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Grey box aggregation modeling of wind farm for wideband oscillations analysis

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  • Zong, Haoxiang
  • Lyu, Jing
  • Wang, Xiao
  • Zhang, Chen
  • Zhang, Ruifang
  • Cai, Xu

Abstract

The power electronic converter-based wind farms have imposed great risks on the small-signal stability of the modern power system, mainly characterized by wideband oscillations ranging from one Hertz to several thousand Hertz. To analyze such wideband oscillations problems, a consensus has been reached that the aggregation of the wind farm is indispensable. However, most of the present aggregation studies are focused on the time-domain consistency (e.g., output active/reactive power) before and after aggregation, while the frequency-domain consistency, closely associated with the accuracy of wideband oscillations analysis, has not been paid much attention. To fulfill this gap, in this paper, a wideband grey-box aggregation model of the wind farm with its parameter identification method are presented, which can be acquired without knowing the details of the wind farm. The identified aggregation model shows a good conformity with the large scope frequency characteristics (e.g., 1–2500 Hz) of the detailed wind farm, suitable for the wideband oscillation analysis. Firstly, the trajectory sensitivity and wideband characteristics of the wind farm are studied, based on which a reduced-order grey-box aggregation model is established in low-medium and high frequency band, respectively. Secondly, a vector fitting-based parameter identification method is presented for the established grey-box aggregation model. At last, the influences of the wind speed distribution and transmission line length on the accuracy of the proposed aggregation method are studied, where a comprehensive deviation analysis is carried out statistically.

Suggested Citation

  • Zong, Haoxiang & Lyu, Jing & Wang, Xiao & Zhang, Chen & Zhang, Ruifang & Cai, Xu, 2021. "Grey box aggregation modeling of wind farm for wideband oscillations analysis," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920314732
    DOI: 10.1016/j.apenergy.2020.116035
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    References listed on IDEAS

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    1. Xia, S.W. & Bu, S.Q. & Zhang, X. & Xu, Y. & Zhou, B. & Zhu, J.B., 2018. "Model reduction strategy of doubly-fed induction generator-based wind farms for power system small-signal rotor angle stability analysis," Applied Energy, Elsevier, vol. 222(C), pages 608-620.
    2. Naemi, Mostafa & Brear, Michael J., 2020. "A hierarchical, physical and data-driven approach to wind farm modelling," Renewable Energy, Elsevier, vol. 162(C), pages 1195-1207.
    3. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
    4. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    5. Zhang, Jian & Cui, Mingjian & He, Yigang, 2020. "Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data," Applied Energy, Elsevier, vol. 261(C).
    6. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    7. Fu, Xiaopeng & Wang, Chengshan & Li, Peng & Wang, Liwei, 2019. "Exponential integration algorithm for large-scale wind farm simulation with Krylov subspace acceleration," Applied Energy, Elsevier, vol. 254(C).
    8. Zou, Jianxiao & Peng, Chao & Yan, Yan & Zheng, Hong & Li, Yan, 2014. "A survey of dynamic equivalent modeling for wind farm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 956-963.
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    Cited by:

    1. Chen, Lei & Xie, Xiaorong & He, Jingbo & Xu, Tao & Xu, Dechao & Ma, Ningning, 2023. "Wideband oscillation monitoring in power systems with high-penetration of renewable energy sources and power electronics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Hu, Yong & Bu, Siqi & Luo, Jianqiang & Wen, Jiaxin, 2023. "Generalization of oscillation loop and energy flow analysis for investigating various oscillations of renewable energy systems," Renewable Energy, Elsevier, vol. 218(C).
    3. Chen, Lei & Xie, Xiaorong & Li, Xiang & Yang, Lei & Cao, Xin, 2023. "Online SSO stability analysis-based oscillation parameter estimation in converter-tied grids," Applied Energy, Elsevier, vol. 351(C).

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