Residual-aware health prediction of power transformers via spatiotemporal graph neural networks.
Accurate health state prediction and timely fault detection of power transformers are critical for ensuring the reliability and resilience of modern power systems. This paper proposes a residual-aware spatiotemporal graph neural network (STGNN) framework that jointly models dynamic topological depen...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science (PLoS)
2025-01-01
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| coleção: | PLoS ONE |
| Acesso em linha: | https://doi.org/10.1371/journal.pone.0332381 |
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