Robustness analysis of power system under sequential attacks with incomplete information

Haicheng Tu, Fengqiang Gu, Xi Zhang and Yongxiang Xia

Reliability Engineering and System Safety, 2023, vol. 232, issue C

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.

Keywords: Robustness analysis; Sequential attacks; Power system; Incomplete information; Learning algorithm (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006639

DOI: 10.1016/j.ress.2022.109048

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