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A novel fault diagnosis method for battery energy storage station based on differential current

Author

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  • Li, Chao
  • Zeng, Kaidi
  • Li, Guanzheng
  • Chen, Peiyu
  • Li, Bin

Abstract

Nowadays, an increasing number of battery energy storage station (BESS) is constructed to support the power grid with high penetration of renewable energy sources. However, many accidents occurred in BESSs threaten the development of the BESS, so it is important to develop a protection method for the BESS. In this work, a novel fault diagnosis method based on differential current is proposed, which can identify the short circuit fault rapidly and effectively. Firstly, in order to simulate the short circuit fault characteristic of a BESS, a linear varying parameter battery equivalent circuit model (ECM) which can demonstrate the short circuit current are established based on the manta ray foraging optimization (MRFO) algorithm. Secondly, the fault diagnosis method based on differential current is proposed and analyzed through the calculation of short circuit current (SCC) in BESS. Finally, different working state data of battery are used to verify the fault diagnosis method. The results show that the proposed method can effectively diagnose the short circuit fault. In summary, the proposed fault diagnosis method based on differential current is efficient in protecting the BESS.

Suggested Citation

  • Li, Chao & Zeng, Kaidi & Li, Guanzheng & Chen, Peiyu & Li, Bin, 2023. "A novel fault diagnosis method for battery energy storage station based on differential current," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s030626192301334x
    DOI: 10.1016/j.apenergy.2023.121970
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    References listed on IDEAS

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    1. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    2. Xiangdong Sun & Jingrun Ji & Biying Ren & Chenxue Xie & Dan Yan, 2019. "Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery," Energies, MDPI, vol. 12(12), pages 1-15, June.
    3. Mejia, Cristian & Kajikawa, Yuya, 2020. "Emerging topics in energy storage based on a large-scale analysis of academic articles and patents," Applied Energy, Elsevier, vol. 263(C).
    4. Killer, Marvin & Farrokhseresht, Mana & Paterakis, Nikolaos G., 2020. "Implementation of large-scale Li-ion battery energy storage systems within the EMEA region," Applied Energy, Elsevier, vol. 260(C).
    5. Kebede, Abraham Alem & Kalogiannis, Theodoros & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Jiang, Lulu & Deng, Zhongwei & Tang, Xiaolin & Hu, Lin & Lin, Xianke & Hu, Xiaosong, 2021. "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

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