Understanding the cascading failures in Indian power grids with complex networks theory

2013 ◽  
Vol 392 (15) ◽  
pp. 3273-3280 ◽  
Author(s):  
Guidong Zhang ◽  
Zhong Li ◽  
Bo Zhang ◽  
Wolfgang A. Halang
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Adnan ◽  
Muhammad Gufran Khan ◽  
Arslan Ahmed Amin ◽  
Muhammad Rayyan Fazal ◽  
Wen Shan Tan ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Franz Kaiser ◽  
Vito Latora ◽  
Dirk Witthaut

AbstractIn our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demostrate that not only decreasing but also increasing the connectivity of the network can be an effective method to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.


Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1994
Author(s):  
Yanchen Liu ◽  
Minfang Peng ◽  
Xingle Gao ◽  
Haiyan Zhang

The prevention of cascading failures and large-scale power outages of power grids by identifying weak links has become one of the key topics in power systems research. In this paper, a vulnerability radius index is proposed to identify the initial fault, and a fault chain model of cascading failure is developed with probabilistic attributes to identify the set of fault chains that have a significant impact on the safe and stable operation of power grids. On this basis, a method for evaluating the vulnerability of transmission lines based on a multi-criteria decision analysis is proposed, which can quickly identify critical transmission lines in the process of cascading failure. Finally, the proposed model and method for identifying vulnerable lines during the cascading failure process is demonstrated on the IEEE-118 bus system.


2017 ◽  
Vol 7 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Stéphane Chrétien ◽  
◽  
Sébastien Darses ◽  
Christophe Guyeux ◽  
Paul Clarkson ◽  
...  

2018 ◽  
Vol 33 (6) ◽  
pp. 6013-6024 ◽  
Author(s):  
Zhuoyao Wang ◽  
Mahshid Rahnamay-Naeini ◽  
Joana M. Abreu ◽  
Rezoan A. Shuvro ◽  
Pankaz Das ◽  
...  

2021 ◽  
pp. 717-723
Author(s):  
Hao Shen ◽  
Shiwen Sun ◽  
Jin Zhang ◽  
Chengyi Xia

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.


2021 ◽  
Vol 147 (1) ◽  
pp. 06020001
Author(s):  
Chao Zhai ◽  
Gaoxi Xiao ◽  
Min Meng ◽  
Hehong Zhang ◽  
Beibei Li

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