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Online SSO stability analysis-based oscillation parameter estimation in converter-tied grids

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  • Chen, Lei
  • Xie, Xiaorong
  • Li, Xiang
  • Yang, Lei
  • Cao, Xin

Abstract

Due to the wide frequency range of sub-synchronous oscillations (SSOs) in converter-tied grids, existing SSO parameter estimators generally use a long window to pre-identify the SSO frequency, and thus generally have slow responses in SSO parameter estimation. To deal with this problem, this paper proposes an online SSO stability analysis-based SSO parameter estimator. The operating condition of the grid is updated online by measuring the fundamental voltage/current phasor. Then the impedance of the converter is updated based on the identified operating condition, and the SSO stability is analyzed online. If an unstable SSO mode is detected, the identified SSO frequency is used to build the imaginary exponential function-based signal model, and the SSO parameters are estimated based on the least square method. Case studies show that the proposed method has a much faster response than the methods recently published in the literature, and can obtain accurate SSO parameter estimates.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923011911
    DOI: 10.1016/j.apenergy.2023.121827
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    References listed on IDEAS

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    1. Yang, Ting & Pen, Haibo & Wang, Dan & Wang, Zhaoxia, 2016. "Harmonic analysis in integrated energy system based on compressed sensing," Applied Energy, Elsevier, vol. 165(C), pages 583-591.
    2. 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).
    3. 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).
    4. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. 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.
    6. Han, Jiangbei & Liu, Chengxi, 2023. "Performance evaluation of SSCI damping controller based on the elastic energy equivalent system," Applied Energy, Elsevier, vol. 331(C).
    7. Jin, Tao & Liu, Siyi & Flesch, Rodolfo C.C. & Su, Wencong, 2017. "A method for the identification of low frequency oscillation modes in power systems subjected to noise," Applied Energy, Elsevier, vol. 206(C), pages 1379-1392.
    Full references (including those not matched with items on IDEAS)

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