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Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

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  • Yaseen Ahmed Mohammed Alsumaidaee

    (College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia)

  • Chong Tak Yaw

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia)

  • Siaw Paw Koh

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
    Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia)

  • Sieh Kiong Tiong

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
    Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia)

  • Chai Phing Chen

    (Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia)

  • Kharudin Ali

    (Faculty of Electrical and Automation Engineering Technology, UC TATI, Teluk Kalong, Kemaman 24000, Terengganu, Malaysia)

Abstract

In power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems’ and components’ health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects.

Suggested Citation

  • Yaseen Ahmed Mohammed Alsumaidaee & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Kharudin Ali, 2022. "Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning," Energies, MDPI, vol. 15(18), pages 1-34, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6762-:d:916141
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    References listed on IDEAS

    as
    1. Yanxin Wang & Jing Yan & Zhou Yang & Tingliang Liu & Yiming Zhao & Junyi Li, 2019. "Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network," Energies, MDPI, vol. 12(24), pages 1-19, December.
    2. Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
    3. Sanuri Ishak & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Talal Yusaf, 2021. "Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine," Energies, MDPI, vol. 14(19), pages 1-21, October.
    4. Xiu Zhou & Xutao Wu & Pei Ding & Xiuguang Li & Ninghui He & Guozhi Zhang & Xiaoxing Zhang, 2019. "Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm," Energies, MDPI, vol. 13(1), pages 1-13, December.
    5. Jiejie Dai & Yingbing Teng & Zhaoqi Zhang & Zhongmin Yu & Gehao Sheng & Xiuchen Jiang, 2019. "Partial Discharge Data Matching Method for GIS Case-Based Reasoning," Energies, MDPI, vol. 12(19), pages 1-15, September.
    6. Lu, Shibo & Phung, B.T. & Zhang, Daming, 2018. "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 88-98.
    7. Ghulam Amjad Hussain & Ashraf A. Zaher & Detlef Hummes & Madia Safdar & Matti Lehtonen, 2020. "Hybrid Sensing of Internal and Surface Partial Discharges in Air-Insulated Medium Voltage Switchgear," Energies, MDPI, vol. 13(7), pages 1-16, April.
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    Cited by:

    1. Paweł Węgierek & Damian Kostyła & Michał Lech, 2023. "Directions of Development of Diagnostic Methods of Vacuum Medium-Voltage Switchgear," Energies, MDPI, vol. 16(5), pages 1-25, February.

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