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Cascading Effects of Targeted Attacks on the Power Grid

  • Rounak Meyur
  • Anil Vullikanti
  • Madhav V. Marathe
  • Anamitra Pal
  • Mina Youssef
  • Virgilio Centeno
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

We study the resilience of real world power grids to targeted adversarial attacks. Prior blackouts have shown that failures in the power grid can cascade, starting from a single failure, leading to a large number of failed nodes. In this paper, we study the problem of identifying a set of k critical nodes, whose failure/attack leads to the maximum number of tripped nodes. There has been a lot of work on this problem, but it has been mainly restricted to simple networks and failure models with either steady state analysis or DC power flow. In this paper, we perform AC power flow based transient analysis on a detailed power grid model. We find that a simple greedy approach yields node sets with higher criticality than a degree based approach, which has been suggested in many prior works. Furthermore, we observe that the cascades exhibit a non-monotonic behavior as a function of k.

Notes

Acknowledgements

We thank the reviewers for their constructive comments. We also thank members of the Network Dynamics and Simulation Science Laboratory at Virginia Tech and Professor Arun Phadke and late Professor James Thorp for their collaboration and their insightful suggestions on topics related to the paper. This work has been partially supported by DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), NSF DIBBS Grant ACI-1443054, NSF BIG DATA Grant IIS-1633028, NSF EAGER Grant CMMI-1745207 and DOE Grant DE-EE0007660.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Rounak Meyur
    • 1
  • Anil Vullikanti
    • 2
  • Madhav V. Marathe
    • 2
  • Anamitra Pal
    • 3
  • Mina Youssef
    • 4
  • Virgilio Centeno
    • 1
  1. 1.Department of Electrical and Computer EngineeringVirginia TechBlacksburgUSA
  2. 2.Biocomplexity Institute and Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA
  3. 3.School of Electrical, Computer, and Energy EngineeringArizona State UniversityTempeUSA
  4. 4.Eaton Power CenterOmahaUSA

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