Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning
This study addresses the challenge of imbalanced dissolved gas analysis (DGA) data in transformer failure classification by assessing the impact of data-level balancing techniques on machine learning performance. Five data-level strategies - Random Under-Sampling (RUS), Edited Nearest Neighbors (ENN...
| Published in: | ENERGY REPORTS |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
ELSEVIER
2025
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| Subjects: | |
| Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386454100001 |