Articles | Volume 9, issue 7
https://doi.org/10.5194/wes-9-1483-2024
© Author(s) 2024. 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-9-1483-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines
Anantha Padmanabhan Kidambi Sekar
CORRESPONDING AUTHOR
Carl von Ossietzky Universität Oldenburg, School V, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Paul Hulsman
Carl von Ossietzky Universität Oldenburg, School V, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Marijn Floris van Dooren
Carl von Ossietzky Universität Oldenburg, School V, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Martin Kühn
Carl von Ossietzky Universität Oldenburg, School V, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Related authors
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
Short summary
Short summary
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.
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Preprint withdrawn
Short summary
Short summary
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.
Arjun Anantharaman, Jörge Schneemann, Frauke Theuer, Laurent Beaudet, Valentin Bernard, Paul Deglaire, and Martin Kühn
Wind Energ. Sci., 10, 1849–1867, https://doi.org/10.5194/wes-10-1849-2025, https://doi.org/10.5194/wes-10-1849-2025, 2025
Short summary
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.
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
Preprint under review for WES
Short summary
Short summary
We developed controllers for the Hybrid-Lambda Rotor, which enables two operating modes below rated power via different tip speed ratios, balancing load reduction and power output. A baseline controller with a model-based wind speed estimator, a load feedback controller and an inflow feed-forward controller were implemented on the MoWiTO 1.8 model turbine and tested in wind tunnel experiments. In depth scaling considerations ensure the transferability of the results to the full-scale model.
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
Preprint under review for WES
Short summary
Short summary
While Low-Level Jets (LLJs) have been well-characterized, their impact on offshore wind farms is not well understood. This study uses multi-elevation lidar scans to derive vertical wind profiles up to 350 m and detect LLJs in up to 22.6 % of available measurements. Further, we analyze their effect on power production using operational wind farm data, observing a slightly negative influence and increased power fluctuations during LLJ events.
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
Short summary
Short summary
In this paper, the Hybrid-Lambda Rotor is scaled to wind tunnel size and validated in wind tunnel experiments. The objectives are to derive a scaling methodology, to investigate the influence of the steep gradients of axial induction along the blade span, and to characterize the near wake. The study reveals complex three-dimensional flow patterns for blade designs with non-uniform loading, and it can offer new inspirations when solving other scaling problems for complex wind turbine systems.
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
Short summary
Short summary
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.
Manuel Alejandro Zúñiga Inestroza, Paul Hulsman, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-171, https://doi.org/10.5194/wes-2024-171, 2025
Revised manuscript accepted for WES
Short summary
Short summary
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.
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
Short summary
Short summary
To be useful for end-users the forecast horizon of lidar-based minute-scale forecasts needs to be extended to at least 15 minutes. In this work, we adapt a lidar-based forecasting methodology to predict wind speed and power with horizons of up to 30 minutes. We found that the skill of the lidar-based approach highly depends on atmospheric conditions and the forecast characteristics. It was able to outperform persistence up to a 16 minute forecast horizon during unstable conditions.
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
Short summary
Short summary
This paper provides an innovative blade design methodology for offshore wind turbines with very large rotors compared to their rated power, which are tailored for an increased power feed-in at low wind speeds. Rather than designing the blade for a single optimized operational point, we include the application of peak shaving in the design process and introduce a design for two tip speed ratios. We describe how enlargement of the rotor diameter can be realized to improve the value of wind power.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-11, https://doi.org/10.5194/amt-2024-11, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Unlocking offshore wind farms’ potential demands a precise understanding of available wind resources. Yet, limited in situ data in marine environments call for innovative solutions. This study delves into the world of satellite remote sensing and numerical models, exploring their capabilities and challenges in characterizing offshore wind dynamics. This investigation evaluates these tools against measurements from a floating ship-based lidar, collected through a novel campaign in the Baltic Sea.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
Short summary
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
Short summary
Short summary
Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Very short-term wind power forecasts are gaining increasing importance with the rising share of renewables in today's energy system. In this work, we developed a methodology to forecast wind power of offshore wind turbines on minute scales utilising long-range single-Doppler lidar measurements. The model was able to outperform persistence during unstable stratification in terms of deterministic and probabilistic scores, while it showed large shortcomings for stable atmospheric conditions.
Cited articles
Abkar, M., Sørensen, J. N., and Porté-Agel, F.: An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes, Energies, 11, 1838, https://doi.org/10.3390/EN11071838, 2018. a
Angelou, N., Mann, J., Sjöholm, M., and Courtney, M.: Direct measurement of the spectral transfer function of a laser based anemometer, Rev. Sci. Instrum., 83, 033111, https://doi.org/10.1063/1.3697728, 2012. a, b
Angelou, N., Mann, J., and Dellwik, E.: Scanning Doppler lidar measurements of drag force on a solitary tree, J. Fluid Mech., 917, A30, https://doi.org/10.1017/jfm.2021.275, 2021. a
Asimakopoulos, M., Clive, P., More, G., and Boddington, R.: Offshore compression zone measurement and visualization, in: European Wind Energy Association 2014 Annual Event, Barcelona, Spain, 2014. a
Barthelmie, R. J.: The effects of atmospheric stability on coastal wind climates, Meteorol. Appl., 6, 39–47, https://doi.org/10.1017/S1350482799000961, 1999. a
Bastankhah, M. and Porte-Agel, F.: Wind tunnel study of the wind turbine interaction with a boundary-layer flow: Upwind region, turbine performance, and wake region, Phys. Fluids, 29, 065105, https://doi.org/10.1063/1.4984078, 2017. a
Beck, H. and Kühn, M.: Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements, Remote Sensing, 9, 561, https://doi.org/10.3390/rs9060561, 2017. a
Branlard, E.: Wiz, wake and induction zone model, GitHub [code], https://github.com/ebranlard/wiz (last access: 23 April 2022), 2019. a
Branlard, E. and Gaunaa, M.: Cylindrical vortex wake model: right cylinder, Wind Energy, 18, 1973–1987, https://doi.org/10.1002/WE.1800, 2015. a, b, c
Branlard, E. and Meyer Forsting, A. R.: Assessing the blockage effect of wind turbines and wind farms using an analytical vortex model, Wind Energy, 23, 2068–2086, https://doi.org/10.1002/we.2546, 2020. a, b
Bromm, M., Rott, A., Beck, H., Vollmer, L., Steinfeld, G., and Kühn, M.: Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes, Wind Energy, 21, 1011–1028, https://doi.org/10.1002/we.2210, 2018. a, b
Cheynet, E., Jakobsen, J. B., Snæbjörnsson, J., Mikkelsen, T., Sjöholm, M., Mann, J., Hansen, P., Angelou, N., and Svardal, B.: Application of short-range dual-Doppler lidars to evaluate the coherence of turbulence, Exp. Fluids, 57, 184, https://doi.org/10.1007/s00348-016-2275-9, 2016. a
Counihan, J., Hunt, J. C., and Jackson, P. S.: Wakes behind two-dimensional surface obstacles in turbulent boundary layers, J. Fluid Mech., 64, 529–564, https://doi.org/10.1017/S0022112074002539, 1974. 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
Dunne, F., Pao, L. Y., Schlipf, D., and Scholbrock, A. K.: Importance of lidar measurement timing accuracy for wind turbine control, P. Amer. Contr. Conf., 3716–3721, https://doi.org/10.1109/ACC.2014.6859337, 2014. a
Fleming, P., Annoni, J., Churchfield, M., Martinez-Tossas, L. A., Gruchalla, K., Lawson, M., and Moriarty, P.: A simulation study demonstrating the importance of large-scale trailing vortices in wake steering, Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, 2018. a
Giyanani, A., Sjöholm, M., Rolighed Thorsen, G., Schuhmacher, J., and Gottschall, J.: Wind speed reconstruction from three synchronized short-range WindScanner lidars in a large wind turbine inflow field campaign and the associated uncertainties, J. Phys. Conf. Ser., 2265, 022032, https://doi.org/10.1088/1742-6596/2265/2/022032, 2022. a, b, c
Göçmen, T., Laan, P. V. D., Réthoré, P. E., Diaz, A. P., Larsen, G. C., and Ott, S.: Wind turbine wake models developed at the technical university of Denmark: A review, Renew. Sust. Energ. Rev., 60, 752–769, https://doi.org/10.1016/J.RSER.2016.01.113, 2016. a
Hulsman, P., Sucameli, C., Petrović, V., Rott, A., Gerds, A., and Kühn, M.: Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions, J. Phys. Conf. Ser., 2265, 032074, https://doi.org/10.1088/1742-6596/2265/3/032074, 2022a. a, b, c
Hulsman, P., Wosnik, M., Petrović, V., Hölling, M., and Kühn, M.: Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow, Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, 2022b. a, b
International Electrotechnical Commission: Wind turbines, Part 12-1: Power performance measurements of electricity producing wind turbines,, International Electrotechnical Commission, 3 Edn. , 2005, 179, https://webstore.iec.ch/publication/68499 (last access: 8 July 2024), 2022. a
Jiménez, Ã., Crespo, A., and Migoya, E.: Application of a LES technique to characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13, 559–572, https://doi.org/10.1002/we.380, 2009. a
Jonkman, J. M. and Buhl Jr., M. L.: FAST user's guide, National Renewable Energy Laboratory, Golden, CO, Technical Report No. NREL/EL-500-38230, https://www.nrel.gov/docs/fy06osti/38230.pdf (last access: 5 July 2024), 2005. a
Kelley, C. L., Herges, T. G., Martinez, L. A., and Mikkelsen, T.: Wind turbine aerodynamic measurements using a scanning lidar, J. Phys. Conf. Ser., 1037, 052014, https://doi.org/10.1088/1742-6596/1037/5/052014, 2018. a
Krüger, S., Steinfeld, G., Kraft, M., and Lukassen, L. J.: Validation of a coupled atmospheric–aeroelastic model system for wind turbine power and load calculations, Wind Energ. Sci., 7, 323–344, https://doi.org/10.5194/wes-7-323-2022, 2022. a
Lund, T. S., Wu, X., and Squires, K. D.: Generation of Turbulent Inflow Data for Spatially-Developing Boundary Layer Simulations, J. Comput. Phys., 140, 233–258, https://doi.org/10.1006/jcph.1998.5882, 1998. a
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a
Madsen, H. A., Riziotis, V., Zahle, F., Hansen, M. O., Snel, H., Grasso, F., Larsen, T. J., Politis, E., and Rasmussen, F.: Blade element momentum modeling of inflow with shear in comparison with advanced model results, Wind Energy, 15, 63–81, https://doi.org/10.1002/we.493, 2012. a, b
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a
Medici, D., Ivanell, S., Dahlberg, J.-Ã., and Alfredsson, P. H.: The upstream flow of a wind turbine: blockage effect, Wind Energy, 14, 691–697, https://doi.org/10.1002/we.451, 2011. a, b, c
Meyer Forsting, A., Rathmann, O., Laan, M. v. d., Troldborg, N., Gribben, B., Hawkes, G., and Branlard, E.: Verification of induction zone models for wind farm annual energy production estimation (2019 J. Phys.: Conf. Ser. 1934 012023), J. Phys. Conf. Ser., 1934, 012024, https://doi.org/10.1088/1742-6596/1934/1/012024, 2021. a
Meyer Forsting, A. R., Troldborg, N., and Borraccino, A.: Modelling lidar volume-averaging and its significance to wind turbine wake measurements, J. Phys. Conf. Ser., 854, 012014, https://doi.org/10.1088/1742-6596/854/1/012014, 2017. a
Mikkelsen, T., Sjöholm, M., Angelou, N., and Mann, J.: 3D WindScanner lidar measurements of wind and turbulence around wind turbines, buildings and bridges, IOP Conf. Ser.-Mat. Sci., 276, 012004, https://doi.org/10.1088/1757-899X/276/1/012004, 2017. a
Mikkelsen, T., Sjöholm, M., Astrup, P., Peña, A., Larsen, G., van Dooren, M. F., and Kidambi Sekar, A. P.: Lidar Scanning of Induction Zone Wind Fields over Sloping Terrain, J. Phys. Conf. Ser., 1452, 012081, https://doi.org/10.1088/1742-6596/1452/1/012081, 2020. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst, 24, 163–187, https://gibbs.science/efd/handouts/monin_obukhov_1954.pdf (last access: 7 July 2024), 1954. a
NREL: FLORIS. Version 3.4, GitHub [code], https://github.com/NREL/floris, 2023. a
Pedersen, A. T. and Courtney, M.: Flywheel calibration of a continuous-wave coherent Doppler wind lidar, Atmos. Meas. Tech., 14, 889–903, https://doi.org/10.5194/amt-14-889-2021, 2021. a, b
Peña, A. and Mann, J.: Turbulence Measurements with Dual-Doppler Scanning Lidars, Remote Sensing, 11, 2444, https://doi.org/10.3390/rs11202444, 2019. a
Rahlves, C., Beyrich, F., and Raasch, S.: Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation, Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, 2022. a
Robey, R. and Lundquist, J. K.: Behavior and mechanisms of Doppler wind lidar error in varying stability regimes, Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, 2022. a
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
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
Sezer-Uzol, N. and Uzol, O.: Effect of steady and transient wind shear on the wake structure and performance of a horizontal axis wind turbine rotor, Wind Energy, 16, 1–17, https://doi.org/10.1002/WE.514, 2013. a
Simley, E., Angelou, N., Mikkelsen, T., Sjöholm, M., Mann, J., and Pao, L. Y.: Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars, J. Renew. Sustain. Ener., 8, 013301, https://doi.org/10.1063/1.4940025, 2016. a, b, c, d, e, f, g
Sjöholm, M., Angelou, N., Hansen, P., Hansen, K. H., Mikkelsen, T., Haga, S., Silgjerd, J. A., and Starsmore, N.: Two-Dimensional Rotorcraft Downwash Flow Field Measurements by Lidar-Based Wind Scanners with Agile Beam Steering, J. Atmos. Ocean. Tech., 31, 930–937, https://doi.org/10.1175/JTECH-D-13-00010.1, 2014. a
Slinger, C. W., Harris, M., and Pitter, M.: Wind speed measurement for absolute power curve determination from induction zone lidar measurements, J. Phys. Conf. Ser., 1618, 032027, https://doi.org/10.1088/1742-6596/1618/3/032027, 2020. a
Sonnenschein, C. M. and Horrigan, F. A.: Signal-to-Noise Relationships for Coaxial Systems that Heterodyne Backscatter from the Atmosphere, Appl. Opt., 10, 1600, https://doi.org/10.1364/ao.10.001600, 1971. a
Stawiarski, C., Traumner, K., Knigge, C., and Calhoun, R.: Scopes and challenges of dual-doppler lidar wind measurements-an error analysis, J. Atmos. Ocean. Tech., 30, 2044–2062, https://doi.org/10.1175/JTECH-D-12-00244.1, 2013. a, b
Tennekes and Lumley: A First Course in Turbulence, The MIT Press, ISBN 9780262536301, https://mitpress.mit.edu/9780262536301/a-first-course-in-turbulence/ (last access: 5 July 2024), 2018. a
Tobin, N., Hamed, A. M., and Chamorro, L. P.: Fractional Flow Speed-Up from Porous Windbreaks for Enhanced Wind-Turbine Power, Bound.-Lay. Meteorol., 163, 253–271, https://doi.org/10.1007/s10546-016-0228-8, 2017. a, b
Trabucchi, D.: Lidar Measurements and Engineering Modelling of Wind Turbine Wakes, PhD thesis, Carl von Ossietzky Universitat Oldenburg, https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-7516-8 (last access: 5 July 2024), 2020. a
Trujillo, J. J., Bingöl, F., Larsen, G. C., Mann, J., and Kühn, M.: Light detection and ranging measurements of wake dynamics. Part II: Two-dimensional scanning, Wind Energy, 14, 61–75, https://doi.org/10.1002/we.402, 2011. a
van Dooren, M. F., Trabucchi, D., and Kühn, M.: A methodology for the reconstruction of 2D horizontal wind fields of wind turbinewakes based on dual-Doppler lidar measurements, Remote Sensing, 8, 809, https://doi.org/10.3390/rs8100809, 2016. a
van Dooren, M. F., Campagnolo, F., Sjöholm, M., Angelou, N., Mikkelsen, T., and Kühn, M.: Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel, Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, 2017. a, b, c, d, e
Werner, C. and Streicher, J.: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, Springer, 102, ISBN 0-387-40075-3, 2005. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Fourth Edition, Statistical Methods in the Atmospheric Sciences, Fourth Edition, Elsevier, 1–818, https://doi.org/10.1016/C2017-0-03921-6, 2019. a
Wyngaard, J. C.: Turbulence in the Atmosphere, Cambridge University Press, Cambridge, ISBN 9780521887694, https://doi.org/10.1017/CBO9780511840524, 2010. a
Xie, S. and Archer, C. L.: A Numerical Study of Wind-Turbine Wakes for Three Atmospheric Stability Conditions, Bound.-Lay. Meteorol., 165, 87–112, https://doi.org/10.1007/s10546-017-0259-9, 2017. a
Short summary
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.
We present induction zone measurements conducted with two synchronised lidars at a two-turbine...
Altmetrics
Final-revised paper
Preprint