# A Markovian influence graph formed from utility line outage data to mitigate large cascades

arXiv: https://arxiv.org/abs/1902.00686 · HTML: https://mlsanity.com/paper/1902.00686/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Cascading Failure Prediction via Causal Inference](https://mlsanity.com/paper/2410.19179/) (2024)
- [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)
- [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)

We use observed transmission line outage data to make a Markov influence graph that describes the probabilities of transitions between generations of cascading line outages, where each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.