Robustness of Interdependent Power Grids and Communication Networks: A Complex Network Perspective

Chen, Zhenhao; Wu, Jiajing; Xia, Yongxiang; Zhang, Xi

doi:10.1109/tcsii.2017.2705758

Cited by 215 publications

(72 citation statements)

References 16 publications

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“…Network types Load Metrics Models Network types Load Metrics [7] supply chain networks degree [14] WSNs betweenness [8] electricity grids simulated current [15] WSNs degree [9] communication networks betweenness [16] WSNs betweenness [10] interdependent networks betweenness [17] WSNs exponential degree [11] transmission networks betweenness [18] WSNs betweenness [12] cyber-physical systems betweenness [19] WSNs cluster degree [13] transportation networks degree [20] WSNs number of messages electricity grids based on the alternating current model. Ren et al [9] proposed a stochastic model to study the cascading dynamics in communication networks and identified the vital nodes from the perspective of network invulnerability.…”

Section: Modelsmentioning

confidence: 99%

“…Ren et al [9] proposed a stochastic model to study the cascading dynamics in communication networks and identified the vital nodes from the perspective of network invulnerability. Chen et al [10] investigated the cascading process in interdependent power grids and communication networks. Wu et al [11] analyzed the impacts of link capacity on the cascading process in general transmission networks and found that a bifurcation point may exist in some cases which divides regions of opposite robustness behavior.…”

Section: Modelsmentioning

confidence: 99%

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Sink‐Convergence Cascading Model for Wireless Sensor Networks with Different Load‐Redistribution Schemes

Yao

Yang

2019

Complexity

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Existing cascading models are unable to depict the sink-convergence characteristic of WSNs (wireless sensor networks). In this work, we build a more realistic cascading model for WSNs, in which two load-redistribution schemes (i.e., idle redistribution and even redistribution) are introduced. In addition, failed nodes are allowed to recover after a certain time delay rather than being deleted from the network permanently. Simulation results show that the network invulnerability is positively correlated to the tolerance coefficient and negatively correlated to the exponential coefficient. Under the idle-redistribution scheme, the network can have stronger invulnerability against cascading failures. The extension of the recovery time will exacerbate the fluctuation of the cascading process.

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Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Sink‐Convergence Cascading Model for Wireless Sensor Networks with Different Load‐Redistribution Schemes

Yao

Yang

2019

Complexity

View full text Add to dashboard Cite

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“…(c) From the security point of view, an interaction model is established for the importance of the coupling relationship between a PN and a supervisory control and data acquisition (SCADA) system. (d) A model has been established to analyze the process of cascading failure propagation between the PN and the CN [8,19,21,25]. Its basic idea is to calculate the power flow redistribution after cascading failures occur by the dynamic power flow.…”

Section: Related Workmentioning

confidence: 99%

“…For coupled networks, researchers analyze the CCF using static geometric methods in the early stage [15][16][17][18][19][20]. Then, the dynamic methods considering the load redistribution [21][22][23][24][25] are explored. These static and dynamic methods are based on the giant cluster assumption [15].…”

Section: Introductionmentioning

confidence: 99%

Cyber–Physical Active Distribution Networks Robustness Evaluation against Cross-Domain Cascading Failures

et al. 2019

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Active distribution networks (ADNs) are a typical cyber-physical system (CPS), which consist of two kinds of interdependent sub-networks: power networks (PNs) and communication networks (CNs). The combination of typical characteristics of the ADN includes (1) a large number of distributed generators contained in the PN, (2) load redistribution in both the PN and CN,and (3) strong interdependence between the PN and CN, which makes ADNs vulnerable to cross-domain cascading failures (CCFs). In this paper, we focus on the robustness analysis of the ADN against the CCF. Rather than via the rate of the clusters with size greater than a predefined threshold, we evaluate the robustness of the ADN using the rate of the clusters containing generators after the CCF. Firstly, a synchronous probabilistic model is derived to calculate the proportions of remaining normal operational nodes after the CCF. With this model, the propagation of the CCF in the ADN can be described as recursive equations. Secondly, we analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of distributed generators, unintentional random initial failure rate, the interdependence between the sub-networks, network topology, and tolerance parameters. Some results are revealed which include (1) the more distributed generators the PN contains, the higher ADN robustness is, (2) the robustness of the ADN is negatively correlated with the unintentional random initial failure rate, (3) the robustness of the ADN can be improved by increasing the average control fan in of each node in the PN and the average power fan in of each node in the CN, (4) the robustness of the ADN with Erdos-Renyi (ER) network topological structure is greater than that with Barabasi-Albert (BA) network topological structure under the same average node degree, and (5) the robustness of the ADN is greater, when the tolerance parameters increase. Lastly, some simulation experiments are conducted and experimental results also demonstrate that the conclusions above are effective to improve the robustness of the ADN against the CCF.Appl. Sci. 2019, 9, 5021 2 of 32 electricity [3]. As shown in Figure 1, the ADN is a large complex network that consists of two interdependent sub-networks: power networks (PNs) and communication networks (CNs) [4]. The PN is composed of the power equipment, such as generators, transmission lines, substations, and loads, etc. The CN is composed of the information equipment, such as sensors, computers, communication lines, data storage, and actuators, etc. All these equipment are interconnected according to a certain topological structure. Some nodes in the PN supply power to the nodes in the CN, meanwhile some nodes in the CN collect information of the nodes in the PN and thus to control the actions of them. In general, the operational process of the ADN includes the physical process and the computational process. The physical process can be described by the continuous change of the PN parameters (e.g....

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“…Recently, interdependent networks with different types of dependent links have drawn a lot of attention. [1][2][3][4][5][6][7][8][9][10][11] The initial investigations of network properties are mainly on single networks, which are viewed as independent and do not exchange any information with other networks. [12][13][14][15][16][17][18][19][20][21] However, the real systems are always coupled or are interdependent on each other to function.…”

Section: Introductionmentioning

confidence: 99%

Robustness on interdependent networks with a multiple-to-multiple dependent relationship

Dong

Chen

Wang

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Interdependent networks as an important structure of the real system not only include one-to-one dependency relationship but also include more realistic undirected multiple interdependent relationship. The study on interdependent networks plays an important role in designing more resilient real systems. Here, we mainly focus on the case of interdependent networks with a multiple-to-multiple correspondence of interdependent nodes by generalizing feedback and nonfeedback conditions. We develop a new mathematical framework and study numerically and analytically the percolation of interdependent networks with partial multiple-to-multiple dependency links by using percolation theory. By analyzing the process of cascading failure, the size of the giant component and the critical threshold are analytically obtained and testified by simulation results for couple Erdös-Rėnyi and scale-free networks. The results imply that the system shows a discontinuous phase transition for different coupling strengths. We find that the system becomes more resilient and easy to defend by increasing the coupling strength and the connectivity density. Our model has the potential to shed light on designing more resilient real-world dependent systems.Published under license by AIP Publishing. https://doi.

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Robustness of Interdependent Power Grids and Communication Networks: A Complex Network Perspective

Cited by 215 publications

References 16 publications

Sink‐Convergence Cascading Model for Wireless Sensor Networks with Different Load‐Redistribution Schemes

Sink‐Convergence Cascading Model for Wireless Sensor Networks with Different Load‐Redistribution Schemes

Cyber–Physical Active Distribution Networks Robustness Evaluation against Cross-Domain Cascading Failures

Robustness on interdependent networks with a multiple-to-multiple dependent relationship

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