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Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies

Author

Listed:
  • Shiyu Liu

    (The State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shanxi, China)

  • Gengfeng Li

    (The State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shanxi, China)

  • Haipeng Xie

    (The State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shanxi, China)

  • Xifan Wang

    (The State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shanxi, China)

Abstract

As the scale of wind power bases rises, it becomes significant in power system planning and operation to provide detailed correlation characteristic of wind speed in different geographical hierarchies, that is among wind turbines, within a wind farm and its regional wind turbines, and among different wind farms. A new approach to analyze the correlation characteristics of wind speed in different geographical hierarchies is proposed in this paper. In the proposed approach, either linear or nonlinear correlation of wind speed in each geographical hierarchy is firstly identified. Then joint sectionalized wind speed probability distribution is modeled for linear correlation analysis while a Copula function is adopted in nonlinear correlation analysis. By this approach, temporal-geographical correlations of wind speed in different geographical hierarchies are properly revealed. Results of case studies based on Jiuquan Wind Power Base in China are analyzed in each geographical hierarchy, which illustrates the feasibility of the proposed approach.

Suggested Citation

  • Shiyu Liu & Gengfeng Li & Haipeng Xie & Xifan Wang, 2017. "Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies," Energies, MDPI, vol. 10(2), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:237-:d:90559
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    References listed on IDEAS

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    Cited by:

    1. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
    2. Kai Ma & Shubing Hu & Jie Yang & Chunxia Dou & Josep M. Guerrero, 2017. "Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach," Energies, MDPI, vol. 10(5), pages 1-16, May.
    3. Eryilmaz, Serkan & Kan, Cihangir, 2020. "Reliability based modeling and analysis for a wind power system integrated by two wind farms considering wind speed dependence," Reliability Engineering and System Safety, Elsevier, vol. 203(C).

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