IOWA STATE UNIVERSITY
Wind energy, one of the cleanest renewable energy sources, is drawing more and more attention in recent years be- cause it is widely available, relatively cheap, simple to harness electrical power, and it doesn’t pollute the atmosphere. Recently, a target of 20% of US electricity from wind energy by 2030 has been set up by the U.S. Department of Energy (DOE), National Renewable Energy Laboratory (NREL), and American Wind Energy Association (AWEA). According to the 2010 annual report of International Energy Agency (IEA), wind energy only provides approximately 2.3 percent of total U.S. electricity generation by the end of 2010. To produce 20 percent of ther electricity by 2030, U.S. wind power capacity will need to exceed 300 gigawatts (Schreck et al., 2008). Suppose if each wind turbine could generate 2MW, which corresponds to large wind turbines with their diameters about 80m, it would take approximately 150,000 wind turbines to meet this goal. It could be imagined that this would require a large number of wind turbines arranged in a wind farm. Turbine dynamics, micrositing and array effects have been delineated as the significant topics amongst the four most focus areas in research needs for wind resource characterization (Schreck et al., 2008). Specifically, detailed measurements and computational modeling were recommended to characterize the inflows and turbine flow field in order to attain the accuracy levels in aerodynamic loads that will be required for future mechanical design. The focus area on micrositing and array effects is to target improvement of mean structure and turbulence statistics of wake models in atmospheric boundary layer corresponding to various atmospheric stabilities and complex topographies. The present research effort on wind turbine interactions and complex terrain effect was motivated by the re- search needs as mentioned above. It was realized that it is essential to understand the effects of wind turbine interactions in a large wind farm in order to optimize the arrangement of turbines to characterize energy capture losses as well as dynamic wind loads. Comprehensive literature reviews on single wind turbine wake research are given by Vermeer et al. (2003). Even though it is of significance to conduct fundamental study on a single freestanding wind turbine, we have to pay more attention to the more realistic wind turbine array over different topographies.
It has been found that a power generation could lose up to 40% when the wind turbine operates within an array other than with free incoming flow (Barthelmie et al., 2007; Corten et al., 2004). Lebron et al. (2009) conducted an experimental study on the interaction between a wind turbine array and turbulent boundary layer with the main aim of collecting velocity data in sufficient detail to address questions such as the mechanisms for kinetic energy entrainment into the wind turbine array field, how to model wind turbines in atmospheric boundary layer in large-scale computer simulation, and thus providing tools for placement optimization. From the same project, Cal et al. (2010) carried out an experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer. However, wind turbine interaction arranged in an array over complex terrains has not been explored fully.
In the present study, a high-resolution Particle Image Velocity (PIV) system was used to study the wake be- hind a wind turbine placed at different locations within wind turbines in tandem over a flat open terrain and a 2D- ridge. The tests were conducted in an Atmospheric Boundary Layer (ABL) wind tunnel, where in addition to the flow measurement, dynamic thrust and power outputs were measured. The evolution of tip vortices and nacelle wake flow were revealed clearly in the PIV measurement results. The unsteady features of vortex strength, locations and propagation of the near wake flow were also obtained from the PIV measurements. The main focus of this paper will be on the velocity deficit in the wake and its effect on power output and wind loads with the wind turbines siting on two different terrains. The detailed flow field measurements were correlated with the wind load and power output measurements to elucidate the underlying physics associated with the energy loss and fatigue life. The quantitative wake information can be used to verify CFD codes with respect to “real world” issues such as wind shear and turbulence effect.
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