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Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Author

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  • Sanuri Ishak

    (Department of Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

  • Chong Tak Yaw

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

  • Siaw Paw Koh

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

  • Sieh Kiong Tiong

    (Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

  • Chai Phing Chen

    (Department of Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

  • Talal Yusaf

    (School of Engineering and Technology, Central Queensland University, Brisbane, OLD 4009, Australia)

Abstract

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6279-:d:648694
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    References listed on IDEAS

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    1. 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.
    2. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    3. 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.
    4. Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
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