TY - GEN
T1 - Assessing the Resilience of the Texas Power Grid Network
AU - Ofori-Boateng, Dorcas
AU - Dey, Asim Kumer
AU - Gel, Yulia R.
AU - Li, Binghui
AU - Zhang, Jie
AU - Poor, H. Vincent
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Complex networks
KW - Power system resilience
KW - Topological features
KW - Transmission lines
UR - http://www.scopus.com/inward/record.url?scp=85069519661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069519661&partnerID=8YFLogxK
U2 - 10.1109/DSW.2019.8755787
DO - 10.1109/DSW.2019.8755787
M3 - Conference contribution
T3 - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
SP - 280
EP - 284
BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Data Science Workshop, DSW 2019
Y2 - 2 June 2019 through 5 June 2019
ER -