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An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods

Author

Listed:
  • Lidong Zhang

    (School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Qikai Li

    (School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yuanjun Guo

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Lei Zhang

    (China Datang Corporation Renewable Science and Technology Research Institute, Beijing 10052, China)

Abstract

Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction characteristics. For this purpose, the angular–linear model for approximating wind direction and speed characteristics were adopted and constructed with specified marginal distributions. Specifically, Weibull–Weibull distribution, lognormal–lognormal distribution and Weibull–lognormal distribution were applied to represent the marginal distribution of wind speed. Moreover, the finite mixture of von Mises function (FVMF) model was used to investigate the marginal distribution of wind direction. The parameters of those models were estimated by the expectation–maximum method. The optimal model was obtained by comparing the coefficient of determination value ( R 2 ) and Akaike’s information criteria (AIC). In the numerical study, wind data measured at a featured wind farm in north China was adopted. Results showed that the proposed joint distribution function could accurately represent the actual wind data at different heights, with the coefficient of determination value ( R 2 ) of 0.99.

Suggested Citation

  • Lidong Zhang & Qikai Li & Yuanjun Guo & Zhile Yang & Lei Zhang, 2018. "An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4338-:d:184625
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    References listed on IDEAS

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    Cited by:

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    2. Krzysztof Wrobel & Krzysztof Tomczewski & Artur Sliwinski & Andrzej Tomczewski, 2021. "Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution," Energies, MDPI, vol. 14(6), pages 1-18, March.
    3. Sara Salvador & Riccardo Gatto, 2022. "Bayesian tests of symmetry for the generalized Von Mises distribution," Computational Statistics, Springer, vol. 37(2), pages 947-974, April.
    4. Mohammad Arashi & Priyanka Nagar & Andriette Bekker, 2020. "Joint Probabilistic Modeling of Wind Speed and Wind Direction for Wind Energy Analysis: A Case Study in Humansdorp and Noupoort," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    5. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    6. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.

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