IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3701-d1132975.html
   My bibliography  Save this article

Variability in the Wind Spectrum between 10 −2 Hz and 1 Hz

Author

Listed:
  • Neil Garcia

    (Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA)

  • Biswaranjan Mohanty

    (Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA)

  • Kim A. Stelson

    (Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA)

Abstract

Wind is an abundant, yet intermittent, source of renewable energy, with speeds changing both spatially and temporally over a wide range of time scales. While wind variability is well documented on large meteorological time scales and the behavior of turbulent flow at high frequencies is well understood, there remain questions in the literature regarding the intermediate region of these domains. Understanding wind variability at the microscale, here considering a frequency range of 10 −2 Hz < f < 1 Hz, is key for wind turbine control and modeling. In this paper, we quantify the variability of wind conditions for the meteorological tower at the Eolos wind research station in Minnesota using power spectral density analysis. Spectral analysis of wind samples with similar mean wind speeds was conducted to test the hypothesis that the wind spectrum’s shape is independent of the mean wind speed. Historical wind speed data were compared and evaluated to identify diurnal, seasonal, and interannual trends in the spectrum of wind at frequencies above 10 −3 Hz. We conclude that the shape of the wind spectrum is independent of the mean wind speed following the Kolmogorov −5/3 law for turbulent flows for incoming wind, with some variations in slope and spectrum magnitude. While no conclusive diurnal, seasonal, or interannual trends were observed, it is shown that some variations in both slope and spectrum magnitude can occur on these time scales.

Suggested Citation

  • Neil Garcia & Biswaranjan Mohanty & Kim A. Stelson, 2023. "Variability in the Wind Spectrum between 10 −2 Hz and 1 Hz," Energies, MDPI, vol. 16(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3701-:d:1132975
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3701/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3701/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Biswaranjan Mohanty & Kim A. Stelson, 2022. "Experimental Validation of a Hydrostatic Transmission for Community Wind Turbines," Energies, MDPI, vol. 15(1), pages 1-15, January.
    2. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
    2. Biswaranjan Mohanty & Kim A. Stelson, 2022. "Dynamics and Control of an Energy-Efficient, Power-Regenerative, Hydrostatic Wind Turbine Dynamometer," Energies, MDPI, vol. 15(8), pages 1-16, April.
    3. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    4. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
    5. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).
    6. Wang, H. & Ke, S.T. & Wang, T.G. & Kareem, A. & Hu, L. & Ge, Y.J., 2022. "Multi-stage typhoon-induced wind effects on offshore wind turbines using a data-driven wind speed field model," Renewable Energy, Elsevier, vol. 188(C), pages 765-777.
    7. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.
    8. Yunliang Li & Zhaobin Li & Zhideng Zhou & Xiaolei Yang, 2023. "Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    9. Xiaohao Liu & Zhaobin Li & Xiaolei Yang & Duo Xu & Seokkoo Kang & Ali Khosronejad, 2022. "Large-Eddy Simulation of Wakes of Waked Wind Turbines," Energies, MDPI, vol. 15(8), pages 1-26, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3701-:d:1132975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.