Sequential recovery of cyber-physical power systems with distributed generation

被引:0
作者
Chen, Beichen [1 ,2 ]
Lv, Haoyuan [2 ]
Li, Yiqiang [3 ]
Li, Jian [3 ,4 ]
机构
[1] Northeast Elect Power Univ, Sch Energy & Power Engn, Jilin 132012, Peoples R China
[2] Jilin Inst Chem Technol, Sch Informat & Control Engn, Jilin 132022, Peoples R China
[3] Northeast Elect Power Univ, Sch Automat Engn, Jilin 132012, Peoples R China
[4] Jilin Prov Key Lab Adv Control Technol Smart Energ, Jilin 132012, Peoples R China
关键词
Cyber-physical power systems; Sequential recovery; Cascading failure; Distributed generation; Deep Q-network;
D O I
10.1016/j.epsr.2025.111529
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a sequential recovery framework is constructed for post-disaster cyber-physical power systems from the viewpoint of the theory of complex networks. Grounded in the power flow analysis pattern of cyber- physical power systems, the framework integrates the recovery sequence, the access of distributed generation, and the cascading failures during restoration. Furthermore, the deep Q-network algorithm is utilized to determine the most effective sequence for repairing malfunctioning parts, thereby maximizing the recovery of the power that was lost. The effects of distributed generation node quantity ratio and permeability on system recovery performance are analyzed on the cyber-physical power system test cases generated grounded in IEEE 30-bus system and IEEE 118-bus system. Meanwhile, the applicability and superiority of the proposed recovery strategy are verified by comparison with alternative strategies.
引用
收藏
页数:10
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共 31 条
  • [1] Liu Y., Yang X., Wen W., Xia M., Smarter grid in the 5G era: A framework integrating power internet of things with a cyber physical system, Front. Commun. Netw., 2, (2021)
  • [2] Li J., Li Y., Su Q., Sequential recovery of cyber-physical power systems based on improved Q-learning, J. Franklin Inst., 360, pp. 13692-13711, (2022)
  • [3] Shepherd A., Crisis in Venezuela, BMJ, (2019)
  • [4] Petitet M., Felder F.A., Alhadhrami K., One Year After the Texas Blackout: Lessons for Reliable and Resilient Power Systems, (2022)
  • [5] Fang Y., Reflections on frequency stability control technology Based on the blackout event of 9 August 2019 in UK, Autom. Electr. Power Syst., (2019)
  • [6] Liu W., Zhan J., Chung C.Y., Sun L., Availability assessment based case-sensitive power system restoration strategy, IEEE Trans. Power Syst., 35, 2, pp. 1432-1445, (2020)
  • [7] Zhao J., Sun S., Shen H., Xia C., An Effective Network Repair Strategy Against Both Random and Malicious Edge Attacks, pp. 8628-8633, (2021)
  • [8] Min O., Dueas-Osorio L., Min X., A three-stage resilience analysis framework for urban infrastructure systems, Struct. Saf., 36-37, none, pp. 23-31, (2012)
  • [9] Huang W., Gao Y., Zhang T., Gao H., Q-learning-based sequential recovery of interdependent power-communication network after cascading failures, Neural Comput. Appl., 35, pp. 12833-12845, (2023)
  • [10] Xu Z., Ramirez-Marquez J.E., Liu Y., Xiahou T., A new resilience-based component importance measure for multi-state networks, Reliab. Eng. Syst. Saf., 193, (2020)