Impact of Topology on the Propagation of Cascading Failure in Power Grid

Dey, Priyanka; Mehra, Rachit; Kazi, Faruk; Wagh, Sushama; Singh, Navdeep

doi:10.1109/tsg.2016.2558465

Cited by 103 publications

(38 citation statements)

References 32 publications

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“…Cascading failures caused by load (re-)distribution among the nodes are studied in literature (e.g., [20], [21], [22]), in which the load of the nodes are independent/decoupled to each other and load distribution only occurs upon failures of the nodes. However, in our model, there is a constant coupling between resources provided from the supply nodes and the load of the demand nodes.…”

Section: Load-based Cascading Failuresmentioning

confidence: 99%

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Hosseinalipour

Mao

Eun

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.

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Section: Load-based Cascading Failuresmentioning

confidence: 99%

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Hosseinalipour

Mao

Eun

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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“…Complex network anlaysis has been successfully applied to analyse the efficiency of networks, for example, a biology network [10], a research cooperating network [11,12], an electricity supply system [13], a traffic network [4,14], and even an Internet network [15]. Dey et al [16] successfully applied complex network theory to analyse safety and reliability of topology impact on the propagation of cascading failure in a national power grid. Zio and Sansavini [17] also applied complex network method for modelling interdependent network systems in order to identify cascade-safe operating margins.…”

Section: Introductionmentioning

confidence: 99%

Application of Complex Network Principles to Key Station Identification in Railway Network Efficiency Analysis

Wang

Jia

et al. 2019

Journal of Advanced Transportation

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Network efficiency analysis becomes important in railways in order to contribute towards improving the safety and capacity of the rail network, making rail travel more attractive for passengers, and improving industry practice and informing policy development. However, a physical railway network structure is a complicated system, and the operation, maintenance, and management of such a network is a difficult task which may be affected by many influential factors. By using efficiency analysis technology for a railway network, combining physical structure with operation functions can help railway industry to optimize the railway network while improving its efficiency and reliability. This paper presents a new methodology based on complex network principles that combines the physical railway structure with railway operation strategy for a railway network efficiency analysis. In this method, two network models of railway physical and train flow networks are developed for the identification of key stations in the railway network based on network efficiency contribution in which the terms of degree, strength, betweenness, clustering coefficient, and a comprehensive factor are taken into consideration. Once the key stations have been identified and analysed, the railway network efficiency is then studied on the basis of selective and random modes of the station failures. A case study is presented in this paper to demonstrate the application of the proposed methodology. The results show that the identified key stations in the railway network play an important role in improving the overall railway network efficiency, which can provide useful information to railway designers, engineers, operators and maintainers to operate and maintain railway network effectively and efficiently.

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“…In recent years, network science research has attracted a great amount of attention from researchers in different fields including physics, mathematics, chemistry, medical science, biology, computer science, sociology and so on [1][2][3][4][5][6][7][8][9]. In particular, the vulnerability of complex networks is one of the most important directions due to its considerable effect on network cascade failure caused by random failure and deliberate attack [10][11][12][13][14][15][16][17][18]. Random failure can be regarded as a simple abstraction of successive errors in complex networks by destroying nodes or edges with uniform probability.…”

Section: Introductionmentioning

confidence: 99%

Identifying Node Importance in a Complex Network Based on Node Bridging Feature

Jiang

Jing

et al. 2018

Applied Sciences

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Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

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Impact of Topology on the Propagation of Cascading Failure in Power Grid

Cited by 103 publications

References 32 publications

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Application of Complex Network Principles to Key Station Identification in Railway Network Efficiency Analysis

Identifying Node Importance in a Complex Network Based on Node Bridging Feature

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