The Effect of Wind Turbine Wakes on Summertime U.S. Great Plains Atmospheric Wind Profiles as Observed with Ground Based Doppler LIDAR

Increased wind power utilization worldwide has sparked investigation into the effects that large wind farms have on local and regional microclimates. The Crop/Wind-energy Experiment (CWEX) seeks to quantify the relationship between wind turbine wakes, boundary layer processes, and crop health using measurements of surface fluxes and LIDAR wind profiles of the turbine-layer. In 2011, measurements were collected by Iowa State University, the University of Colorado at Boulder, the National Renewable Energy Laboratory, and the National Center for Atmospheric Research, focusing on a wind farm situated within and surrounded by central Iowa corn fields during the growing season; the characteristics of the land surface were defined by the progression of crop growth. Measurements included wind profiles north and south of a row of turbines collected by two Leosphere/NRG Windcube LIDARs and data from four surface flux stations. The instruments were aligned in a linear configuration to sample the free-stream boundary layer, the near-wake, and the far-wake during typical southerly flow conditions.

Data from the two LIDARs were filtered for the prevailing southerly flow in order to simultaneously capture inflow and waked conditions. This presentation compares data from the upwind and downwind LIDARs for horizontal and vertical wind speed, wind shear, wind direction, wind directional shear, horizontal and vertical turbulence intensity, turbulent kinetic energy, and the power law coefficient (alpha). Results indicate measurable reductions in waked wind speeds at heights spanning the wind turbine rotor (40m to 120m). Turbulent and wind shear quantities increase in the wake of the turbine rotor. Results also indicate that the power law coefficient below turbine hub height may be a convenient parameter for identifying whether the downwind LIDAR was sampling turbine wake or free flow conditions. Changes in quantities downwind of the wind turbine are also shown to vary with inflow wind speed and time of day. Results are consistent with the few observations available from other studies; this dataset contributes higher temporal and spatial resolution data to provide a dataset which will be useful for turbine wake model validation.