The copyright line for this article was changed on 17 July 2013 after original online publication.
Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm
Article first published online: 6 MAY 2013
© 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Volume 17, Issue 8, pages 1169–1178, August 2014
How to Cite
Gaumond, M., Réthoré, P.-E., Ott, S., Peña, A., Bechmann, A. and Hansen, K. S. (2014), Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm. Wind Energ., 17: 1169–1178. doi: 10.1002/we.1625
- Issue published online: 14 JUL 2014
- Article first published online: 6 MAY 2013
- Manuscript Accepted: 22 MAR 2013
- Manuscript Revised: 28 FEB 2013
- Manuscript Received: 9 NOV 2012
- EUDP WakeBench. Grant Number: 64011-0308
- EERA DTOC. Grant Number: FP7-ENERGY-2011/n 282797
- wind farm;
- power deficit;
- wind direction
Accurately quantifying wind turbine wakes is a key aspect of wind farm economics in large wind farms. This paper introduces a new simulation post-processing method to address the wind direction uncertainty present in the measurements of the Horns Rev offshore wind farm. This new technique replaces the traditional simulations performed with the 10 min average wind direction by a weighted average of several simulations covering a wide span of directions. The weights are based on a normal distribution to account for the uncertainty from the yaw misalignment of the reference turbine, the spatial variability of the wind direction inside the wind farm and the variability of the wind direction within the averaging period. The results show that the technique corrects the predictions of the models when the simulations and data are averaged over narrow wind direction sectors. In addition, the agreement of the shape of the power deficit in a single wake situation is improved. The robustness of the method is verified using the Jensen model, the Larsen model and Fuga, which are three different engineering wake models. The results indicate that the discrepancies between the traditional numerical simulations and power production data for narrow wind direction sectors are not caused by an inherent inaccuracy of the current wake models, but rather by the large wind direction uncertainty included in the dataset. The technique can potentially improve wind farm control algorithms and layout optimization because both applications require accurate wake predictions for narrow wind direction sectors. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.