DOAJ Open Access 2024

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 +1 lainnya

Abstrak

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.

Topik & Kata Kunci

Technology

Penulis (6)

Felipe M. Laburú

Thales W. Cabral

Felippe V. Gomes

Eduardo R. de Lima

José C. S. S. Filho

Luís G. P. Meloni

Format Sitasi

APA MLA BibTeX

Laburú, F.M., Cabral, T.W., Gomes, F.V., Lima, E.R.d., Filho, J.C.S.S., Meloni, L.G.P. (2024). New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers. https://doi.org/10.3390/en17122889

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.3390/en17122889
Akses: Open Access ✓