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Reliability analysis and optimal generator allocation and protection strategy of a non-repairable power grid system

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Listed:
  • Cao, Minhao
  • Guo, Jianjun
  • Xiao, Hui
  • Wu, Liang

Abstract

In a power grid system, generators may fail due to the internal structure and material degradation caused by stress levels or work conditions. However, power grid systems may also be subject to external impacts such as landslides and earthquakes. Protecting these systems can mitigate the external negative effects. In this research, we extend the general network reliability model with consideration of the individual generator protection and the overarching protection. The generators can be destroyed if and only if both the individual protection and the overarching protection are destroyed. A joint generator allocation and protection optimization problem is formulated to increase the system reliability. We propose an algorithm to evaluate the system reliability by extending the universal generating function technique. Numerical experiments show that determining the generator allocation and the protection strategy optimally can improve the system reliability.

Suggested Citation

  • Cao, Minhao & Guo, Jianjun & Xiao, Hui & Wu, Liang, 2022. "Reliability analysis and optimal generator allocation and protection strategy of a non-repairable power grid system," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022001089
    DOI: 10.1016/j.ress.2022.108443
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    4. Zhang, Chenwei & Wang, Ying & Zheng, Tao & Zhang, Kaifeng, 2024. "Complex network theory-based optimization for enhancing resilience of large-scale multi-energy System11The short version of the paper was presented at CUE2023. This paper is a substantial extension of," Applied Energy, Elsevier, vol. 370(C).
    5. Gu, Liudong & Wang, Guanjun & Zhou, Yifan, 2024. "Optimal allocation of multi-state performance sharing systems with multiple common buses," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    6. Christine H. Müller & Kirsten Schorning, 2023. "A-optimal designs for state estimation in networks," Statistical Papers, Springer, vol. 64(4), pages 1159-1186, August.
    7. Sun, Qin & Li, Hongxu & Zhong, Yuanfu & Ren, Kezhou & Zhang, Yingchao, 2024. "Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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