Journal article · Conference paper
Wind turbine wake characterization using the SpinnerLidar measurements
We analyze SpinnerLidar measurements of a single wind turbine wake collected at the SWiFT facility and investigate the wake behaviour under different atmospheric turbulence conditions. The derived wake characteristics include the wake deficit, wake-added turbulence and wake meandering in both lateral and vertical directions.
The atmospheric stability at the site is characterized using observations from a sonic anemometer. A wake-tracking technique, based on a bi-variate Gaussian wake shape, is implemented to monitor the wake center dis-placements in time to derive quasi-steady wake deficit and turbulence profiles in a meandering frame of reference.
The analysis demonstrates the influence of atmospheric stability on the wake behaviour; a faster wake deficit recovery and a higher level of turbulence mixing are observed under unstable compared to stable atmospheric conditions. We also show that the wake me-andering is driven by large-scale turbulence structures, which are characterized by increasing energy content as the atmosphere becomes more unstable.
These results suggest the suitability of the dataset for wake-model calibration and provide statistics of the wake deficit, turbulence levels, and meandering, which are key aspects for load validation studies.
Language: | English |
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Publisher: | IOP Publishing |
Year: | 2020 |
Pages: | 062040 |
Proceedings: | TORQUE 2020 |
ISSN: | 17426596 and 17426588 |
Types: | Journal article and Conference paper |
DOI: | 10.1088/1742-6596/1618/6/062040 |
ORCIDs: | Conti, Davide , Dimitrov, Nikolay Krasimirov and Peña, Alfredo |