IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v351y2023ics0306261923011911.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923011911
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121827?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    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. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Wei & Wu, Yue & Huang, Xiang & Lu, Renzhi & Zhang, Hai-Tao, 2022. "A group sparse Bayesian learning algorithm for harmonic state estimation in power systems," Applied Energy, Elsevier, vol. 306(PB).
    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. Qingshan Gong & Yurong Xiong & Zhigang Jiang & Xugang Zhang & Mingmao Hu & Zhanlong Cao, 2022. "Economic, Environmental and Social Benefits Analysis of Remanufacturing Strategies for Used Products," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    4. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    5. Giani, Paolo & Tagle, Felipe & Genton, Marc G. & Castruccio, Stefano & Crippa, Paola, 2020. "Closing the gap between wind energy targets and implementation for emerging countries," Applied Energy, Elsevier, vol. 269(C).
    6. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    7. Fuquan Zhao & Fanlong Bai & Xinglong Liu & Zongwei Liu, 2022. "A Review on Renewable Energy Transition under China’s Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    8. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Li, Jiangxia & Pan, Shunqi & Chen, Yongping & Yao, Yu & Xu, Conghao, 2022. "Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas," Energy, Elsevier, vol. 260(C).
    10. Min Wang & Xiaobin Dong & Youchun Zhai, 2021. "Optimal Configuration of the Integrated Charging Station for PV and Hydrogen Storage," Energies, MDPI, vol. 14(21), pages 1-12, October.
    11. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    12. Zhuo Chen & Wei Li & Junhong Guo & Zhe Bao & Zhangrong Pan & Baodeng Hou, 2020. "Projection of Wind Energy Potential over Northern China Using a Regional Climate Model," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
    13. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    14. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    15. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    16. 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).
    17. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    18. Gao, Yang & Ma, Shaoxiu & Wang, Tao & Miao, Changhong & Yang, Fan, 2022. "Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy," Energy, Elsevier, vol. 258(C).
    19. Michal Kaczmarek & Piotr Kaczmarek & Ernest Stano, 2022. "The Effect of the Load Power Factor of the Inductive CT’s Secondary Winding on Its Distorted Current’s Harmonics Transformation Accuracy," Energies, MDPI, vol. 15(17), pages 1-11, August.
    20. Liu, Laibao & Wang, Zheng & Wang, Yang & Wang, Jun & Chang, Rui & He, Gang & Tang, Wenjun & Gao, Ziqi & Li, Jiangtao & Liu, Changyi & Zhao, Lin & Qin, Dahe & Li, Shuangcheng, 2020. "Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923011911. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.