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

Lee, Joseph C. Y.; Stuart, Peter; Clifton, Andrew; Fields, M. Jason; Perr-Sauer, Jordan; Williams, Lindy; Cameron, Lee; Geer, Taylor; Housley, Paul

doi:https://doi.org/10.5194/wes-5-199-2020

Articles | Volume 5, issue 1

https://doi.org/10.5194/wes-5-199-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-5-199-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 5, issue 1

Research article

|

05 Feb 2020

Research article |

| 05 Feb 2020

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

Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley

Data sets

Power Curve Working Group Document Repository A. Clifton https://zenodo.org/communities/pcwg/

Model code and software

Power Curve Working Group Analysis Tool P. Stuart and L. Cameron https://github.com/PCWG/PCWG

Power Curve Working Group Share-3 P. Stuart and J. C. Y. Lee https://github.com/PCWG/PCWG-Share-3

Short summary

This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.