Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm

Maarten T. van Beek, Axelle Vire*, Søren J. Andersen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

3 Downloads (Pure)

Abstract

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.
Original languageEnglish
Article number1293
JournalEnergies
Volume14
Issue number5
Number of pages21
ISSN1996-1073
DOIs
Publication statusPublished - 2021

Keywords

  • Wind farm control
  • Wake steering
  • Lillgrund
  • Sensitivity analysis
  • Uncertainty analysis

Fingerprint Dive into the research topics of 'Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm'. Together they form a unique fingerprint.

Cite this