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 |
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| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2025
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211464729&doi=10.1016%2fj.egyr.2024.12.006&partnerID=40&md5=a2ce560b66c09b702e817d88325bd90e |