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Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment

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  • Li, Jian
  • Dueñas-Osorio, Leonardo
  • Chen, Changkun
  • Shi, Congling

Abstract

As infrastructure systems evolve, their design, maintenance, and optimal performance require mature tools from system reliability theory, as well as principles to handle emerging system features, such as controllability. This paper conducts a comparative study of the connectivity reliability (CR) and topological controllability (TC) of infrastructure systems in terms of three aspects: topology, robustness, and node importance. Taking eight city-level power transmission networks and thousands of artificial networks as examples, this paper reveals that a dense and homogeneous network topology is better to satisfy CR and TC requirements, than more common sparse and heterogeneous networks when node attributes are generic. It is observed that the average degree's impact on CR is more significant than on TC, while degree heterogeneity is more significant on TC. When node attributes are accounted for, for generators the reliability-based node importance measure may underestimate some important nodes in terms of TC, and vice versa—an issue not observed for substation nodes. The findings in this paper suggest a potential new direction to enhance reliability-based design by integrating it with emerging controllability-based measures relevant in the future as infrastructure networks increase reliance on information systems.

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  • Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:24-33
    DOI: 10.1016/j.ress.2016.07.003
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    3. Jing Liu & Huapu Lu & He Ma & Wenzhi Liu, 2017. "Network Vulnerability Analysis of Rail Transit Plans in Beijng-Tianjin-Hebei Region Considering Connectivity Reliability," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    4. Han, Fangyuan & Zio, Enrico, 2019. "A multi-perspective framework of analysis of critical infrastructures with respect to supply service, controllability and topology," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 1-13.
    5. Fangyuan Han & Enrico Zio, 2018. "Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability, controllability and topological perspectives," Journal of Risk and Reliability, , vol. 232(2), pages 216-224, April.
    6. Lee, Joohyun & Kwak, Jaewook & Lee, Hyang-Won & Shroff, Ness B., 2018. "Finding minimum node separators: A Markov chain Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 225-235.
    7. Liu, Xiaohang & Zheng, Shansuo & Wu, Xinxia & Chen, Dianxin & He, Jinchuan, 2021. "Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2017. "AC power flow importance measures considering multi-element failures," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 89-97.
    10. Nogal, M. & Honfi, D., 2019. "Assessment of road traffic resilience assuming stochastic user behaviour," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 72-83.

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