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Measurement and statistical analysis of wind speed intermittency

Author

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  • Ren, Guorui
  • Liu, Jinfu
  • Wan, Jie
  • Guo, Yufeng
  • Yu, Daren
  • Liu, Jizhen

Abstract

Developing the description and gaining an understanding of wind characteristics are important research tasks in wind engineering. Intermittency is one of the most important characteristics of wind. However, little literature is available on its quantitative measurement. In this study, wind speed intermittency is defined from the perspective of the physical nature of wind. According to this definition, the duty ratio of wind speed ramp (DRWSR) is proposed to quantitatively measure wind speed intermittency. Based on the measurement of wind speed intermittency, quantitative insights into intermittency can be obtained by analyzing the time series of the DRWSR, e.g., the intensity and variation pattern of wind speed intermittency in a day, which contributes to better understanding of wind characteristics.

Suggested Citation

  • Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren & Liu, Jizhen, 2017. "Measurement and statistical analysis of wind speed intermittency," Energy, Elsevier, vol. 118(C), pages 632-643.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:632-643
    DOI: 10.1016/j.energy.2016.10.096
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    References listed on IDEAS

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    Cited by:

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    2. Troon, Benedict, 2021. "Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique," AfricArxiv bhuv2, Center for Open Science.
    3. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    4. Ren, Guorui & Liu, Jinfu & Wan, Jie & Li, Fei & Guo, Yufeng & Yu, Daren, 2018. "The analysis of turbulence intensity based on wind speed data in onshore wind farms," Renewable Energy, Elsevier, vol. 123(C), pages 756-766.
    5. Han, Chanok & Vinel, Alexander, 2022. "Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization," Energy, Elsevier, vol. 239(PB).
    6. Daniel Suchet & Adrien Jeantet & Thomas Elghozi & Zacharie Jehl, 2020. "Defining and Quantifying Intermittency in the Power Sector," Energies, MDPI, vol. 13(13), pages 1-12, July.
    7. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    8. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    9. Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
    10. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    11. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.

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