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. Wind turbine power production deviates from the referencepower curve in real-world atmospheric conditions. Correctly predictingturbine power performance requires models to be validated for a wide rangeof wind turbines using inflow in different locations. The Share-3 exerciseis the most recent intelligence-sharing exercise of the Power Curve WorkingGroup, which aims to advance the modeling of turbine performance. The goalof the exercise is to search for modeling methods that reduce error anduncertainty in power prediction when wind shear and turbulence digress fromdesign conditions. Herein, we analyze data from 55 wind turbine powerperformance tests from nine contributing organizations with statistical teststo quantify the skills of the prediction-correction methods. We assess theaccuracy and precision of four proposed trial methods against the baselinemethod, which uses the conventional definition of a power curve with windspeed and air density at hub height. The trial methods reducepower-production prediction errors compared to the baseline method at highwind speeds, which contribute heavily to power production; however, thetrial methods fail to significantly reduce prediction uncertainty in mostmeteorological conditions. For the meteorological conditions when a windturbine produces less than the power its reference power curve suggests,using power deviation matrices leads to more accurate power prediction. Wealso determine that for more than half of the submissions, the data set has alarge influence on the effectiveness of a trial method. Overall, this workaffirms the value of data-sharing efforts in advancing power curve modelingand establishes the groundwork for future collaborations.

The Power Curve Working Group's assessment of wind turbine power performance prediction methods