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

Felipe M. Laburú, Thales W. Cabral, Felippe V. Gomes, Eduardo R. de Lima, José C. S. S. Filho and Luís G. P. Meloni ()
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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

Energies, 2024, vol. 17, issue 12, 1-20

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.

Keywords: power transformers; DGA sensoring; fault diagnosis; dissolved gas analysis; evaluation metrics; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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