Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-1849-2025
© Author(s) 2025. 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-10-1849-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2025
Research article |
| 08 Sep 2025
| 08 Sep 2025
The impact of far-reaching offshore cluster wakes on wind turbine fatigue loads
Arjun Anantharaman
CORRESPONDING AUTHOR
School of Mathematics and Science, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Jörge Schneemann
School of Mathematics and Science, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Frauke Theuer
School of Mathematics and Science, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Laurent Beaudet
Siemens Gamesa Renewable Energy, Rouen, France
Valentin Bernard
Siemens Gamesa Renewable Energy, Rouen, France
Paul Deglaire
Siemens Gamesa Renewable Energy, Rouen, France
Martin Kühn
School of Mathematics and Science, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
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Wind Energ. Sci., 11, 175–193, https://doi.org/10.5194/wes-11-175-2026, https://doi.org/10.5194/wes-11-175-2026, 2026
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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 it with independent laser measurements of the flow field.
Ricardo Amaral, Felix Houtin-Mongrolle, Dominic von Terzi, Kasper Laugesen, Paul Deglaire, and Axelle Viré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-264, https://doi.org/10.5194/wes-2025-264, 2025
Preprint under review for WES
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This work uses simulations to investigate floating wind turbines which have the potential to supply the world's electricity demand many times by 2040. In particular, the effect of the rotor motion was investigated on the wake by forcing the turbine to move under a variety of motions. The results highlight differences in the effect of these motions. While some led to a wake behavior that was close to that of a fixed-bottom turbine, other motions produced a remarkably different wake structure.
Manuel Alejandro Zúñiga Inestroza, Paul Hulsman, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 10, 2257–2278, https://doi.org/10.5194/wes-10-2257-2025, https://doi.org/10.5194/wes-10-2257-2025, 2025
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Wake effects cause power losses that degrade wind farm efficiency. This paper presents a wind tunnel investigation of dynamic induction control (DIC), a strategy to mitigate wake losses by improving turbine–flow interactions. WindScanner lidar measurements are used to explore the wake development of model turbines in response to DIC. Our results demonstrate consistent benefits and adaptability under realistic inflow conditions, highlighting DIC’s potential to increase wind farm power production.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech., 18, 4949–4968, https://doi.org/10.5194/amt-18-4949-2025, https://doi.org/10.5194/amt-18-4949-2025, 2025
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Due to the scarcity of offshore in situ observations, alternative data sources are essential for a reliable understanding of offshore winds. Therefore, this study delves into the world of satellite remote sensing (ASCAT) and numerical models (ERA5), exploring their capabilities and limitations in characterising offshore winds. This investigation evaluates these two datasets against measurements from a floating ship-based lidar, collected during a novel measurement campaign in the Baltic Sea.
Daniel Ribnitzky, Vlaho Petrovic, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-143, https://doi.org/10.5194/wes-2025-143, 2025
Revised manuscript accepted for WES
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Johannes Paulsen, Jörge Schneemann, Gerald Steinfeld, Frauke Theuer, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-118, https://doi.org/10.5194/wes-2025-118, 2025
Revised manuscript accepted for WES
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Daniel Ribnitzky, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 10, 1329–1349, https://doi.org/10.5194/wes-10-1329-2025, https://doi.org/10.5194/wes-10-1329-2025, 2025
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Ricardo Amaral, Felix Houtin-Mongrolle, Dominic von Terzi, Kasper Laugesen, Paul Deglaire, and Axelle Viré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-34, https://doi.org/10.5194/wes-2025-34, 2025
Manuscript not accepted for further review
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This work uses simulations to investigate floating offshore wind turbines which have the potential to supply the world's electricity demand many times by 2040. In particular, the effect of the rotor motion on the wake was investigated by forcing the turbine to move under different motions and the results highlight differences between motions. While some motions led to a wake behavior that was close to that of a fixed-bottom turbine, other motions produced a remarkably different wake structure.
Frauke Theuer, Janna Kristina Seifert, Jörge Schneemann, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-141, https://doi.org/10.5194/wes-2024-141, 2024
Preprint under review for WES
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Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn Floris van Dooren, and Martin Kühn
Wind Energ. Sci., 9, 1483–1505, https://doi.org/10.5194/wes-9-1483-2024, https://doi.org/10.5194/wes-9-1483-2024, 2024
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We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 9, 359–383, https://doi.org/10.5194/wes-9-359-2024, https://doi.org/10.5194/wes-9-359-2024, 2024
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Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
Wind Energ. Sci., 8, 1755–1770, https://doi.org/10.5194/wes-8-1755-2023, https://doi.org/10.5194/wes-8-1755-2023, 2023
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This study examines wind vane measurements of commercial wind turbines and their impact on yaw control. The authors discovered that rotor interference can cause an overestimation of wind vane measurements, leading to overcorrection of the yaw controller. A correction function that improves the yaw behaviour is presented and validated in free-field experiments on a commercial wind turbine. This work provides new insights into wind direction measurements and suggests ways to optimize yaw control.
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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Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112, https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
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This paper presents an approach to analytically estimate the wake deficit within the near-wake region by modifying the curled wake model. This is done by incorporating a new initial condition at the rotor using an azimuth-dependent Gaussian profile, an adjusted turbulence model in the near-wake region and the far-wake region and an iterative process to determine the velocity field, while considering the relation of the pressure gradient and accounting the conservation of mass.
Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, https://doi.org/10.5194/wes-8-747-2023, 2023
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The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available input data, it gives good results.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
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A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
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Frederik Berger, Lars Neuhaus, David Onnen, Michael Hölling, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 7, 1827–1846, https://doi.org/10.5194/wes-7-1827-2022, https://doi.org/10.5194/wes-7-1827-2022, 2022
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We proof the dynamic inflow effect due to gusts in wind tunnel experiments with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg, where we created coherent gusts with an active grid. The effect is isolated in loads and rotor flow by comparison of a quasi-steady and a dynamic case. The observed effect is not caught by common dynamic inflow engineering models. An improvement to the Øye dynamic inflow model is proposed, matching experiment and corresponding FVWM simulations.
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
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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.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
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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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
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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We present three methods that can determine the alignment of a lidar placed on the transition piece of an offshore wind turbine based on measurements with the instrument: a practical implementation of hard targeting for north alignment, a method called sea surface levelling to determine the levelling of the system from water surface measurements, and a model that can determine the dynamic levelling based on the operating status of the wind turbine.
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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Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
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.
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.
Cited articles
Barthelmie, R. J., Frandsen, S. T., Nielsen, M., Pryor, S., Rethore, P.-E., and Jørgensen, H. E.: Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm, Wind Energy, 10, 517–528, https://doi.org/10.1002/we.238, 2007. a
Cañadillas, B., Beckenbauer, M., Trujillo, J. J., Dörenkämper, M., Foreman, R., Neumann, T., and Lampert, A.: Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling, Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, 2022. a
Churchfield, M. J., Lee, S., Michalakes, J., and Moriarty, P. J.: A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics, J. Turbul., 13, N14, https://doi.org/10.1080/14685248.2012.668191, 2012. a
de N Santos, F., D’Antuono, P., Robbelein, K., Noppe, N., Weijtjens, W., and Devriendt, C.: Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks, Renew. Energ., 205, 461–474, 2023. a
de N Santos, F., Noppe, N., Weijtjens, W., and Devriendt, C.: Farm-wide interface fatigue loads estimation: A data-driven approach based on accelerometers, Wind Energy, 27, 321–340, 2024. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I.: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a
Göçmen, T. and Giebel, G.: Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms, Renew. Energ., 99, 524–532, https://doi.org/10.1016/j.renene.2016.07.038, 2016. a
Gonzalez, E., Stephen, B., Infield, D., and Melero, J. J.: Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study, Renew. Energ., 131, 841–853, https://doi.org/10.1016/J.RENENE.2018.07.068, 2019. a
Hasager, C. B., Vincent, P., Badger, J., Badger, M., Di Bella, A., Peña, A., Husson, R., and Volker, P. J.: Using satellite SAR to characterize the wind flow around offshore wind farms, Energies, 8, 5413–5439, https://doi.org/10.3390/en8065413, 2015. a
Holtslag, M., Bierbooms, W., and Van Bussel, G.: Wind turbine fatigue loads as a function of atmospheric conditions offshore, Wind Energy, 19, 1917–1932, https://doi.org/10.1002/we.1959, 2016. a
IEA: Net Zero by 2050, Tech. rep., IEA, https://www.iea.org/reports/net-zero-by-2050 (last access: 23 October 2023), 2021. a
Kang, S.-L. and Won, H.: Intra-farm wind speed variability observed by nacelle anemometers in a large inland wind farm, J. Wind Eng. Ind. Aerod., 147, 164–175, https://doi.org/10.1016/j.jweia.2015.10.010, 2015. a
Lin, Z. and Liu, X.: Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network, Energy, 201, 117693, https://doi.org/10.1016/j.energy.2020.117693, 2020. a
Mittelmeier, N., Allin, J., Blodau, T., Trabucchi, D., Steinfeld, G., Rott, A., and Kühn, M.: An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects, Wind Energ. Sci., 2, 477–490, https://doi.org/10.5194/wes-2-477-2017, 2017. a, b, c
Muller, E., Gremmo, S., Houtin-Mongrolle, F., Duboc, B., and Bénard, P.: Field-data-based validation of an aero-servo-elastic solver for high-fidelity large-eddy simulations of industrial wind turbines, Wind Energ. Sci., 9, 25–48, https://doi.org/10.5194/wes-9-25-2024, 2024. a
Neumann, T. and Emeis, S.: Long-range modifications of the wind field by offshore wind parks–results of the project WIPAFF, Meteorol. Z, 29, 355–376, https://doi.org/10.1127/metz/2020/1023, 2020. a
Noppe, N., Iliopoulos, A., Weijtjens, W., and Devriendt, C.: Full load estimation of an offshore wind turbine based on SCADA and accelerometer data, J. Phys. Conf. Ser., 753, 072025, https://doi.org/10.1088/1742-6596/753/7/072025, 2016. a
Nygaard, N. G. and Christian Newcombe, A.: Wake behind an offshore wind farm observed with dual-Doppler radars, J. Phys. Conf. Ser., 1037, 072008, https://doi.org/10.1088/1742-6596/1037/7/072008, 2018. a
Ouro, P., Ghobrial, M., Ali, K., and Stallard, T.: Numerical modelling of offshore wind-farm cluster wakes, Renewable and Sustainable Energy Reviews, 215, 115526, https://doi.org/10.1016/j.rser.2025.115526, 2025. a
Pérez-Campuzano, D. and Gallego-Castillo, C.: Modelling damage equivalent loads in wind turbines from general operational signals: Exploration of relevant input selection methods using aeroelastic simulations, Wind Energy, 21, 441–459, https://doi.org/10.1002/we.2171, 2018. a
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep., 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a, b
Platis, A., Hundhausen, M., Lampert, A., Emeis, S., and Bange, J.: The role of atmospheric stability and turbulence in offshore wind-farm wakes in the German bight, Bound.-Lay. Meteorol., 182, 441–469, https://doi.org/10.1007/s10546-021-00668-4, 2021. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and wind-farm flows: A review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. 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
Rubak, R. and Petersen, J. T.: Monopile as part of aeroelastic wind turbine simulation code, Proceedings of Copenhagen Offshore Wind, 20, 655, 2005. a
Sathe, A., Mann, J., Barlas, T., Bierbooms, W., and van Bussel, G.: Influence of atmospheric stability on wind turbine loads, Wind Energy, 16, 1013–1032, https://doi.org/10.1002/we.1528, 2013. a, b
Schulz-Stellenfleth, J., Emeis, S., Dörenkämper, M., Bange, J., Cañadillas, B., Neumann, T., Schneemann, J., Weber, I., zum Berge, K., Platis, A., Djath, B., Gottschall, J., Vollmer, L., Rausch, T., Barekzai, M., Hammel, J., Steinfeld, G., and Lampert, A.: Coastal impacts on offshore wind farms – A review focussing on the German Bight area, Meteorol. Z, 31, 289–315, https://doi.org/10.1127/metz/2022/1109, 2022. a
Seifert, J. K., Kraft, M., Kühn, M., and Lukassen, L. J.: Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data, Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, 2021. a
Shaler, K., Robertson, A. N., and Jonkman, J.: Sensitivity analysis of the effect of wind and wake characteristics on wind turbine loads in a small wind farm, Wind Energ. Sci., 8, 25–40, https://doi.org/10.5194/wes-8-25-2023, 2023. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D., and Huang, X.-Y.: A description of the advanced research WRF version 4, NCAR tech. note ncar/tn-556+ str, 145, 2019. a
St. Martin, C. M., Lundquist, J. K., Clifton, A., Poulos, G. S., and Schreck, S. J.: Atmospheric turbulence affects wind turbine nacelle transfer functions, Wind Energ. Sci., 2, 295–306, https://doi.org/10.5194/wes-2-295-2017, 2017. a
Vera-Tudela, L. and Kühn, M.: Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions, Renew. Energ., 107, 352–360, https://doi.org/10.1016/j.renene.2017.01.065, 2017. a, b
Wackerly, D. D., Mendenhall, W., and Scheaffer, R.L.: Mathematical statistics with applications, Thomson Brooks/Cole, 2008 a
Ziegler, L., Smolka, U., Cosack, N., and Muskulus, M.: Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?, Wind Energ. Sci., 2, 469–476, https://doi.org/10.5194/wes-2-469-2017, 2017. a
Short summary
The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are important to analyse for both existing and planned wind farms. We developed a new methodology to quantify how much the reductions in wind speed behind a farm can affect the loads on turbines which are tens of kilometres downstream. We find a 2.5 % increase in the turbine loads and discuss how further measurements could add to the design standards of future wind farms.
The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are...
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