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Real-Time Risky Fault-Chain Search using Time-Varying Graph RNNs

arXiv 2503.09775 ↗First posted 12 Mar 2025cs.LG cs.SY eess.SPcited by 0

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Abstract (arXiv)

This paper introduces a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs) in power-grids, crucial for enhancing grid resiliency in the face of climate change. As extreme weather events driven by climate change increase, identifying risky FCs becomes crucial for mitigating cascading failures and ensuring grid stability. However, the complexity of the spatio-temporal dependencies among grid components and the exponential growth of the search space with system size pose significant challenges to modeling and risky FC search. To tackle this, we model the search process as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN). This approach captures the spatial and temporal structure induced by the system's topology and dynamics, while efficiently summarizing the system's history in the GRNN's latent space, enabling scalable and effective identification of risky FCs.

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References · 14

  1. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 457
  2. 2019Tackling Climate Change with Machine Learningcited by 264
  3. 2020Gated Graph Recurrent Neural Networkscited by 57
  4. 2020Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learningcited by 12
  5. 2015Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topologycited by 9
  6. 2017Filtering Random Graph Processes Over Random Time-Varying Graphscited by 8
  7. 2014An Interaction Model for Simulation and Mitigation of Cascading Failurescited by 6
  8. 2019A Markovian influence graph formed from utility line outage data to mitigate large cascadescited by 5
  9. 2023GRNN-based Real-time Fault Chain Predictioncited by 4
  10. 2024Power Failure Cascade Prediction using Graph Neural Networkscited by 4
  11. 2024Blackout Mitigation via Physics-guided RLcited by 3
  12. 2024Cascading Failure Prediction via Causal Inferencecited by 3
2 more
  1. 2016Risk Assessment of Multi-timescale Cascading Outages based on Markovian Tree Searchcited by 1
  2. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factorscited by 1

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