Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm

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Abstract

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. Copyright © 2013 John Wiley & Sons, Ltd.
Original languageEnglish
JournalWind Energy
Volume17
Issue number8
Pages (from-to)1169–1178
ISSN1095-4244
DOIs
Publication statusPublished - 2014

Keywords

  • Wind farm
  • Offshore
  • Wake
  • Power deficit
  • Wind direction

Cite this

@article{7305c44a91a3448cbd008bd85965db0d,
title = "Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm",
abstract = "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. Copyright {\circledC} 2013 John Wiley & Sons, Ltd.",
keywords = "Wind farm, Offshore, Wake, Power deficit, Wind direction",
author = "M. Gaumond and Pierre-Elouan R{\'e}thor{\'e} and S{\o}ren Ott and Alfredo Pe{\~n}a and Andreas Bechmann and Hansen, {Kurt Schaldemose}",
year = "2014",
doi = "10.1002/we.1625",
language = "English",
volume = "17",
pages = "1169–1178",
journal = "Wind Energy",
issn = "1095-4244",
publisher = "JohnWiley & Sons Ltd.",
number = "8",

}

TY - JOUR

T1 - Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm

AU - Gaumond, M.

AU - Réthoré, Pierre-Elouan

AU - Ott, Søren

AU - Peña, Alfredo

AU - Bechmann, Andreas

AU - Hansen, Kurt Schaldemose

PY - 2014

Y1 - 2014

N2 - 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. Copyright © 2013 John Wiley & Sons, Ltd.

AB - 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. Copyright © 2013 John Wiley & Sons, Ltd.

KW - Wind farm

KW - Offshore

KW - Wake

KW - Power deficit

KW - Wind direction

U2 - 10.1002/we.1625

DO - 10.1002/we.1625

M3 - Journal article

VL - 17

SP - 1169

EP - 1178

JO - Wind Energy

JF - Wind Energy

SN - 1095-4244

IS - 8

ER -