IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8447-d970128.html
   My bibliography  Save this article

Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location

Author

Listed:
  • Bin Yang

    (State Grid Wuhan Electric Power Company, Wuhan 430077, China)

  • Zhanran Xia

    (State Grid Wuhan Electric Power Company, Wuhan 430077, China)

  • Xinyun Gao

    (State Grid Wuhan Electric Power Company, Wuhan 430077, China)

  • Jing Tu

    (State Grid Wuhan Electric Power Company, Wuhan 430077, China)

  • Hao Zhou

    (Wuhan Fujia Anda Electric Technology Co., Ltd., Wuhan 430074, China)

  • Jun Wu

    (School of Electrical Engineering, Nantong University, Nantong 226019, China)

  • Mingzhen Li

    (School of Electrical Engineering, Nantong University, Nantong 226019, China)

Abstract

In HV cable fault location technology, line parameter uncertainty has an impact on the location criterion and affects the fault location result. Therefore, it is of great significance to study the uncertainty quantification of line parameters. In this paper, an impedance-based fault location criterion was used for an uncertainty study. Three kinds of uncertainty factors, namely the sheath resistivity per unit length, the equivalent grounding resistance on both sides, and the length of the cable section, were taken as random input variables without interaction. They were subject to random uniform distribution within a 50% amplitude variation. The relevant statistical information, such as the mean value, standard deviation and probability distribution, of the normal operation and fault state were calculated using the Monte Carlo simulation (MCS) method, the polynomial chaos expansion (PCE) method, and the univariate dimension reduction method (UDRM), respectively. Thus, the influence of uncertain factors on fault location was analyzed, and the calculation results of the three uncertainty quantification methods compared. The results indicate that: (1) UQ methods are effective for simulation analysis of fault locations, and UDRM has certain application prospects for HV fault location in practice; (2) the quantification results of the MCS, PCE, and UDRM were very close, while the mean convergence rate was significantly higher for the UDRM; (3) compared with the MCS, PCE, and UDRM, the PCE and UDRM had higher accuracy, and MCS and UDRM required less running time.

Suggested Citation

  • Bin Yang & Zhanran Xia & Xinyun Gao & Jing Tu & Hao Zhou & Jun Wu & Mingzhen Li, 2022. "Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location," Energies, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8447-:d:970128
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8447/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8447/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dashti, Rahman & Ghasemi, Mohsen & Daisy, Mohammad, 2018. "Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model," Energy, Elsevier, vol. 159(C), pages 344-360.
    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. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    2. Mingzhen Li & Jialong Bu & Yupeng Song & Zhongyi Pu & Yuli Wang & Cheng Xie, 2021. "A Novel Fault Location Method for Power Cables Based on an Unsupervised Learning Algorithm," Energies, MDPI, vol. 14(4), pages 1-19, February.
    3. Abdul Haleem Medattil Ibrahim & Madhu Sharma & Vetrivel Subramaniam Rajkumar, 2023. "Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network," Energies, MDPI, vol. 16(9), pages 1-42, April.
    4. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    5. Farshchian, Ghazaleh & Darestani, Soroush Avakh & Hamidi, Naser, 2021. "Developing a decision-making dashboard for power losses attributes of Iran’s electricity distribution network," Energy, Elsevier, vol. 216(C).
    6. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    7. Ehsan Gord & Rahman Dashti & Mojtaba Najafi & Hamid Reza Shaker, 2019. "Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis," Energies, MDPI, vol. 12(6), pages 1-29, March.

    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:gam:jeners:v:15:y:2022:i:22:p:8447-:d:970128. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.