@inproceedings{883b74690f7441179a463790f7d585d8,
title = "Assessing the Resilience of the Texas Power Grid Network",
abstract = "Understanding the structural properties of the power grids under different disruptive event scenarios is the key towards improvement of the security, reliability, and efficiency of modern power systems. In this pilot study, the concepts of topological data analysis, particularly, persistent homology, are used to derive a new metric for resilience of power grid networks. The proposed approach is illustrated in application to a simulated version of the Texas power grid network, under node and edge based attacks for three different weight functions.",
keywords = "Complex networks, Power system resilience, Topological features, Transmission lines",
author = "Dorcas Ofori-Boateng and Dey, \{Asim Kumer\} and Gel, \{Yulia R.\} and Binghui Li and Jie Zhang and Poor, \{H. Vincent\}",
year = "2019",
month = jun,
doi = "10.1109/DSW.2019.8755787",
language = "English (US)",
series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "280--284",
booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",
address = "United States",
note = "2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",
}