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Locating high-impedance faults in DC microgrid clusters using support vector machines

Author

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  • Bayati, Navid
  • Balouji, Ebrahim
  • Baghaee, Hamid Reza
  • Hajizadeh, Amin
  • Soltani, Mohsen
  • Lin, Zhengyu
  • Savaghebi, Mehdi

Abstract

With the increasing number of DC microgrids, DC microgrid clusters are emerging as a cost-effective solution. Therefore, due to the possible long distances between DC microgrids, once a fault occurs and is cleared, it should be located. Especially, locating high impedance faults (HIFs) is challenging. With communication-free fault locating methods, implementation costs can be reduced, and noise and delay of communication can be eliminated. In this paper, a novel localized fault location method using support vector machines (SVMs) is proposed for DC microgrid clusters. The purpose of this study is to facilitate the post fault conditions by locating the accurate place of the faults, even the challenging HIFs, by using the local measurements at one end of each line. The proposed scheme applies the faults, and fault features generated experimentally to the SVM, which is trained in Python for determining the fault location. The experimental test results prove that the proposed scheme is immune against disturbances, such as noise and bad calibration, and can efficiently and reliably estimate the location and resistance of faults with high accuracy.

Suggested Citation

  • Bayati, Navid & Balouji, Ebrahim & Baghaee, Hamid Reza & Hajizadeh, Amin & Soltani, Mohsen & Lin, Zhengyu & Savaghebi, Mehdi, 2022. "Locating high-impedance faults in DC microgrid clusters using support vector machines," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015889
    DOI: 10.1016/j.apenergy.2021.118338
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    Citations

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    Cited by:

    1. Wang, Ting & Zhang, Chunyan & Hao, Zhiguo & Monti, Antonello & Ponci, Ferdinanda, 2023. "Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach," Applied Energy, Elsevier, vol. 336(C).
    2. Mehdi Moradian & Tek Tjing Lie & Kosala Gunawardane, 2023. "DC Circuit Breaker Evolution, Design, and Analysis," Energies, MDPI, vol. 16(17), pages 1-16, August.
    3. Sun, Chenhao & Xu, Hao & Zeng, Xiangjun & Wang, Wen & Jiang, Fei & Yang, Xin, 2023. "A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification," Applied Energy, Elsevier, vol. 344(C).
    4. Dong Yu & Shan Gao & Xin Zhao & Yu Liu & Sicheng Wang & Tiancheng E. Song, 2023. "Alternating Iterative Power-Flow Algorithm for Hybrid AC–DC Power Grids Incorporating LCCs and VSCs," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
    5. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).

    More about this item

    Keywords

    DC Microgrid; SVM; Fault; Clusters;
    All these keywords.

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