DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

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. https://doi.org/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. https://doi.org/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 = {Wed Feb 05 00:00:00 EST 2020},
month = {Wed Feb 05 00:00:00 EST 2020}
}

Works referenced in this record:

Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring
journal, October 2018


SciPy 1.0: fundamental algorithms for scientific computing in Python
journal, February 2020


Power curve measurement with a nacelle mounted lidar: Power curve measurement with a nacelle lidar
journal, June 2013

  • Wagner, R.; Pedersen, T. F.; Courtney, M.
  • Wind Energy
  • DOI: 10.1002/we.1643

Nacelle power curve measurement with spinner anemometer and uncertainty evaluation
journal, January 2017

  • Demurtas, Giorgio; Friis Pedersen, Troels; Wagner, Rozenn
  • Wind Energy Science, Vol. 2, Issue 1
  • DOI: 10.5194/wes-2-97-2017

Influence of atmospheric stability on wind turbine loads: Atmospheric stability and loads
journal, July 2012

  • Sathe, A.; Mann, J.; Barlas, T.
  • Wind Energy, Vol. 16, Issue 7
  • DOI: 10.1002/we.1528

Atmospheric stability affects wind turbine power collection
journal, January 2012


Using machine learning to predict wind turbine power output
journal, April 2013


Robust Tests for the Equality of Variances
journal, June 1974


Time Adaptive Conditional Kernel Density Estimation for Wind Power Forecasting
journal, October 2012

  • Bessa, Ricardo J.; Miranda, Vladimiro; Botterud, Audun
  • IEEE Transactions on Sustainable Energy, Vol. 3, Issue 4
  • DOI: 10.1109/TSTE.2012.2200302

Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring
conference, September 2018

  • Pandit, Ravi; Infield, David
  • 2018 53rd International Universities Power Engineering Conference (UPEC)
  • DOI: 10.1109/UPEC.2018.8542057

Performance Test of a 3MW Wind Turbine – Effects of Shear and Turbulence
journal, January 2015


Influence of Atmospheric Stability on Wind Turbine Power Performance Curves
journal, February 2006

  • Sumner, Jonathon; Masson, Christian
  • Journal of Solar Energy Engineering, Vol. 128, Issue 4
  • DOI: 10.1115/1.2347714

On “Field Significance” and the False Discovery Rate
journal, September 2006

  • Wilks, D. S.
  • Journal of Applied Meteorology and Climatology, Vol. 45, Issue 9
  • DOI: 10.1175/JAM2404.1

Monotone Piecewise Cubic Interpolation
journal, April 1980

  • Fritsch, F. N.; Carlson, R. E.
  • SIAM Journal on Numerical Analysis, Vol. 17, Issue 2
  • DOI: 10.1137/0717021

The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production
journal, September 2019


The Impact of Levene’s Test of Equality of Variances on Statistical Theory and Practice
journal, August 2009

  • Gastwirth, Joseph L.; Gel, Yulia R.; Miao, Weiwen
  • Statistical Science, Vol. 24, Issue 3
  • DOI: 10.1214/09-STS301

A kernel plus method for quantifying wind turbine performance upgrades: Kernel plus method
journal, April 2014

  • Lee, Giwhyun; Ding, Yu; Xie, Le
  • Wind Energy, Vol. 18, Issue 7
  • DOI: 10.1002/we.1755

Modeling wind-turbine power curve: A data partitioning and mining approach
journal, March 2017


Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy
journal, October 2018

  • Pandit, Ravi Kumar; Infield, David; Carroll, James
  • Wind Energy, Vol. 22, Issue 2
  • DOI: 10.1002/we.2285

Using Conditional Kernel Density Estimation for Wind Power Density Forecasting
journal, March 2012


Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms
journal, January 2015

  • Lee, Giwhyun; Ding, Yu; Genton, Marc G.
  • Journal of the American Statistical Association, Vol. 110, Issue 509
  • DOI: 10.1080/01621459.2014.977385

Influence of turbulence intensity on wind turbine power curves
journal, October 2017


Comparison of different measurement methods for a nacelle-based lidar power curve
journal, June 2018


A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems
journal, January 2016

  • Sohoni, Vaishali; Gupta, S. C.; Nema, R. K.
  • Journal of Energy, Vol. 2016
  • DOI: 10.1155/2016/8519785

Wind turbine power curve modelling using artificial neural network
journal, April 2016


Power Performance Measurements of the NREL CART-2 Wind Turbine Using a Nacelle-Based Lidar Scanner
journal, October 2014

  • Rettenmeier, Andreas; Schlipf, David; Würth, Ines
  • Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 10
  • DOI: 10.1175/JTECH-D-13-00154.1

Wind power curve modeling in complex terrain using statistical models
journal, January 2015

  • Bulaevskaya, V.; Wharton, S.; Clifton, A.
  • Journal of Renewable and Sustainable Energy, Vol. 7, Issue 1
  • DOI: 10.1063/1.4904430