Articles | Volume 5, issue 4
https://doi.org/10.5194/wes-5-1601-2020
© Author(s) 2020. 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-5-1601-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2020
Research article |
| 19 Nov 2020
| 19 Nov 2020
Optimal tuning of engineering wake models through lidar measurements
Lu Zhan
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
Stefano Letizia
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
Giacomo Valerio Iungo
CORRESPONDING AUTHOR
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, Texas, USA
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A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
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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
Acker, T. and Chime, A. H.: Wind modeling using WindPro and WAsP software, Norther Arizon University, USA, available at: https://docuri.com/download/wind-modeling-using-windpro-and-wasp-software_59c1d09ff581710b28646f50_pdf
(last access: 11 June 2020), 2011. a
Annoni, J., Gebraad, P. M., Scholbrock, A. K., Fleming, P. A., and Wingerden, J. W.: Analysis of axial-induction-based wind plant control using an engineering and a high-order wind plant model, Wind Energy, 19, 1135-1150, https://doi.org/10.1002/we.1891, 2016. a
Archer, C. L., Vasel-Be-Hagh, A., Yan, C., Wu, S., Pan, Y., Brodie, J. F., and Maguire, A. E.: Review and evaluation of wake loss models for wind energy applications, Appl. Energy, 226, 1187–1207, https://doi.org/10.1016/j.apenergy.2018.05.085, 2018. a
Barnes, S. L.: A technique for maximizing details in numerical weather map analysis, J. Appl. Meteorol., 3, 396–409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 1964. a
Barthelmie, R. J. and Jensen, L. E.: Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm, Wind Energy, 13, 573–586, https://doi.org/10.1002/we.408, 2010. a
Barthelmie, R. J., Frandsen, S. T., Nielsen, M. N., Pryor, S. C., Rethoré, 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
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energy, 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c
Chattot, J. J.: Helicoidal vortex model for wind turbine aeroelastic simulation, Comput. Struct., 85, 1072–1079, https://doi.org/10.1016/j.compstruc.2006.11.013, 2007. a
Dekker, J. W.: European wind turbine standards II: executive summary, edited by: Pierik, J. T., Netherlands Energy Research Foundation ECN, available at: https://cordis.europa.eu/docs/publications/4840/48401361-6_en.pdf (last access: 11 June 2020), 1998. a
Doubrawa, P., Debnath, M., Moriarty, P. J., Branlard, E., Herges, T. G., Maniaci, D. C., and Naughton, B.: Benchmarks for model validation based on lidar wake measurements, J. Phys.: Conf. Ser., 125, 012024, https://doi.org/10.1088/1742-6596/1256/1/012024, 2019. a
Duckworth, A. and Barthelmie, R. J.: Investigation and validation of wind turbine wake models, Wind Energy, 32, 459–475, https://doi.org/10.1260/030952408786411912, 2008. a
Duc, T., Coupiac, O., Girard, N., Giebel, G., and Göçcmen, T.: Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, Wind Energ. Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, 2019. a
El-Asha, S., Zhan, L., and Iungo, G. V.: Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data, Wind Energy, 20, 1823-1839, https://doi.org/10.1002/we.2123, 2017. a, b
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., and Thøgersen, M.: Analytical modelling of wind speed deficit in large offshore wind farms, Wind Energy, 9, 39–53, https://doi.org/10.1002/we.189, 2006. a, b
Gaumond, M., Réthoré, P. E., Bechmann, A., Ott, S., Larsen, G. C., Diaz, A. P., and Hansen, K. S.: Benchmarking of wind turbine wake models in large offshore wind farms, in: Proc. of The Science of Making Torque from Wind 2012: 4th scientific conference, available at: http://www.eera-dtoc.eu/wp-content/uploads/files/Gaumond-et-al-Benchmarking-of-wind-turbine-wake-models-
(last access: 11 June 2020), 2012. a
Göçcmen, T., and Giebel, G.: Data-driven wake modelling for reduced uncertainties in short-term possible power estimation, J. Phys.: Conf. Ser., 1037, 072002, https://doi.org/10.1088/1742-6596/1037/7/072002, 2018. a, b
González, J. S., Rodriguez, A. G., Mora, J. C., Santos, J. R., and Payan, M. B.: Optimization of wind farm turbines layout using an evolutive algorithm, Renew. Energy, 35, 1671–1681, https://doi.org/10.1016/j.renene.2010.01.010, 2010. a
Hansen, K. S., Barthelmie, R. J., Jensen, L. E., and Sommer, A.: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm, Wind Energy, 15, 183–196, https://doi.org/10.1002/we.512, 2012. a
Iungo, G. V.: LiDAR Cluster Statistic of Wind Turbine Wakes [Data set]. Wind Energy, Zenodo, https://doi.org/10.5281/zenodo.3604444, 2020. a, b
Iungo, G. V. and Porté-Agel, F.: Volumetric lidar scanning of wind turbine wakes under convective and neutral atmospheric stability regimes, J. Atmos. Ocean. Tech., 31, 2035–2048, https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a, b
Iungo, G. V., Santoni, C., Abkar, M., Porté-Agel, F., Rotea, M. A., and Leonardi, S.: Data-driven reduced order model for prediction of wind turbine wakes, J. Phys.: Conf. Ser., 625, 012009, https://doi.org/10.1088/1742-6596/625/1/012009, 2015. a
Iungo, G. V., Santhanagopalan, V., Ciri, U., Viola, F., Zhan, L., Rotea, M. A., and Leonardi, S.: Parabolic RANS solver for low-computational-cost simulations of wind turbine wakes, Wind Energy, 21, 184–197, https://doi.org/10.1002/we.2154, 2018a. a, b
Iungo, G. V., Letizia, S., and Zhan, L.: Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations, J. Phys.: Conf. Ser., 1037, 062010, https://doi.org/10.1088/1742-6596/1037/7/072023, 2018b. a, b, c
Jeon, S., Kim, B., and Huh, J.: Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine, Energy, 93, 1769–1777, https://doi.org/10.1016/j.energy.2015.09.086, 2015. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster efficiency, in: Proc. of Europ. Wind Energy Ass. Conf. Exhib, 407–410, 1987. a
Kim, H., Kim, K., Bottasso, C. L., Campagnolo, F., and Paek, I.: Wind turbine wake characterization for improvement of the Ainslie eddy viscosity wake model, Energies, 11, 2823, https://doi.org/10.3390/en11102823, 2018. a, b
Kusiak, A. and Song, Z.: Design of wind farm layout for maximum wind energy capture, Renew. Energy, 35, 685–694, https://doi.org/10.1016/j.renene.2009.08.019, 2010. a
Larsen, G. C., Aagaard, H. M., Bingöl, F., Mann, J., Ott, S., Sørensen, J. N., Okulov, V., Troldborg, N., Nielsen, N. M., Thomsen, K., and Larsen, T. J.: Dynamic wake meandering modeling, Forskningscenter Risoe, Risoe-R, No. 1607(EN), Risø National Laboratory, Roskilde, Denmark, 2007. a
Lee, J., Son, E., Hwang, B., Lee, S., and Lee, S.: Blade pitch angle control for aerodynamic performance optimization of a wind farm, Renew. Energy, 54, 124–130, https://doi.org/10.1016/j.renene.2012.08.048, 2013. a
Letizia, S., Zhan, L., and Iungo, G. V.: LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I: Theoretical framework, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-227, in review, 2020a. a
Letizia, S., Zhan, L., and Iungo, G. V.: LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part II: Applications to synthetic and real LiDAR data of wind turbine wakes, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-228, in review, 2020b. a
Luzzatto-Fegiz, P.: A one-parameter model for turbine wakes from the entrainment hypothesis, J. Phys.: Conf. Ser., 1037, 072019, https://doi.org/10.1088/1742-6596/1037/7/072019, 2018. a
Martínez-Tossas, L. A., Churchfield, M. J., and Leonardi, S.: Large eddy simulations of the flow past wind turbines: actuator line and disk modeling, Wind Energy, 18, 1047–1060, https://doi.org/10.1002/we.1747, 2015. a
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech, 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
Peña, A. and Rathmann, O.: Atmospheric stability-dependent infinite wind-farm models and the wake-decay coefficient, Wind Energy, 17, 1269–1285, https://doi.org/10.1002/we.1632, 2014. a
Pope, S. B.: Turbulent flows, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511840531, 2000. a
Renkema, D. J.: Validation of wind turbine wake models, MS Thesis, Delft University of Technology, Delft, the Netherlands, 19 pp., 2007. a
Santhanagopalan, V., Letizia, S., Zhan, L., Al-Hamidi, L. Y., and Iungo, G. V.: Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver, in: 2018 Wind Energ. Symp., AIAA SciTECH, Kissimmee, Florida, https://doi.org/10.2514/6.2018-2018, 2018a. a
Santhanagopalan, V., Rotea, M. A., and Iungo, G. V.: Performance optimization of a wind turbine column for different incoming wind turbulence, Renew. Energy, 116, 232–243, https://doi.org/10.1016/j.renene.2017.05.046, 2018b. a
Santoni, C., Carrasquillo, K., Arenas-Navarro, I., and Leonardi, S.: Effect of tower and nacelle on the flow past a wind turbine, Wind Energy, 20, 1927–1939, https://doi.org/10.1002/we.2130, 2017. a
Schlichting, H. and Gersten, K.: Boundary-layer theory, Springer, Berlin, Germany, 2016. a
Sebastian, T. and Lackner, M. A.: Development of a free vortex wake method code for offshore floating wind turbines, Renew. Energy, 46, 269–275, https://doi.org/10.1016/j.renene.2012.03.033, 2012. a
Shaler, K., Kecskemety, K. M., and McNamara, J. J.: Benchmarking of a free vortex wake model for prediction of wake interactions, Renew. Energy, 136, 607–620, https://doi.org/10.1016/j.renene.2018.12.044, 2019. a
Shapiro, C. R., Starke, G. M., Meneveau, C., and Gayme, D. F.: A wake modeling paradigm for wind farm design and control, Energies, 12, 2956, https://doi.org/10.3390/en12152956, 2019. a
Stull, R. B.: An introduction to boundary layer meteorology, Springer Science, the Netherlands, 1988. a
Swain, L. M.: On the turbulent wake behind a body of revolution, P. Roy. Soc. Lond. A, 125, 647–659, https://doi.org/10.1098/rspa.1929.0193, 1929.
a, b
Tian, J., Zhou, D., Su, C., Soltani, M., Chen, Z., and Blaabjerg, F.: Wind turbine power curve design for optimal power generation in wind farms considering wake effect, Energies, 10, 395, https://doi.org/10.3390/en10030395, 2017. a
US Geological Survey: Website, available at: https://www.usgs.gov/, last access: 31 October 2017. a
Zhan, L., Letizia, S., and Iungo, G. V.: Matlab function for optimal tuning of wake engineering models, available at: https://www.utdallas.edu/windflux/ (last access: 11 June2020), 2020b. a
Short summary
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning...
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