Wind turbine wake characterization using the SpinnerLidar measurements

Abstract

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.