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

arXiv: https://arxiv.org/abs/2503.09775 · HTML: https://mlsanity.com/paper/2503.09775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Gated Graph Recurrent Neural Networks](https://mlsanity.com/paper/2002.01038/) (2020)
- [Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning](https://mlsanity.com/paper/2012.14906/) (2020)
- [Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology](https://mlsanity.com/paper/1508.01775/) (2015)
- [Filtering Random Graph Processes Over Random Time-Varying Graphs](https://mlsanity.com/paper/1705.00442/) (2017)
- [An Interaction Model for Simulation and Mitigation of Cascading Failures](https://mlsanity.com/paper/1401.0750/) (2014)
- [A Markovian influence graph formed from utility line outage data to mitigate large cascades](https://mlsanity.com/paper/1902.00686/) (2019)
- [GRNN-based Real-time Fault Chain Prediction](https://mlsanity.com/paper/2303.08864/) (2023)
- [Power Failure Cascade Prediction using Graph Neural Networks](https://mlsanity.com/paper/2404.16134/) (2024)
- [Blackout Mitigation via Physics-guided RL](https://mlsanity.com/paper/2401.09640/) (2024)
- [Cascading Failure Prediction via Causal Inference](https://mlsanity.com/paper/2410.19179/) (2024)
- [Risk Assessment of Multi-timescale Cascading Outages based on Markovian Tree Search](https://mlsanity.com/paper/1603.03935/) (2016)
- [RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors](https://mlsanity.com/paper/2411.18050/) (2024)

## 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.