Campus Units

Electrical and Computer Engineering, Statistics

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Publication Version

Accepted Manuscript

Publication Date


Journal or Book Title

IEEE Transactions on Power Systems




We use observed transmission line outage data to make a Markovian influence graph that describes the probabilities of transitions between generations of cascading line outages. 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.


This is a manuscript of an article published as Zhou, Kai, Ian Dobson, Zhaoyu Wang, Alexander Roitershtein, and Arka P. Ghosh. "A Markovian influence graph formed from utility line outage data to mitigate cascading." (2020). DOI: 10.1109/TPWRS.2020.2970406. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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The Authors



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Published Version