# Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks

arXiv: https://arxiv.org/abs/2403.15363 · HTML: https://mlsanity.com/paper/2403.15363/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology](https://mlsanity.com/paper/1508.01775/) (2015)
- [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)
- [Robustness of Interdependent Random Geometric Networks](https://mlsanity.com/paper/1709.03032/) (2017)
- [Group percolation in interdependent networks](https://mlsanity.com/paper/1801.00665/) (2018)

## Built on by (most cited first, 2 of 2)

- [Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention](https://mlsanity.com/paper/2605.07010/) (2026)
- [Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification](https://mlsanity.com/paper/2609.04943/) (2026)

## Abstract (arXiv)

Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of possible failures and load/generation patterns. We add to the growing literature of faster graph-neural-network (GNN)-based techniques, developing two novel techniques for the estimation of blackout magnitude from initial grid conditions. First we propose several methods for employing an initial classification step to filter out safe "non blackout" scenarios prior to magnitude estimation. Second, using insights from the statistical properties of cascading blackouts, we propose a method for facilitating non-local message passing in our GNN models. We validate these two approaches on a large simulated dataset, and show the potential of both to increase blackout size estimation performance.