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Deep Learning in High Voltage Engineering: A Literature Review

Author

Listed:
  • Sara Mantach

    (Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Abdulla Lutfi

    (Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Hamed Moradi Tavasani

    (Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Ahmed Ashraf

    (Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Ayman El-Hag

    (Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Behzad Kordi

    (Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

Abstract

Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

Suggested Citation

  • Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5005-:d:858863
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    References listed on IDEAS

    as
    1. ZhenHua Li & Yujie Zhang & Ahmed Abu-Siada & Xingxin Chen & Zhenxing Li & Yanchun Xu & Lei Zhang & Yue Tong, 2021. "Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network," Energies, MDPI, vol. 14(6), pages 1-14, March.
    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. Vo-Nguyen Tuyet-Doan & Tien-Tung Nguyen & Minh-Tuan Nguyen & Jong-Ho Lee & Yong-Hwa Kim, 2020. "Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 13(8), pages 1-16, April.
    4. Benjamin Adam & Stefan Tenbohlen, 2021. "Classification of Superimposed Partial Discharge Patterns," Energies, MDPI, vol. 14(8), pages 1-10, April.
    5. 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.
    6. 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.
    7. Zhe Li & Yongpeng Xu & Xiuchen Jiang, 2020. "Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN," Energies, MDPI, vol. 13(17), pages 1-12, September.
    8. Ning Liu & Bo Fan & Xianyong Xiao & Xiaomei Yang, 2019. "Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    9. 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.
    10. Yichen Zhou & Xiaohui Yang & Lingyu Tao & Li Yang, 2021. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network," Energies, MDPI, vol. 14(11), pages 1-21, May.
    11. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    12. Sara Mantach & Ahmed Ashraf & Hamed Janani & Behzad Kordi, 2021. "A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set," Energies, MDPI, vol. 14(5), pages 1-16, March.
    13. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
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    Cited by:

    1. Ana-Maria Moldovan & Mircea Ion Buzdugan, 2023. "Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network," Sustainability, MDPI, vol. 15(7), pages 1-19, April.

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