Using Machine Learning to Create Turbine Performance Models (Presentation)

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Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For ... continued below

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17 p.

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Clifton, A. April 1, 2013.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.

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17 p.

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  • Presented at the Power Curve Working Group, 12 March 2013, Brande, Denmark; Related Information: NREL (National Renewable Energy Laboratory)

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  • Report No.: NREL/PR-5000-58314
  • Grant Number: AC36-08GO28308
  • Office of Scientific & Technical Information Report Number: 1079091
  • Archival Resource Key: ark:/67531/metadc844960

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  • April 1, 2013

Added to The UNT Digital Library

  • May 19, 2016, 9:45 a.m.

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  • April 3, 2017, 8:45 p.m.

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Clifton, A. Using Machine Learning to Create Turbine Performance Models (Presentation), article, April 1, 2013; Golden, Colorado. (digital.library.unt.edu/ark:/67531/metadc844960/: accessed August 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.