Effect of winds in a mountain pass on turbine performance

Authors

  • A. Clifton,

    Corresponding author
    1. National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401-3305, USA
    2. WSL Institute for Snow and Avalanche Research, SLF, Switzerland
    • Correspondence: Andrew Clifton, National Wind Technology Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401-3305, USA.

      E-mail: andrew.clifton@nrel.gov

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  • M. H. Daniels,

    1. Environmental Fluid Dynamics Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole polytechnique fédérale de Lausanne EPFL, CH-1015 Lausanne, Switzerland
    2. Currently unaffiliated
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  • M. Lehning

    1. WSL Institute for Snow and Avalanche Research, SLF, Switzerland
    2. Laboratory of Cryospheric Sciences, School of Architecture, Civil and Environmental Engineering, Ecole polytechnique fédérale de Lausanne EPFL, CH-1015 Lausanne, Switzerland
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ABSTRACT

Mountain passes are potentially advantageous sites for the deployment of wind turbines because of road links and electrical transmission infrastructure. However, relatively little is known about wind characteristics and turbine response in these environments. Using hub height wind data from a mountain pass in Switzerland, this paper discusses the causes of the observed pass winds and how a generic wind turbine might perform in those conditions. During 3 months of winter measurements, the winds in the pass showed signatures of forcing by regional pressure gradients rather than local cooling or heating. Turbulence intensity was often less than 10%, and the magnitude of the wind shear power law exponent was less than 0.1. To understand the impact of pass winds on a wind turbine, we simulated a Wind Partnership for Advanced Component Technologies 1.5 MW wind turbine using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) aeroelastic simulator , forced by artificial wind fields of varying turbulence intensity and shear generated by the turbulence simulator TurbSim. We used the turbine simulation data to train a regression model that is used to predict the turbine response to the pass wind time series. Results showed that depending on long-term wind characteristics, wind turbines in the pass may perform differently than predicted using a power curve derived from test measurements at another location. This method of generating site-specific energy capture predictions could be combined with long-term wind resource data and specific turbine models to better predict the energy production and turbine loads at this, or any other site. Copyright © 2013 John Wiley & Sons, Ltd.

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