15A.3 Scanning of wind turbine upwind conditions: numerical algorithm and first applications

Thursday, 12 June 2014: 11:00 AM
Queens Ballroom (Queens Hotel)
Marc Calaf, University of Utah, Wind Energy and Turbulence Laboratory, Salt Lake City, UT; and V. Sharma, G. Cortina, and M. B. Parlange

With the extensive development of large wind farms around the world, wind turbine designs are quickly evolving, increasing overall efficiency and progressively increasing the rotor diameter. However, the way in which wind turbines obtain in-situ meteorological information remains the same – i.e., by means of a traditional wind vane and cup anemometer installed at the turbine's nacelle, right behind the blades. This has two important drawbacks, especially with increasing rotor dimensions: 1) since the velocity is measured immediately behind the blades, turbine misalignment with the mean wind direction is common and energy losses are experienced; and 2) the near-blade monitoring does not provide any time to readjust the profile of the wind turbine to incoming turbulence gusts. This latency in adjusting to wind direction and intensity prevents timely profile readjustment and subjects the blades and structure to wind gusts or extreme incoming wind conditions. These velocity aberrations induce increased loading, structural fatigue, and associated increases in maintenance costs. A solution is to install wind Lidar devices on the turbine's nacelle. This technique is currently under development as an alternative to traditional in-situ wind anemometry because it can measure the wind vector at substantial distances upwind. However, wind Lidar systems are optimised for measuring within a fixed upwind range. But at what upwind distance should they interrogate the atmosphere?

A new, fully-flexible, wind turbine algorithm for large eddy simulations of wind farms that allows answering this question, will be presented. The new wind turbine algorithm permits a timely correction of the wind turbines' yaw misalignment with the changing wind conditions. The additional upwind scanning flexibility of the algorithm also allows to track the wind vector and turbulent kinetic energy as they approach the wind turbine's rotor blades. Results will illustrate the spatiotemporal evolution of the wind vector and the turbulent kinetic energy as the incoming flow approaches the wind turbine and under different atmospheric stability conditions. Results will also show that the available atmospheric wind power is larger during daytime periods, but at the cost of an increased variance.

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