Assessing the Resilience of the Texas Power Grid Network

Dorcas Ofori-Boateng, Asim Kumer Dey, Yulia R. Gel, Binghui Li, Jie Zhang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages280-284
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Computational Theory and Mathematics
  • Artificial Intelligence

Keywords

  • Complex networks
  • Power system resilience
  • Topological features
  • Transmission lines

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  • Cite this

    Ofori-Boateng, D., Dey, A. K., Gel, Y. R., Li, B., Zhang, J., & Poor, H. V. (2019). Assessing the Resilience of the Texas Power Grid Network. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 280-284). [8755787] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755787