NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data

Hamilton, Nicholas

doi:10.21947/1775531

Title: NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data

Dataset
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Abstract

This dataset is intended to be a public resource for anyone conducting wind turbine noise research. It contains noise spectra and equivalent sound pressure levels at 11 measurement stations. The dataset also contains wind turbine operational data and meteorological data from a met mast directly upstream of the wind turbine.

Authors:

Hamilton, Nicholas

National Renewable Energy Laboratory

Publication Date:: Mon Jun 30 00:00:00 EDT 2025

DOE Contract Number:: AC05-76RL01830

Research Org.:: Pacific Northwest National Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

Subject:: 17 WIND ENERGY

OSTI Identifier:: 1775531

DOI:: https://doi.org/10.21947/1775531

Citation Formats


                    Hamilton, Nicholas. NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data.  United States: N. p., 2025. 
        Web.  doi:10.21947/1775531.

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                    Hamilton, Nicholas. NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data.  United States.  doi:https://doi.org/10.21947/1775531

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                    Hamilton, Nicholas. 2025.  
"NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data".  United States.  doi:https://doi.org/10.21947/1775531.  https://www.osti.gov/servlets/purl/1775531. Pub date:Mon Jun 30 00:00:00 EDT 2025

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@article{osti_1775531,

  title        = {NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data},

  author       = {Hamilton, Nicholas},

  abstractNote = {This dataset is intended to be a public resource for anyone conducting wind turbine noise research. It contains noise spectra and equivalent sound pressure levels at 11 measurement stations. The dataset also contains wind turbine operational data and meteorological data from a met mast directly upstream of the wind turbine.},

  doi          = {10.21947/1775531},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Jun 30 00:00:00 EDT 2025},

  month        = {Mon Jun 30 00:00:00 EDT 2025}

}

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DOI: https://doi.org/10.21947/1775531

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