Overview
Graph neural network for stability margin estimation under contingency scenarios.
gnnstabilitycontingency
Confidence
84%
Blended from documentation depth, benchmark presence, and cross-source corroboration.
Significance score: 81/100
Source metadata
- Primary source
- arXiv
- Last updated
- 2026-04-23
- Domain
- Optimization
Maturity assessment
Emerging
Assessment combines release cadence, maintainer responsiveness, and sector-specific adoption proxies.
AI-generated summary
Graph neural network for stability margin estimation under contingency scenarios.
Utility relevance reasoning
Supports N-1 contingency screening and operator decision support in control centers.
Related technologies
- LoadSense AI
Time Series Forecasting
- TransformerGrid
Optimization
- OutageGraph Optimizer
Optimization
Lineage references
Lineage Map- Upstream paper references linked in model card.
- Dataset manifests registered for reproducibility.
Source citations
- GridStability GNN README — installation, data expectations, and evaluation metrics.
- Model documentation — hyperparameters and reproducibility notes.
- Mobiloitte internal rubric MOBILOITTE-AIR-UR-02 — utility relevance scoring dimensions.
Timeline of changes
- 2026-05-02Maturity reassessed from Emerging → Growth based on adoption signals.
- 2026-04-18Utility relevance mapping refreshed for smart grid sector weighting.