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Graph Neural Networks Enhance Fault Location in Power Grids with DERs

Burak Karabulut, Olayiwola Arowolo, Carlo Manna, Chris Develder, Jochen L. Cremer· August 3, 2026 View original

Key takeaways

  • STGNNs significantly improve fault location accuracy in power grids with high DER penetration.
  • Models trained on high DER penetration generalize better to lower penetration levels than vice-versa.
  • Topological awareness is crucial for robust fault location in active distribution networks.
  • STGNNs maintain high performance even in the presence of realistic measurement noise.

Who benefits

EnergyUtilitiesSmart Grid TechnologyInfrastructure Management

Summary

This research evaluates Spatio-Temporal Graph Neural Networks (STGNNs) for accurately locating faults in power distribution networks, especially those with increasing distributed energy resources (DERs). It finds STGNNs significantly outperform other models, maintaining high accuracy even with varying DER penetration and measurement noise.

A new study investigates the effectiveness of Spatio-Temporal Graph Neural Networks (STGNNs) in identifying fault locations within electrical distribution networks. The challenge arises from the growing integration of Distributed Energy Resources (DERs), which introduce intermittent generation and bidirectional power flows, altering traditional fault signatures. Researchers benchmarked STGNNs against other neural network types and traditional machine learning methods. The findings indicate that STGNNs, specifically STGATv2, consistently deliver superior performance, achieving high F1 scores in various scenarios. Crucially, these models demonstrate robust generalization capabilities across different DER penetration levels. Training an STGNN at high DER penetration (50%) allows it to perform well at lower levels, whereas models trained at low penetration struggle significantly when DERs increase. This highlights the importance of topological awareness for reliable fault location in modern, active power grids, even under realistic measurement noise.

Why it matters

Professionals in energy infrastructure and grid management need robust solutions for maintaining reliability as renewable energy sources become more prevalent. This research offers a promising AI-driven approach to enhance fault detection and minimize downtime in complex power systems.

How to implement this in your domain

  1. 1Evaluate existing fault detection systems for compatibility with advanced AI models like STGNNs.
  2. 2Pilot STGNN-based solutions in a controlled environment using historical grid data with varying DER scenarios.
  3. 3Collaborate with AI researchers to adapt and integrate these models into current operational technology (OT) infrastructure.
  4. 4Develop training programs for grid operators on interpreting and utilizing AI-driven fault location insights.
  5. 5Invest in data collection infrastructure to feed high-quality spatio-temporal data to these advanced models.

Original post by Burak Karabulut, Olayiwola Arowolo, Carlo Manna, Chris Develder, Jochen L. Cremer

"arXiv:2607.29293v1 Announce Type: new Abstract: Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to intermittent generation and bidirectional power flows that…"

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Originally posted by Burak Karabulut, Olayiwola Arowolo, Carlo Manna, Chris Develder, Jochen L. Cremer on X · view source

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