Robust Local Contingency Planning in Power Grids Using Spatio-Temporal Graph Neural Networks
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Bibliographic record
Abstract
This study investigates the use of Graph Neural Networks (GNNs) for modeling spatio-temporal dynamics under extreme conditions, framed as a Reinforcement Learning (RL) problem. It focuses on voltage magnitude, voltage angle, and load predictions in complex contingency scenarios. A GNN-based regression model, utilizing Graph Convolutional Networks (GCN), was developed with residual connections to enhance learning stability and accuracy. The training objective combined a traditional loss function with physics-informed regularization terms, including voltage stability loss, current balance constraints, and demand penalties. Results on the IEEE-118 benchmark show a strong agreement between GNN predictions and theoretical optimal power flow solutions, with only minor discrepancies. Results underscore the model’s ability to rapidly and accurately predict complex spatio-temporal system responses under extreme exogenous events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it