Resilient Temporal Graph Convolutional Network for Smart Grid State Estimation Under Topology Inaccuracies
Dynamic State Estimation is a crucial task in power systems. Graph Neural Networks have demonstrated significant potential in dynamic state estimation, for power systems by effectively analyzing measurement data and capturing the complex interactions and interrelations among the measurements through...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11105082/ |
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