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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-69
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-69
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 24 Oct 2019

Submitted as: research article | 24 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

Joseph C. Y. Lee1, Peter Stuart2, Andrew Clifton3, M. Jason Fields1, Jordan Perr-Sauer4, Lindy Williams4, Lee Cameron2, Taylor Geer5, and Paul Housley6 Joseph C. Y. Lee et al.
  • 1National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
  • 2Renewable Energy Systems, Kings Langley, Hertfordshire, England, UK
  • 3Stuttgart Wind Energy, Institute of Aircraft Design and Manufacture, University of Stuttgart, Stuttgart, Germany
  • 4Computational Science Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
  • 5DNV GL, Portland, Oregon, 97204, USA
  • 6SSE plc, Glasgow, Scotland, UK

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze the data of 55 wind turbine power performance tests from 9 contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the Baseline method, which uses the conventional definition of power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the Baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also identify that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power-curve modeling and establishes the groundwork for future collaborations.

Joseph C. Y. Lee et al.
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Joseph C. Y. Lee et al.
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Latest update: 22 Nov 2019
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Short summary
This work summarizes the results of the intelligence sharing initiative of the Power Curve Working Group. Participants of 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 manuscript, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
This work summarizes the results of the intelligence sharing initiative of the Power Curve...
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