Preprints
https://doi.org/10.5194/wes-2020-135
https://doi.org/10.5194/wes-2020-135

  04 Feb 2021

04 Feb 2021

Review status: a revised version of this preprint was accepted for the journal WES.

Calibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristics

Davide Conti1, Nikolay Dimitrov1, Alfredo Peña1, and Thomas Herges2 Davide Conti et al.
  • 1Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
  • 2Sandia National Laboratories, Albuquerque, New Mexico, 87123, USA

Abstract. In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.

Davide Conti et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2020-135', Anonymous Referee #1, 11 May 2021
  • RC2: 'Comment on wes-2020-135', Anonymous Referee #2, 18 May 2021

Davide Conti et al.

Davide Conti et al.

Viewed

Total article views: 479 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
326 147 6 479 10 6
  • HTML: 326
  • PDF: 147
  • XML: 6
  • Total: 479
  • BibTeX: 10
  • EndNote: 6
Views and downloads (calculated since 04 Feb 2021)
Cumulative views and downloads (calculated since 04 Feb 2021)

Viewed (geographical distribution)

Total article views: 467 (including HTML, PDF, and XML) Thereof 467 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jul 2021
Download
Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected as part of the Small Wind Farm Technology (SWiFT) experiment in Texas. The analysis includes both the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric stability conditions.