Assessment method of vulnerable communities in power grid considering cyber-physical integration

被引:0
作者
Wang X. [1 ]
Wang B. [1 ]
机构
[1] School of Electrical Engineering, Wuhan University, Wuhan
来源
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2017年 / 37卷 / 12期
基金
中国国家自然科学基金;
关键词
Community theory; Cyber-physical system; Interdependent network; Robustness; Vulnerability assessment; Vulnerable communities;
D O I
10.16081/j.issn.1006-6047.2017.12.006
中图分类号
学科分类号
摘要
In view of the high computational complexity and vulnerability protection configuration difficulties when the vulnerability assessment of grid cyber-physical system of component level is applied to an actual large-scale power cyber-physical system, an assessment method of vulnerable communities in power grid considering cyber -physical integration is proposed, considering the mesoscopic local characteristics and community structure of networks. Based on Fast Unfolding algorithm, the power grid is divided into several communities taking power flow as edge weights, the communities of communication network is divided according to the hierarchical partition status and the actual coupling relationship between the power grid and the communication network. Simulative results of IEEE standard system prove the superiority of the community partition method. Under different coupling connections, each community in the power grid is attacked by different strategies, and the vulnerable communities of the power grid are assessed based on the ratio of maximum connected subset with fault in the cyber-physical system. The assessment method conforms to the present hierarchical partition status in the power grid and the communication network and can reduce the calculation complexities and difficulties of vulnerability protection configuration. Simulative results of 500 kV power grid cyber-physical system in Central China verify the feasibility of the proposed method. © 2017, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码:43 / 51
页数:8
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