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

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 data from 55 wind turbine power performance tests from nine 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 a 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 curvemore » suggests, using power deviation matrices leads to more accurate power prediction. We also determine 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.« less

Authors:
ORCiD logo [1];  [2];  [3];  [1]; ORCiD logo [1];  [1];  [2];  [4];  [5]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Renewable Energy Systems (United Kingdom)
  3. Univ. of Stuttgart (Germany). Stuttgart Wind Energy, Inst. of Aircraft Design and Manufacture
  4. DNV GL, Portland, OR (United States)
  5. SSE plc, Glasgow, Scotland (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1600901
Report Number(s):
[NREL/JA-5000-76102]
[Journal ID: ISSN 2366-7451]
Grant/Contract Number:  
[AC36-08GO28308]
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
[Journal Name: Wind Energy Science (Online); Journal Volume: 5; Journal Issue: 1]; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind energy; power curve; wind turbine power performance prediction methods

Citation Formats

Lee, Cheuk Yi Joseph, Stuart, Peter, Clifton, Andrew, Fields, Michael J, Perr-Sauer, Jordan, Williams, Lindy, Cameron, Lee, Geer, Taylor, and Housley, Paul. The Power Curve Working Group's assessment of wind turbine power performance prediction methods. United States: N. p., 2020. Web. doi:10.5194/wes-5-199-2020.
Lee, Cheuk Yi Joseph, Stuart, Peter, Clifton, Andrew, Fields, Michael J, Perr-Sauer, Jordan, Williams, Lindy, Cameron, Lee, Geer, Taylor, & Housley, Paul. The Power Curve Working Group's assessment of wind turbine power performance prediction methods. United States. doi:10.5194/wes-5-199-2020.
Lee, Cheuk Yi Joseph, Stuart, Peter, Clifton, Andrew, Fields, Michael J, Perr-Sauer, Jordan, Williams, Lindy, Cameron, Lee, Geer, Taylor, and Housley, Paul. Wed . "The Power Curve Working Group's assessment of wind turbine power performance prediction methods". United States. doi:10.5194/wes-5-199-2020. https://www.osti.gov/servlets/purl/1600901.
@article{osti_1600901,
title = {The Power Curve Working Group's assessment of wind turbine power performance prediction methods},
author = {Lee, Cheuk Yi Joseph and Stuart, Peter and Clifton, Andrew and Fields, Michael J and Perr-Sauer, Jordan and Williams, Lindy and Cameron, Lee and Geer, Taylor and Housley, Paul},
abstractNote = {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 data from 55 wind turbine power performance tests from nine 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 a 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 determine 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.},
doi = {10.5194/wes-5-199-2020},
journal = {Wind Energy Science (Online)},
number = [1],
volume = [5],
place = {United States},
year = {2020},
month = {2}
}

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