%0 Journal Article %1 Hines2016Cascading %A Hines, Paul %A Dobson, Ian %A Rezaei, Pooya %D 2016 %J IEEE Transactions on Power Systems %K power\_grids influence cascades %P 1 %R 10.1109/TPWRS.2016.2578259 %T Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology %U http://dx.doi.org/10.1109/TPWRS.2016.2578259 %X 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, since 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.

@article{Hines2016Cascading, abstract = {{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, since 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.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Hines, Paul and Dobson, Ian and Rezaei, Pooya}, biburl = {https://www.bibsonomy.org/bibtex/21e5b3d98f39cbe0c1dee97d22bbdae26/nonancourt}, citeulike-article-id = {13764529}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPWRS.2016.2578259}, citeulike-linkout-1 = {http://arxiv.org/abs/1508.01775}, citeulike-linkout-2 = {http://arxiv.org/pdf/1508.01775}, day = 7, doi = {10.1109/TPWRS.2016.2578259}, eprint = {1508.01775}, interhash = {e4eb5460eb0ff0e6ca64baa04b890c49}, intrahash = {1e5b3d98f39cbe0c1dee97d22bbdae26}, issn = {1558-0679}, journal = {IEEE Transactions on Power Systems}, keywords = {power\_grids influence cascades}, month = aug, pages = 1, posted-at = {2015-09-15 15:54:37}, priority = {2}, timestamp = {2019-07-31T12:52:15.000+0200}, title = {{Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology}}, url = {http://dx.doi.org/10.1109/TPWRS.2016.2578259}, year = 2016 }