Modified Power Curve for Prediction of Wind Turbine Power

Typically, the wind turbine power curve simply relates hub height wind speed and power output by fitting a curve based on ten-minute averaged values of the two parameters. The relationship is used for wind energy assessment and for forecasting output at operating wind farms. There is evidence that only relating power output to hub height wind speed yields inaccuracy in power prediction. Clifton et al. (2013) showed in their work that power production can deviate 5-10% compared to manufacturer’s power curve predictions and attributed the error to not accounting for turbulence intensity. Also, the difference between turbine orientation and incoming wind velocity (yaw error) results in decreased power output and needs to be considered for a more accurate power prediction. As wind turbines are being installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in more variability of the inflow must be considered.

In this work we investigate an approach to include turbulence intensity and yaw error in the prediction of turbine power by using high resolution wind measurements. An adjusted power curve model will be evaluated and compared to the standard operating curve for a 2.5 MW Clipper wind turbine. One second SCADA data along with wind speed measurements from the nacelle of the turbine using a 2D sonic anemometer are used in the model. The results show that turbulence intensity has a systematic effect on the accuracy of power prediction while the effect of yaw error may be incorporated using an adjusted inflow wind speed.

References

Clifton, A., Kilcher, L., Lundquist, J., & Fleming, P. (2013). Using machine learning to predict wind turbine power output. Environmental research letters, 8(2), 024009.