mlsanityData to 14 Oct 2026

SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids

arXiv 2407.12421 ↗First posted 17 Jul 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

Power grids are critical infrastructures of paramount importance to modern society and their rapid evolution and interconnections has heightened the complexity of power systems (PS) operations. Traditional methods for grid analysis struggle with the computational demands of large-scale RES and ES integration, prompting the adoption of machine learning (ML) techniques, particularly Graph Neural Networks (GNNs). GNNs have proven effective in solving the alternating current (AC) Power Flow (PF) and Optimal Power Flow (OPF) problems, crucial for operational planning. However, existing benchmarks and datasets completely ignore safety and robustness requirements in their evaluation and never consider realistic safety-critical scenarios that most impact the operations of the power grids. We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for GNNs in PS operations. SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages. Our extensive experiments underscore the importance of self-supervised learning and graph attention architectures for GNN robustness. We provide at https://github.com/yamizi/SafePowerGraph our open-source repository, a comprehensive leaderboard, a dataset and model zoo and expect our framework to standardize and advance research in the critical field of GNN for power systems.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

26 of 45 references.

References · 26

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
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  9. 2021Robustness of Graph Neural Networks at Scalecited by 100
  10. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
  11. 2019Optimal Power Flow Using Graph Neural Networkscited by 63
  12. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 63
14 more
  1. 2019Certifiable Robustness and Robust Training for Graph Convolutional Networkscited by 59
  2. 2019Imperceptible Adversarial Attacks on Tabular Datacited by 35
  3. 2021Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustnesscited by 35
  4. 2017PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulationscited by 33
  5. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  6. 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directionscited by 28
  7. 2021A Unified Framework for Adversarial Attack and Defense in Constrained Feature Spacecited by 22
  8. 2022Adversarial Weight Perturbation Improves Generalization in Graph Neural Networkscited by 21
  9. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  10. 2021Adversarial Robustness in Multi-Task Learning: Promises and Illusionscited by 17
  11. 2022Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flowcited by 4
  12. 2023GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Taskscited by 4
  13. 2024PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systemscited by 4
  14. 2024Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placementcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Modelscited by 6
  2. 2025SafePowerGraph-HIL: Real-Time HIL Validation of Heterogeneous GNNs for Bridging Sim-to-Real Gap in Power Gridscited by 1
  3. 2026Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shiftcited by 1
  4. 2026MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Modelcited by 1
  5. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0