Abstract
Novel uncertainty quantification approach for machine learning based physics informed graph neural networks capable of quickly computing bulk energy system contingencies from a large search space with tolerance to changing grid topologies.
| Original language | English |
|---|---|
| State | Published - 2024 |
| Event | SIAM Activity Group on Uncertainty Quantification - Trieste, Italy Duration: Feb 27 2024 → Mar 1 2024 |
Conference
| Conference | SIAM Activity Group on Uncertainty Quantification |
|---|---|
| Country/Territory | Italy |
| City | Trieste |
| Period | 02/27/24 → 03/1/24 |
Keywords
- 97 - MATHEMATICS AND COMPUTING
- 42 - ENGINEERING
- 24 - POWER TRANSMISSION AND DISTRIBUTION
- Neural Networks
- Power Grid Contingency Analysis
- Physics Informed Graph Neural Networks
- Uncertainty Quantification
INL Publication Number
- INL/CON-24-76731
- 169873
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