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Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Author

Listed:
  • Sonia Barrios

    (Ormazabal Corporate Technology, 48340 Amorebieta, Spain)

  • David Buldain

    (Department of Electronic Engineering and Communications, University of Zaragoza, 50018 Zaragoza, Spain)

  • María Paz Comech

    (Instituto CIRCE (Universidad de Zaragoza—Fundación CIRCE), 50018 Zaragoza, Spain)

  • Ian Gilbert

    (Ormazabal Corporate Technology, 48340 Amorebieta, Spain)

  • Iñaki Orue

    (Ormazabal Corporate Technology, 48340 Amorebieta, Spain)

Abstract

This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

Suggested Citation

  • Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2485-:d:243641
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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. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
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    Cited by:

    1. Jun Qiang Chan & Wong Jee Keen Raymond & Hazlee Azil Illias & Mohamadariff Othman, 2023. "Partial Discharge Localization Techniques: A Review of Recent Progress," Energies, MDPI, vol. 16(6), pages 1-31, March.
    2. Andrey A. Radionov & Ivan V. Liubimov & Igor M. Yachikov & Ildar R. Abdulveleev & Ekaterina A. Khramshina & Alexander S. Karandaev, 2023. "Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data," Energies, MDPI, vol. 16(12), pages 1-27, June.
    3. Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    4. Tomasz Boczar & Sebastian Borucki & Daniel Jancarczyk & Marcin Bernas & Pawel Kurtasz, 2022. "Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique," Energies, MDPI, vol. 15(14), pages 1-13, July.
    5. Sinda Kaziz & Mohamed Hadj Said & Antonino Imburgia & Bilel Maamer & Denis Flandre & Pietro Romano & Fares Tounsi, 2023. "Radiometric Partial Discharge Detection: A Review," Energies, MDPI, vol. 16(4), pages 1-33, February.

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