9A.2 Effect of wind turbine wakes on cropland surface fluxes in the US great plains during a nocturnal low level jet

Thursday, 27 January 2011: 3:45 PM
4C-4 (Washington State Convention Center)
Michael E. Rhodes, Univ. of Colorado, Boulder, CO; and M. L. Aitken, J. K. Lundquist, E. S. Takle, and J. H. Prueger

Installation of large scale wind farms is becoming a common operation in the Midwest, and wind farms frequently are situated among fields of agricultural crops. Each wind turbine is known to alter the behavior of the air mass downwind of the rotor; consequently, the rotor wakes alter the local microclimate. Quantification of the effects of wind turbine wakes on local microclimate is required to understand how large-scale wind deployment affects large-scale agriculture.

This study examines the potential effect of wind turbine wakes on a corn crop in central Iowa during summer 2010. The field site consisted of one surface flux tower upwind of a row of five modern wind turbine generators, an identical surface flux station downwind of the turbine row, and a ground based LIDAR system downwind of the wind turbines. Each flux tower was instrumented with an array consisting of radiometers, a three-dimensional sonic anemometer, an open cell CO2 analyzer, a cup anemometer and wind vane, temperature and relative humidity sensors, and a tipping bucket. The LIDAR system reliably obtained readings up to 200 m above ground level (AGL), spanning the entire rotor disk (~40 m to 120 m AGL).

This presentation examines wake-surface interaction on one particular night, during which the prevailing winds situated the LIDAR directly behind a wind turbine approximately 2 rotor diameters downwind of the turbine tower. As expected preliminary LIDAR results indicate that in the turbine rotor shadow there is a strong deficit of horizontal momentum. Additionally, a strong nocturnal low-level jet occurred above the turbine rotor disk. Wavelet spectral analysis indicates that oscillatory behavior, with frequencies characteristic of wind turbine wakes, is observed in the LIDAR horizontal and vertical winds and in the downwind flux station datastreams. The characterization of wake effects provided by this unique dataset will allow for better parameterization and modeling of wind turbine wake dynamics.

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