Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model
This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/15/9/561 |
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