# Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology

arXiv: https://arxiv.org/abs/1508.01775 · HTML: https://mlsanity.com/paper/1508.01775/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2015.

## Built on by (most cited first, 9 of 9)

- [Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series](https://mlsanity.com/paper/2410.14030/) (2024)
- [Power Failure Cascade Prediction using Graph Neural Networks](https://mlsanity.com/paper/2404.16134/) (2024)
- [Cascading Failure Prediction via Causal Inference](https://mlsanity.com/paper/2410.19179/) (2024)
- [Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization](https://mlsanity.com/paper/2505.11627/) (2025)
- [Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks](https://mlsanity.com/paper/2403.15363/) (2024)
- [Predicting Cascading Failures with a Hyperparametric Diffusion Model](https://mlsanity.com/paper/2406.08522/) (2024)
- [Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks](https://mlsanity.com/paper/2112.01061/) (2021)
- [Real-Time Risky Fault-Chain Search using Time-Varying Graph RNNs](https://mlsanity.com/paper/2503.09775/) (2025)
- [Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning](https://mlsanity.com/paper/2506.08893/) (2025)

## Abstract (arXiv)

In a cascading power transmission outage, component outages propagate non-locally, after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting "influence graph" model is Markovian, in that component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from n-2 contingencies in a 2896 branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.