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New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers

Author

Listed:
  • Felipe M. Laburú

    (Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil)

  • Thales W. Cabral

    (Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil)

  • Felippe V. Gomes

    (Transmissora Aliança de Energia Elétrica S.A.—TAESA, Praça Quinze de Novembro, Centro, Rio de Janeiro 20010-010, Brazil)

  • Eduardo R. de Lima

    (Department of Hardware Design, Instituto de Pesquisa Eldorado, Campinas 13083-898, Brazil)

  • José C. S. S. Filho

    (Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil)

  • Luís G. P. Meloni

    (Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil)

Abstract

The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets.

Suggested Citation

  • Felipe M. Laburú & Thales W. Cabral & Felippe V. Gomes & Eduardo R. de Lima & José C. S. S. Filho & Luís G. P. Meloni, 2024. "New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers," Energies, MDPI, vol. 17(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2889-:d:1413557
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    References listed on IDEAS

    as
    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    Full references (including those not matched with items on IDEAS)

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