English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Network Measure-Enriched GNNs: A New Framework for Power Grid Stability Prediction

Authors
/persons/resource/Junyou.Zhu

Zhu,  Junyou
Potsdam Institute for Climate Impact Research;

/persons/resource/nauck

Nauck,  Christian
Potsdam Institute for Climate Impact Research;

/persons/resource/Michael.Lindner

Lindner,  Michael
Potsdam Institute for Climate Impact Research;

He,  Langzhou
External Organizations;

Yu,  Philip S.
External Organizations;

Müller,  Klaus-Robert
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

/persons/resource/frank.hellmann

Hellmann,  Frank       
Potsdam Institute for Climate Impact Research;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Zhu, J., Nauck, C., Lindner, M., He, L., Yu, P. S., Müller, K.-R., Kurths, J., Hellmann, F. (2025 online): Network Measure-Enriched GNNs: A New Framework for Power Grid Stability Prediction. - IEEE Transactions on Knowledge and Data Engineering.
https://doi.org/10.1109/TKDE.2025.3624222


Cite as: https://publications.pik-potsdam.de/pubman/item/item_33427
Abstract
Facing climate change, the transformation to renewable energy poses stability challenges for power grids due to their reduced inertia and increased decentralization. Traditional dynamic stability assessments, crucial for safe grid operation with higher renewable shares, are computationally expensive and unsuitable for large-scale grids in the real world. Although multiple proofs in the network science have shown that network measures, which quantify the structural characteristics of networked dynamical systems, have the potential to facilitate basin stability prediction, no studies to date have demonstrated their ability to efficiently generalize to real-world grids. With recent breakthroughs in Graph Neural Networks (GNNs), we are surprised to find that there is still a lack of a common foundation about: Whether network measures can enhance GNNs' capability to predict dynamic stability and how they might help GNNs generalize to realistic grid topologies. In this paper, we conduct, for the first time, a comprehensive analysis of 48 network measures in GNN-based stability assessments, introducing two strategies for their integration into the GNN framework. We uncover that prioritizing measures with consistent distributions across different grids as the input or regarding measures as auxiliary supervised information improves the model's generalization ability to realistic grid topologies, even when models trained on only 20-node synthetic datasets are used. Our empirical results demonstrate a significant enhancement in model generalizability, increasing the R2 perforsmance from 66% to 83%. When evaluating the probabilistic stability indices on the realistic Texan grid model, GNNs reduce the time needed from 28,950 hours (Monte Carlo sampling) to just 0.06 seconds.