Robustness analysis of power system under sequential attacks with incomplete information

Haicheng Tu, Fengqiang Gu, Xi Zhang, Yongxiang Xia*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Modern power system demonstrates good performance to withstand a single failure. However, the recent progress has shown that power system is increasingly threatened by the sequential attacks. In this paper, we aim at evaluating the robustness of power system under the sequential attacks with incomplete information, taking into account both the electrical properties and the cascading failure process. In light of this, we first formulate the sequential attacks as a partial observable Markov decision process, and use Deep Q-network algorithm to identify the optimal attack sequence. The influences of network structures, attacks methods and protection measures are demonstrated in complex networks and IEEE 118-Bus system. Experimental results show that our proposed algorithm can effectively identify the optimal attack sequence under different attack methods and protection measures. Moreover, a larger redundant parameter and homogeneous network can improve the robustness of power system. Our findings can provide practical insights for building a robust power system.

Original languageEnglish
Article number109048
JournalReliability Engineering and System Safety
Volume232
DOIs
Publication statusPublished - Apr 2023

Keywords

  • Incomplete information
  • Learning algorithm
  • Power system
  • Robustness analysis
  • Sequential attacks

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