Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM
Abstract This paper proposes a novel approach for transformer fault diagnosis. Initially, a high-dimensional feature set comprising 19 features related to five gas concentrations is constructed to reflect the gas-fault relationship. Subsequently, the Shapley Additive Explanations (SHAP) method is em...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-04-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-025-00519-3 |
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