Journal cover Journal topic
Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
Journal topic

Journal metrics

CiteScore value: 0.6
CiteScore
0.6
h5-index value: 13
h5-index13
Preprints
https://doi.org/10.5194/wes-2020-104
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-104
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Sep 2020

24 Sep 2020

Review status
This preprint is currently under review for the journal WES.

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Davide Conti1, Vasilis Pettas2, Nikolay Dimitrov1, and Alfredo Peña1 Davide Conti et al.
  • 1Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
  • 2Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany

Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists in incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time-series, which are derived by fitting a bivariate Gaussian shape function on lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the Dynamic Wake Meandering (DWM) model. The lidar-reconstructed wake fields are input to aeroelastic simulations of the DTU 10 MW wind turbine and the resulting load predictions are compared with loads obtained with the target (no lidar-based) DWM simulated fields. The accuracy of load predictions is estimated across a variety of lidar beam configurations, probe volume sizes, and atmospheric turbulence conditions. The results indicate that the 10-min power and fatigue load statistics, predicted with lidar-reconstructed fields, are comparable with results obtained with the DWM simulations. Furthermore, the simulated power and load time-series exhibit a high level of correlation with the target observations, thus decreasing the statistical uncertainty (realization-to-realization) by a factor between 1.2 and 5, compared to results obtained with the baseline, which is DWM simulated fields with different random seeds. Finally, we show that the spatial resolutions of the lidar's scanning strategies as well as the size of the probe volume are critical aspects for the accuracy of the reconstructed wake fields and load predictions.

Davide Conti et al.

Interactive discussion

Status: open (until 22 Nov 2020)
Status: open (until 22 Nov 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Davide Conti et al.

Davide Conti et al.

Viewed

Total article views: 201 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
144 54 3 201 2 2
  • HTML: 144
  • PDF: 54
  • XML: 3
  • Total: 201
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 24 Sep 2020)
Cumulative views and downloads (calculated since 24 Sep 2020)

Viewed (geographical distribution)

Total article views: 156 (including HTML, PDF, and XML) Thereof 155 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 29 Oct 2020
Publications Copernicus
Download
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time-series into the turbulence fields. The uncertainty of the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment...
Citation