Vulnerable transmission line identification considering depth of
            <i>K</i>
            ‐shell decomposition in complex grids

Wang, Fan; Zhang, Xuemin; Mei, Shengwei; Huang, Shaowei

doi:10.1049/iet-gtd.2017.0767

Cited by 35 publications

(25 citation statements)

References 26 publications

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“…In recent years, a wide range of real-world complex systems, such as social network [1], [2], power grids [3]- [5], computer system [6], [7] and traffic system [8], [9], can be described by complex networks. Although complex systems bring us great convenience, the related hazards will also occur, such as the high speed and large scale of WannaCry's spread, the outbreak of infectious diseases, and the North American blackout.…”

Section: Introductionmentioning

confidence: 99%

A Novel Centrality of Influential Nodes Identification in Complex Networks

et al. 2020

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Influential nodes identification in complex networks is vital for understanding and controlling the propagation process in complex networks. Some existing centrality measures ignore the impacts of neighbor node. It is well-known that degree is a famous centrality measure for influential nodes identification, and the contributions of neighbors also should be taken into consideration. Furthermore, topological connections among neighbors will affect nodes' spreading ability, that is, the denser the connections among neighbors, the greater the chance of infection. In this paper, we propose a novel centrality, called DCC, to identify influential nodes by comprehensively considering degree and clustering coefficient as well as neighbors. The weights of degree and clustering coefficient are calculated by entropy technology. To verify the feasibility and effectiveness of DCC, the comparisons between DCC and other centrality measures in four aspects are conducted based on four real networks. The experimental results demonstrate that DCC is more effective in identifying influential nodes. INDEX TERMS Complex networks, influential nodes, degree centrality, neighbor node, clustering coefficient.

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Section: Introductionmentioning

confidence: 99%

A Novel Centrality of Influential Nodes Identification in Complex Networks

et al. 2020

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“…In recent years, the study of complex network theory has received sustained attention in various academic fields, such as aviation networks [1,2], power networks [3][4][5], social networks [6][7][8], and biological networks [9][10][11]. By collecting and analyzing data from actual networks, researchers have studied the statistical characteristics [12,13] and the dynamic behavior of networks [14].…”

Section: Introductionmentioning

confidence: 99%

Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

Yang

Lei

Zhou

et al. 2019

Mathematical Problems in Engineering

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Measuring node importance in complex networks has great theoretical and practical significance for network stability and robustness. A variety of network centrality criteria have been presented to address this problem, but each of them focuses only on certain aspects and results in loss of information. Therefore, this paper proposes a relatively comprehensive and effective method to evaluate node importance in complex networks using a multicriteria decision-making method. This method not only takes into account degree centrality, closeness centrality, and betweenness centrality, but also uses an entropy weighting method to calculate the weight of each criterion, which can overcome the influence of the subjective factor. To illustrate the effectiveness and feasibility of the proposed method, four experiments were conducted to rank node importance on four real networks. The experimental results showed that the proposed method can rank node importance more comprehensively and accurately than a single centrality criterion.

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“…Ma et al [33] applied the PageRank algorithm to screen the vulnerable lines. Structural hole [34], K-shell decomposition [35] had also been applied to the identification of vulnerable lines. In addition, a Bayes network was established to predict the cascading failure propagation based on N-1 contingency [36].…”

Section: Introductionmentioning

confidence: 99%

“…However, the blackouts propagate in the form of cascade faults. The correlation network established by the N-1 contingency [28], [33]- [35] only grasped the first stage of the cascading failure process, the depth of the fault chain was insufficiently grasped, and the vulnerability of the line in the latter failure stage cannot be well quantified, which may result in omissions or misidentifications.…”

Section: Introductionmentioning

confidence: 99%

A New Identification Approach of Power System Vulnerable Lines Based on Weighed H-Index

et al. 2019

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Identification of vulnerable lines in power systems has great meaning to predict cascading failures and prevent the blackouts. A novel approach to identify the vulnerable lines in power systems based on the weighted H-index is proposed. Firstly, to remove the Kirchhoff's law constraint in power system, a second-level correlation network is constructed considering the secondary cascading failure. Then, the relative node strength is defined in the second-level correlation network to improve the identification effectiveness. Secondly, a weighted H-index is proposed, aiming at the disadvantages of the classic Hindex only suited for unweighted networks. The improved indicator is applied in the second-level correlation network to identify the vulnerable lines of the power grid. The proposed method is verified in the IEEE-39 bus system and a real-life regional power grid in China. The experimental results show that the proposed method has higher precision compared with other methods. Specifically, the identification accuracy is improved by at least 8.33% than existing methods in the IEEE-39 bus system. In addition, the weighted H-index has higher discrimination and robustness than the classic H-index. INDEX TERMS Vulnerable lines, weighted H-index, second-level correlation network, power system.

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Vulnerable transmission line identification considering depth of K ‐shell decomposition in complex grids

Cited by 35 publications

References 26 publications

A Novel Centrality of Influential Nodes Identification in Complex Networks

A Novel Centrality of Influential Nodes Identification in Complex Networks

Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

A New Identification Approach of Power System Vulnerable Lines Based on Weighed H-Index

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