Articles | Volume 6, issue 5
https://doi.org/10.5194/wes-6-1227-2021
© 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-6-1227-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations
Department of Wind Energy, Technical University of Denmark, Risø
Lab/Campus, Roskilde 4000, Denmark
Søren Juhl Andersen
Department of Wind Energy, Technical University of Denmark, Lyngby 2800,
Denmark
Ásta Hannesdóttir
Department of Wind Energy, Technical University of Denmark, Risø
Lab/Campus, Roskilde 4000, Denmark
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Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
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Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
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This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Mads Baungaard, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 783–800, https://doi.org/10.5194/wes-7-783-2022, https://doi.org/10.5194/wes-7-783-2022, 2022
Short summary
Short summary
Wind turbine wakes are dependent on the atmospheric conditions, and it is therefore important to be able to simulate in various different atmospheric conditions. This paper concerns the specific case of an unstable atmospheric surface layer, which is the lower part of the typical daytime atmospheric boundary layer. A simple flow model is suggested and tested for a range of single-wake scenarios, and it shows promising results for velocity deficit predictions.
Maarten Paul van der Laan, Mark Kelly, and Mads Baungaard
Wind Energ. Sci., 6, 777–790, https://doi.org/10.5194/wes-6-777-2021, https://doi.org/10.5194/wes-6-777-2021, 2021
Short summary
Short summary
Wind farms operate in the atmospheric boundary layer, and their performance is strongly dependent on the atmospheric conditions. We propose a simple model of the atmospheric boundary layer that can be used as an inflow model for wind farm simulations for isolating a number of atmospheric effects – namely, the change in wind direction with height and atmospheric boundary layer depth. In addition, the simple model is shown to be consistent with two similarity theories.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
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Short summary
The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Cited articles
Aagard Madsen, H., Bak, C., Paulsen, U. S., Guanaa, M., Fuglsang, P.,
Romblad, J., Olesen, N. A., Enevoldsen, P., Laursen, J., and Jensen, L.: The
DAN-AERO MW Experiments Final report, Tech. Rep. Risø-R-1726-(EN),
Risø National Laboratory, Roskilde, Denmark, 2010.
Abkar, M. and Porté-Agel, F.: The Effect of Free-Atmosphere
Stratification on Boundary-Layer Flow and Power Output from Very Large Wind
Farms, Energies, 6, 2339–2361,
https://doi.org/10.3390/en6052338, 2013.
Alcayaga Román, L. A.: From Gusts to Turbulence: Vertical Structure (Fra
Vindstød Til Turbulens: Vertikale Struktur), MSc Thesis, Danish
Technical University/Oldenburg University, 2017.
Allaerts, D. and Meyers, J.: Wind farm performance in conventionally
neutral atmospheric boundary layers with varying inversion strengths,
J. Phys. Conf. Ser., 524, 012114, https://doi.org/10.1088/1742-6596/524/1/012114, 2014.
Andersen, S. J.: Simulation and Prediction of Wakes and Wake Interaction in
Wind Farms, PhD Dissertation, Danish Technical University, 2014.
Andersen, S. J., Sørensen, J. N., and Mikkelsen, R. F.: Performance and
Equivalent Loads of Wind Turbines in Large Wind Farms, J. Phys. Conf.
Ser., 854, 012001,
https://doi.org/10.1088/1742-6596/854/1/012001, 2017a.
Andersen, S. J., Sørensen, J. N., and Mikkelsen, R. F.: Turbulence and
entrainment length scales in large wind farms,
Philos. T. R. Soc. A,
375, 20160107, https://doi.org/10.1098/rsta.2016.0107,
2017b.
Andersen, S. J., Sørensen, N. N., and Kelly, M.: LES Modelling of Highly
Transient Wind Speed Ramps in Wind Farms, J. Phys. Conf. Ser.,
1934, 012015, https://doi.org/10.1088/1742-6596/1934/1/012015, 2021.
Berg, J., Vasiljevic, N., Kelly, M., Lea, G., and Courtney, M.: Addressing
Spatial Variability of Surface-Layer Wind with Long-Range WindScanners,
J. Atmos. Ocean. Tech., 32, 518–527, 2015.
DeMarco, A. and Basu, S.: On the tails of the wind ramp distributions, Wind
Energy, 21, 892–905, https://doi.org/10.1002/we.2202, 2018.
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018.
Dimitrov, N. K. and Natarajan, A.: Application of simulated lidar scanning
patterns to constrained Gaussian turbulence fields for load validation, Wind
Energy, 20, 79–85, https://doi.org/10.1002/we.1992, 2017.
Frandsen, S. T., Jørgensen, H. E., and Sørensen, J. D.: Relevant
Criteria for Testing the Quality of Models for Turbulent Wind Speed
Fluctuations, J. Sol. Energ. T-ASME, 130, 31016–31022, https://doi.org/10.1115/1.2931511, 2008.
Galinos, C. and Larsen, T. J.: Investigation of rotor imbalance on a
NEG-Micon 80 Wind Turbine, DTU Wind Energy Tech. Report I-0386,
Roskilde/Risø Campus, 13 pp., 2015.
Gallego-Castillo, C., Cuerva-Tejero, A., and Lopez-Garcia, O.: A review on
the recent history of wind power ramp forecasting, Renew. Sust. Energ. Rev.,
52, 1148–1157, https://doi.org/10.1016/j.rser.2015.07.154,
2015.
Gilling, L., Sørensen, N. N., and Réthoré, P.-E.: Imposing
resolved turbulence by an actuator in a detached eddy simulation of an
airfoil, Proc. Of the 2004 European Wind Energy Conference, European Wind
Energy Association (EWEA), Marseille, 2009.
Hannesdóttir, Á.: Extreme wind speed ramps – probabilistic
characterization for turbine loads, PhD Dissertation, Danish
Technical University, 2019.
Hannesdóttir, Á. and Kelly, M.: Detection and characterization of extreme wind speed ramps, Wind Energ. Sci., 4, 385–396, https://doi.org/10.5194/wes-4-385-2019, 2019.
Hannesdóttir, Á., Kelly, M., and Dimitrov, N. K.: Extreme variance
vs. turbulence: What can the IEC cover?, Wind Energy Denmark 2017, poster session,
Herning, Denmark, 2017.
Hannesdóttir, Á., Kelly, M., and Dimitrov, N.: Extreme wind fluctuations: joint statistics, extreme turbulence, and impact on wind turbine loads, Wind Energ. Sci., 4, 325–342, https://doi.org/10.5194/wes-4-325-2019, 2019.
Hannesdóttir, Á, Urbán, A. L., and Verelst, D. R.: Extreme
coherent gusts with direction change – observations, yaw control and wind
turbine loads, Wind Energ. Sci. Discuss., in preparation, 2021.
International Electrotechnical Commission: IEC 61400-1 Wind turbines – Part 1: Design requirements, 4th Edn., International
Electrotechnical Commission, Geneva, Switzerland, 2019.
Jahn, D. E., Takle, E. S., and Gallus Jr., W. A.: Wind-Ramp-Forecast
Sensitivity to Closure Parameters in a Boundary-Layer Parametrization
Scheme, Bound.-Lay. Meteorol. 164, 475–490, https://doi.org/10.1007/s10546-017-0250-5, 2017.
Kelly, M.: From standard wind measurements to spectral characterization: turbulence length scale and distribution, Wind Energ. Sci., 3, 533–543, https://doi.org/10.5194/wes-3-533-2018, 2018.
Kelly, M., Larsen, G. C., Dimitrov, N. K., and Natarajan, A.: Probabilistic
Meteorological Characterization for Turbine Loads, J. Phys. Conf. Ser.,
524, 012076, https://doi.org/10.1088/1742-6596/524/1/012076, 2014.
Kelly, M., Cersosimo, R. A., and Berg, J.: Universal wind profile for the
inversion-capped neutral atmospheric boundary layer, Q. J. Roy. Meteor.
Soc., 145, 982–992, 2019a.
Kelly, M., Andersen S. J., and Hannesdóttir, Á.: Impact of wind-speed
ramps on turbines: from fluid-dynamic to aeroelastic simulation, via
observed joint statistics, Tech. Rep. DTU Wind Energy E-0194, Danish
Technical University, Risø Campus, Roskilde, Denmark, 2019b.
Liu, S. and Liang, X.-Z: Observed Diurnal Cycle Climatology of Planetary
Boundary-Layer Height, J. Climate, 23, 5790–5809, 2010.
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994.
Mann, J.: Wind Field Simulation, Prob. Eng. Mech., 13, 269–282, 1998.
Michelsen, J. A.: Basis3D – a platform for development of a multiblock PDE
solver, Tech. Rep. DTU AFM 92-05, Danish Technical University, 1992.
Musilek, P. and Li, Y.: Forecasting of wind ramp events – analysis of cold
front detection, Proc. 31st International Symposium on Forecasting,
Prague, Czech Republic, June 2011.
Nygaard, N. G. and Hansen, S. D.: Wake Effects between Two Neighbouring Wind
Farms, J. Phys. Conf. Ser., 753, 032020, https://doi.org/10.1088/1742-6596/753/3/032020, 2016.
Øye, S.: FLEX5 simulation of wind turbine dynamics, Proc. 28th IEA
Meeting of Experts Concerning State of the Art of Aeroelastic Codes for Wind
Turbine Calculations, International Energy Agency, Lyngby/DTU, Denmark,
1996.
Panofsky, H. A. and Dutton, J. A.: Atmospheric Turbulence, Wiley, New York, 397 pp., 1984.
Peña Diaz, A., Floors, R., Sathe, A., Gryning, S.-E., Wagner, R.,
Courtney, M. S., Larsén, X. G., Hahmann, A. H., and Hasager, C. B.: Ten
Years of Boundary-Layer and Wind-Power Meteorology at Høvsøre,
Denmark, Bound.-Lay. Meteorol., 158, 1–26, 2016.
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.
Sørensen, J. N. and Shen, W. Z.: Numerical modelling of Wind Turbine
Wakes, J. Fluids Eng., 124, 393–399,
https://doi.org/10.1115/1.1471361, 2002.
Sørensen, J. N., Mikkelsen, R. F., Henningson, D. S., Ivanell, S.,
Sarmast, S., and Andersen, S. J.: Simulation of wind turbine wakes using the
actuator line technique, Philos. T. R. Soc. A, 373, 20140071, https://doi.org/10.1098/rsta.2014.0071, 2015.
Sørensen, N. N.: General purpose flow solver applied to flow over hills,
PhD Dissertation, Danish Technical University, Risø National Laboratory,
1995.
Ta Phuoc, L.: Modèles de sous-maille appliqués aux écoulements
instationnaires et décollés, Journée thématique
DRET – Aérodynamique instationnaire turbulente, aspects numériques
et expérimentaux, 1994.
Ta Phuoc, L., Lardat, R., Coutanceau, M., and Pineau, G.: Recherche et
analyse de modeles de turbulence de sous maille adaptés aux
écoulements instationnaires décollés, Tech. Rep. LIMSI 93074,
Lab. d'Inf. pour la Mecanique et les Sci. de l'Ingenieur, Orsay, 1994.
Troldborg, N.: Actuator line modeling of wind turbine wakes, PhD
Dissertation, Danish Technical University, 2009.
Wyngaard, J.: Turbulence in the Atmosphere, Cambridge Univ. Press, Cambridge, UK, 393 pp., 2010.
Short summary
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of...
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