Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors
Article
Li, S., Chen, Y., Wu, X., Cheng, X. and Tian, Z. 2021. Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors. Journal of Sensors. 2021, pp. 1-12. https://doi.org/10.1155/2021/8820413
| Type | Article |
|---|---|
| Title | Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors |
| Authors | Li, S., Chen, Y., Wu, X., Cheng, X. and Tian, Z. |
| Contributors | Ahmad, I. |
| Abstract | In our paper, we study the vulnerability in cascading failures of the real-world network (power grid) under intentional attacks. Here, we use three indexes (B, K, k-shell) to measure the importance of nodes; that is, we define three attacks, respectively. Under these attacks, we measure the process of cascade effect in network by the number of avalanche nodes, the time steps, and the speed of the cascade propagation. Also, we define the node’s bearing capacity as a tolerant parameter to study the robustness of the network under three attacks. Taking the power grid as an example, we have obtained a good regularity of the collapse of the network when the node’s affordability is low. In terms of time and speed, under the betweenness-based attacks, the network collapses faster, but for the number of avalanche nodes, under the degree-based attack, the number of the failed nodes is highest. When the nodes’ bearing capacity becomes large, the regularity of the network’s performances is not obvious. The findings can be applied to identify the vulnerable nodes in real networks such as wireless sensor networks and improve their robustness against different attacks. |
| Publisher | Hindawi |
| Journal | Journal of Sensors |
| ISSN | 1687-725X |
| Electronic | 1687-7268 |
| Publication dates | |
| 28 Jun 2021 | |
| Publication process dates | |
| Deposited | 07 Jul 2021 |
| Accepted | 22 May 2021 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | Copyright © 2021 Shudong Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Digital Object Identifier (DOI) | https://doi.org/10.1155/2021/8820413 |
| Language | English |
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