Identifying Sets of Critical Components That Affect the Resilience of Power Networks

Shan, Xiwen; Zhang, Xi; Tse, Chi K.

doi:10.1109/tcsii.2022.3188348

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…From the perspective of graph theory, the nature of radial distribution network is very similar to that of spanning tree [30]. According to this, the constraint of radial topology operation of the distribution network can be written as Equation (22).…”

Section: Constraint Of Radial Topologymentioning

confidence: 99%

“…If bus i is the child node of bus j, then 𝜄 i j = 0, else 𝜄 i j = 1. Equation (22) ensures that there is no loop operation in the distribution network.…”

Section: Constraint Of Radial Topologymentioning

confidence: 99%

“…However, the resilience of the system should also take account its pre-defence, in-process adaptation, and postrestoration capabilities when extreme events occur [22,23]. Therefore, to prevent cyber space risks from hindering the restoration of CPDSs from fault after extreme events, it is necessary to improve the system resilience enhancement strategy from the perspective of the entire fault development process.…”

Section: Introductionmentioning

confidence: 99%

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Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Wang

et al. 2023

IET Renewable Power Gen

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Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

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Section: Constraint Of Radial Topologymentioning

confidence: 99%

“…If bus i is the child node of bus j, then 𝜄 i j = 0, else 𝜄 i j = 1. Equation (22) ensures that there is no loop operation in the distribution network.…”

Section: Constraint Of Radial Topologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

Wang

et al. 2023

IET Renewable Power Gen

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“…There has been a lot of research on key node identification methods. [21][22][23][24][25][26][27][28][29][30][31] Researchers often use node centrality methods to measure the importance of nodes, such as degree centrality, betweenness centrality, and closeness centrality. [21] Based on these methods, some researchers have proposed new methods by combining different centrality metrics or improving them.…”

Section: Introductionmentioning

confidence: 99%

Identification of key recovering node for spatial networks

Zhang

Xia

Guo³

et al. 2023

Chinese Phys. B

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Many networks in the real-world have spatial attributes, such as the location of nodes and the length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by recovering which the network performance can be recovered the most. In this paper, we propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance (ED) recovery method and route-length (RL) recovery method, respectively. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.

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