Campus Units
Electrical and Computer Engineering, Statistics
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
1-30-2020
Journal or Book Title
IEEE Transactions on Power Systems
DOI
10.1109/TPWRS.2020.2970406
Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Owner
The Authors
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Zhou, Kai; Dobson, Ian; Wang, Zhaoyu; Roitershtein, Alexander; and Ghosh, Arka P., "A Markovian influence graph formed from utility line outage data to mitigate large cascades" (2020). Electrical and Computer Engineering Publications. 209.
https://lib.dr.iastate.edu/ece_pubs/209
Comments
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.