Articles | Volume 3, issue 1
Wind Energ. Sci., 3, 329–343, 2018
https://doi.org/10.5194/wes-3-329-2018
Wind Energ. Sci., 3, 329–343, 2018
https://doi.org/10.5194/wes-3-329-2018
Research article
01 Jun 2018
Research article | 01 Jun 2018

Wind tunnel experiments on wind turbine wakes in yaw: effects of inflow turbulence and shear

Jan Bartl et al.

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Preprint withdrawn
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Cited articles

Annoni, J., Gebraad, P., Scholbrock, A., Fleming, P., and van Wingerden, J.-W.: Analysis of axial-induction-based wind plant control using an engineering and a high-order wind plant model, Wind Energy, 19, 113–1150, https://doi.org/10.1002/we.1891, 2016. a
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Schepers, J. G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E., and Neckelmann, S.: Quantifying the impact of wind turbine wakes on power output at offshore wind farms, J. Atmos. Ocean. Tech., 27, 1302–1317, https://doi.org/10.1175/2010JTECHA1398.1, 2010. a
Bartl, J. and Sætran, L.: Experimental testing of axial induction based control strategies for wake control and wind farm optimization, J. Phys. Conf. Ser., 753, 032035, https://doi.org/10.1088/1742-6596/753/3/032035, 2016. a
Bartl, J. and Sætran, L.: Blind test comparison of the performance and wake flow between two in-line wind turbines exposed to different turbulent inflow conditions, Wind Energ. Sci., 2, 55–76, https://doi.org/10.5194/wes-2-55-2017, 2017. a, b, c, d
Bartl, J., Müller, A., Landolt, A., Mühle, F., Vatn, M., Oggiano, L., and Sætran, L.: Validation of the real-time-response ProCap measurement system for full wake scans behind a yawed model-scale wind turbine, DeepWind 2018 Conference, J. Phys. Conf. Ser., in review, 2018. a
Short summary
Wake steering by yawing a wind turbine offers great potential to increase the wind farm power production. A model scale experiment in a controlled wind tunnel environment has been performed to map the wake flow's complex velocity distribution for different inflow conditions. A non-uniform sheared inflow was observed to affect the wake flow only insignificantly. The level of turbulent velocity fluctuations in the inflow, however, influenced the wake's velocity distribution to a higher degree.