Abstract
Wind turbines require methods to predict the power produced as inflow conditions change. We compare the standard method of binning with a turbulence renormalization method and a machine learning approach using a data set derived from simulations. The method of binning is unable to cope with changes in turbulence; the turbulence renormalization method cannot account for changes in shear other than by using the the equivalent wind speed, which is derived from wind speed data at multiple heights in the rotor disk. The machine learning method is best able to predict the power as conditions change, and could be modified to include additional inflow variables such as veer or yaw error.
Highlights
IntroductionSites in the U.S Midwest experience a large diurnal cycle of wind speed and turbulence, and offshore wind turbines in the North Sea in northern Europe experience severe gales and strong seasonal variation
Wind turbines operate in a wide variety of wind climates
The method of binning is unable to cope with changes in turbulence; the turbulence renormalization method cannot account for changes in shear other than by using the the equivalent wind speed, which is derived from wind speed data at multiple heights in the rotor disk
Summary
Sites in the U.S Midwest experience a large diurnal cycle of wind speed and turbulence, and offshore wind turbines in the North Sea in northern Europe experience severe gales and strong seasonal variation. Turbines at these sites, were likely developed and tested at other sites, with different wind conditions. Recent research and industry experience has shown that the power output of wind turbines can vary dramatically at the same 10-minute average wind speed, as turbulence and other inflow characteristics change. Several methods have recently been developed to understand and predict the power of a wind turbine under a range of inflow conditions.
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