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powergraph-gnn-research

Research pipeline for topology-aware GNN representation learning on power grids using the PowerGraph benchmark. Use when (1) building physics-guided GNNs for power flow (PF), optimal power flow (OPF), or cascading failure prediction, (2) implementing self-supervised pretraining for power systems, (3) evaluating cascade explanation fidelity against ground-truth masks, or (4) conducting reproducible ML-for-power-systems research. Triggers include "PowerGraph", "power flow GNN", "OPF surrogate", "cascade prediction", "physics-guided GNN", "grid analytics ML", "power system representation learning".

personAuthor: jakexiaohubgithub
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PowerGraph GNN Research Pipeline

Primary claim: A grid-specific self-supervised, physics-consistent GNN encoder improves PF/OPF learning (especially low-label/OOD), and transfers to cascading-failure prediction and explanation.

Scripts

| Task | Script | |------|--------| | Data ingestion | scripts/load_powergraph.py | | PF baseline | scripts/train_pf_baseline.py | | Physics metrics | scripts/physics_residual.py | | SSL pretraining | scripts/pretrain_ssl.py | | Multi-task training | scripts/train_multitask.py | | Explanation eval | scripts/eval_cascade_explanation.py |

Workflow

  1. Data → PowerGraph → PyG (PF/OPF node targets + cascade graph labels + exp masks)
  2. Baseline → PF regression with sin/cos angles + physics residual metric
  3. Multi-task → Shared encoder + PF/OPF/Cascade heads
  4. SSL → Masked injection/edge reconstruction → fine-tune
  5. Evaluation → Explanation AUC vs ground-truth masks + robustness tests

Validity Anchors (Critical)

Angle handling: Predict sin(θ), cos(θ), recover via atan2. Direct MSE on raw angles fails at ±π wrap-around.

Physics residual: Report KCL mismatch alongside accuracy. Ground truth ≈ 0, random >> 1.

Blocked splits: PowerGraph uses 1-year load @ 15-min. Use months 1-9 train / 10 val / 11-12 test. Random splits leak seasonal patterns.

Explanation fidelity: Use PowerGraph exp.mat ground-truth masks. Report AUC + Precision@K.

Reference Docs

  • references/data_pipeline.md — PowerGraph → PyG conversion, splits
  • references/model_architecture.md — Physics-guided message passing, heads
  • references/ssl_pretraining.md — Masked tasks, low-label experiments
  • references/uncertainty_quantification.md — Ensembles, MC dropout, calibration
  • references/evaluation_protocols.md — Metrics, robustness, statistical tests
  • references/publication_soundness.md — Reviewer risks, claim framing
  • references/experiment_configs.md — YAML config structure, sweeps

Common Pitfalls

| Issue | Fix | |-------|-----| | Angle wrap-around | sin/cos representation | | Data leakage | Blocked time splits | | Cascade imbalance | Weighted/focal loss | | OOM large grids | Gradient checkpointing | | SSL collapse | Stop-gradient + EMA encoder | | Physics violations | Residual regularization |

Publication Checklist

  • [ ] Ablation: single-task vs multi-task vs SSL+multi-task
  • [ ] Low-label curves: 10/20/50/100% training data
  • [ ] Physics residual alongside accuracy metrics
  • [ ] Blocked splits (not random)
  • [ ] Explanation AUC against exp.mat ground truth
  • [ ] Robustness under edge-drop perturbations
  • [ ] Statistical significance with 95% CI
  • [ ] One-command reproducibility (python analysis/run_all.py)