], https://github.com/NREL/floris (last access: 28 November 2022), 2022. a
Rott, A., Doekemeijer, B., Seifert, J. K., van Wingerden, J.-W., and Kühn, M.: Robust active wake control in consideration of wind direction variability and uncertainty, Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, 2018. a
Rott, A., Petrović, V., and Kühn, M.: Wind farm flow reconstruction and prediction from high frequency SCADA Data, J. Phys. Conf. Ser., 1618, 062067, https://doi.org/10.1088/1742-6596/1618/6/062067, 2020. a, b
Schlipf, D. and Cheng, P. W.: Flatness-based feedforward control of wind turbines using lidar, IFAC P., 19, 5820–5825, https://doi.org/10.3182/20140824-6-ZA-1003.00443, 2014. a
Schlipf, D., Kapp, S., Anger, J., Bischoff, O., Hofsäß, M., Rettenmeier, A., and Kühn, M.: Prospects of Optimization of Energy Production by LIDAR Assisted Control of Wind Turbines, in: EWEA 2011, 14-17 March, Brussels, Belgium, https://doi.org/10.18419/opus-3916, 2011. a
Schlipf, D., Schlipf, D., and Kuhn, M.: Nonlinear model predictive control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129, https://doi.org/10.1002/we.1533, 2013. a
Scholbrock, A., Fleming, P., Wright, A., Slinger, C., Medley, J., and Harris, M.: Field test results from lidar measured yaw control for improved yaw alignment with the NREL controls advanced research turbine, in: 33rd Wind Energy Symposium, Kissimmee, Florida, USA, 5–9 January, https://doi.org/10.2514/6.2015-1209, 2015. a
Scholbrock, A., Fleming, P., Schlipf, D., Wright, A., Johnson, K., and Wang, N.: Lidar-Enhanced Wind Turbine Control: Past, Present, and Future, in: P. Amer. Contr. Conf., Boston, Massachusetts, USA, 6–8 July, 1399–1406, https://doi.org/10.1109/ACC.2016.7525113, 2016. a
Sengers, B. A. M.: Increased power gains from wake steering control using preview wind direction information, Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.8420331, 2023. a
Sengers, B. A. M., Zech, M., Jacobs, P., Steinfeld, G., and Kühn, M.: A physically interpretable data-driven surrogate model for wake steering, Wind Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, 2022. a, b, c, d
Sengers, B. A. M., Steinfeld, G., Hulsman, P., and Kühn, M.: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment, Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, 2023. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. a, b
Simley, E., Fleming, P., Girard, N., Alloin, L., Godefroy, E., and Duc, T.: Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, 2021a. a
Simley, E., Debnath, M., and Fleming, P.: Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant, J. Phys. Conf. Ser., 2265, 032097, https://doi.org/10.1088/1742-6596/2265/3/032097, 2022. a
Sinner, M., Pao, L. Y., and King, J.: Estimation of Large-Scale Wind Field Characteristics using Supervisory Control and Data Acquisition Measurements, in: P. Amer. Contr. Conf., Denver, Colorado, USA, 1–3 July, 2357–2362, https://doi.org/10.23919/ACC45564.2020.9147859, 2020. a
Sinner, M., Simley, E., King, J., Fleming, P., and Pao, L. Y.: Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings, J. Renew. Sustain. Ener., 13, 023310, https://doi.org/10.1063/5.0039899, 2021. a
Song, D. R., Li, Q. A., Cai, Z., Li, L., Yang, J., Su, M., and Joo, Y. H.: Model Predictive Control Using Multi-Step Prediction Model for Electrical Yaw System of Horizontal-Axis Wind Turbines, IEEE T. Sustain. Energ., 10, 2084–2093, https://doi.org/10.1109/TSTE.2018.2878624, 2019. a
Spencer, M., Stol, K., and Cater, J.: Predictive Yaw Control of a 5 MW Wind Turbine Model, in: 51th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Nashville, Tennessee, USA, 9–12 January, https://doi.org/10.2514/6.2012-1020, 2011. a, b
Stieren, A., Gadde, S. N., and Stevens, R. J.: Modeling dynamic wind direction changes in large eddy simulations of wind farms, Renew. Energ., 170, 1342–1352, https://doi.org/10.1016/j.renene.2021.02.018, 2021. a, b, c, d
Theuer, F., van Dooren, M. F., von Bremen, L., and Kühn, M.: Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements, Wind Energ. Sci., 5, 1449–1468, https://doi.org/10.5194/wes-5-1449-2020, 2020. a, b
Theuer, F., Rott, A., Schneemann, J., von Bremen, L., and Kühn, M.: Observer-based power forecast of individual and aggregated offshore wind turbines, Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, 2022. a
van Dooren, M. F., Trabucchi, D., and Kühn, M.: A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements, Remote Sensing, 8, 809, https://doi.org/10.3390/rs8100809, 2016. a
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
Wang, N., Johnson, K. E., and Wright, A. D.: Comparison of strategies for enhancing energy capture and reducing loads using LIDAR and feedforward control, IEEE T. Contr. Syst. T., 21, 1129–1142, https://doi.org/10.1109/TCST.2013.2258670, 2013. a
Wu, Y.-T. and Porté-Agel, F.: Large-Eddy Simulation of Wind-Turbine Wakes: Evaluation of Turbine Parametrisations, Bound.-Lay. Meteorol., 138, 345–366, https://doi.org/10.1007/s10546-010-9569-x, 2011. a
Würth, I., Valldecabres, L., Simon, E., Möhrlen, C., Uzunoğlu, B., Gilbert, C., Giebel, G., Schlipf, D., and Kaifel, A.: Minute-scale forecasting of wind power–results from the collaborative workshop of IEA Wind task 32 and 36, Energies, 12, 712, https://doi.org/10.3390/en12040712, 2019. a
Short summary
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.
Unexpected wind direction changes are undesirable, especially when performing wake steering....
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