mlsanityData to 14 Oct 2026

Predicting Basin Stability of Power Grids using Graph Neural Networks

arXiv 2108.08230 ↗First posted 18 Aug 2021physics.soc-ph cs.LG cs.SYcited by 11

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Abstract (arXiv)

The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 66 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

15 of 66 references.

References · 15

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3 more
  1. 2021PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power gridscited by 6
  2. 2018Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Networkcited by 4
  3. 2019On structural and dynamical factors determining the integrated basin instability of power-grid nodescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 5; 2025: 1; 2026: 1.

11 papers.

  1. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  2. 2022Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networkscited by 4
  3. 2023BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUscited by 4
  4. 2022Towards dynamic stability analysis of sustainable power grids using graph neural networkscited by 3
  5. 2024Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricitycited by 2
  6. 2024Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictionscited by 2
  7. 2024Graph Neural Networks Need Cluster-Normalize-Activate Modulescited by 2
  8. 2025Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Studycited by 2
  9. 2026When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Seriescited by 1
  10. 2022Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Netscited by 0
  11. 2024Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Gridcited by 0