School of Meteorology (Defense)

Optimizing Lidar Scanning Strategies for Wind Energy Turbulence Measurements

Jennifer Newman
OU School of Meteorology

16 April 2015, 2:00 PM

National Weather Center, Room 3620
(CAPS Conference Room)
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Turbulence has a significant effect on wind farm power production and turbine reliability. Since remote sensing devices such as lidars are commonly used in wind energy studies, the accurate measurement of turbulence with lidars is an important research goal in the wind energy industry. However, several factors cause lidars to measure different values of turbulence than a sonic anemometer on a tower, including the use of a scanning circle to estimate the three-dimensional wind field and the velocity variance contamination that can occur when a traditional Doppler-beam swinging (DBS) or velocity-azimuth display (VAD) strategy is used.

One way to avoid the use of a scanning circle is to deploy multiple scanning lidars and point them toward the same volume in space to collect velocity measurements. To test the ability of multi-lidar scanning strategies to measure mean wind speeds and turbulence, a lidar experiment was conducted in summer 2013 at the Southern Great Plains Atmospheric Radiation Measurement site. Two scanning strategies were tested: the tri-Doppler technique, where three scanning lidars were pointed to approximately the same point in space, and the virtual tower technique, where the lidars were pointed at several different locations above the ground. Another field campaign was conducted at the Boulder Atmospheric Observatory in Colorado to evaluate the ability of the novel six-beam lidar scanning technique, as well as the DBS and VAD techniques, to measure turbulence under different atmospheric stability conditions.

Based on results from the two lidar campaigns, the horizontal components of lidar-measured turbulence are often inaccurate under unstable conditions as a result of variance contamination and horizontal heterogeneity across the lidar scanning circle. To improve lidar variance estimates under unstable conditions, two correction techniques were developed and tested on data from the field campaigns. Similarity theory was used to estimate the approximate ratio of the horizontal variance components to the vertical variance as a function of stability, which can then be used to calculate new values of the horizontal variance components. Taylor's frozen turbulence hypothesis was used to estimate changes in the vertical velocity field across the lidar scanning circle such that the contribution of vertical velocity to the horizontal velocity components could be taken into account. Both techniques reduced lidar variance estimates significantly and brought the estimates closer to variance estimated by sonic anemometers.

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