1415 Case Study of Wind Turbine Wake Using Scanning Doppler Lidar Measurements in Complex Terrain

Wednesday, 25 January 2017
Yelena Pichugina, CIRES/Univ. of Colorado, Boulder, CO; and A. Choukulkar, R. M. Banta, A. Brewer, J. K. Lundquist, J. B. Olson, T. A. Bonin, M. Marquis, N. Bodini, S. Sand, and S. Redfern

Velocity deficits and enhanced turbulence generated by wind turbine blade rotation are two major wake effects that could extend to distances of several rotor diameters behind a wind turbine. Accounting for wakes is an important issue in the optimization of siting turbines in a wind farm, operational strategies to reduce wake effects, and improved design of wind turbines. The complexity of wake effects depends on many factors, including hardware (turbine size, rotor speed, blade geometry, etc.) and meteorological features (prevalent wind direction at the site, wind velocity, wind gradients, boundary layer stability, turbulence characteristics, etc.).

            During the WFIP2 experiment, two identical scanning Doppler lidars were deployed to research sites separated by ~ 40 km.  Simultaneous measurements utilizing similar scanning patterns are being collected at the two sites. Such data present an opportunity to analyze the wind flow upstream and downstream of the local wind farms and obtain valuable information on the variability of wind flow between the two sites. This variability is due to the combined effects of the distance between the lidars, the difference in the elevation of the lidar locations, the surrounding topography, and the influence of operating wind turbines on wind flow passing through this area.

            This paper will present an analysis of wakes observed during selected cases when the lidar beam was reasonably well aligned with the prevalent wind direction in the rotor layer. These cases were selected by examining the lidar measurements from individual conical scans obtained at low-elevation-angle (1.80-2.50) to identify periods of waked wind flow downwind of turbines located at distances of 2-4 km from the lidars and at 246-294 (m) above sea level (ASL) for westerly and easterly wind directions. The results from these particular cases show a velocity reduction of 1-5 m/s up to 300-600 m downstream. In addition, wind field retrievals made using the Optimal Interpolation technique will be utilized to study wake characteristics. Other wake parameters such as width and meandering are difficult to estimate due to the dynamic and inconsistent nature of wake interference from multiple nearby turbines located at different heights AGL. This would require measurements from an array of lidars.

Overall, the analysis of single lidar measurements at two separated sites provides better understanding of wind flow variability to improve wind modelling accuracy and to validate wind farm parametrization models.

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