PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 6, 2015IEEE Transactions on Smart Grid244 citations

Cascading Failure Analysis Considering Interaction Between Power Grids and Communication Networks

View Full Paper
YCYe CaiYCYijia CaoYLYong Li

Key Points

Key points are not available for this paper at this time.

Abstract

This paper aims to model interdependencies between power systems and dispatching data networks, and to analyze the intricate impacts on cascading failures. The functions of communication networks are embedded into dispatching data networks in China, thus we use dispatching data networks in the paper. The structures of dispatching data networks are generally categorized into two types: 1) double-star; and 2) mesh. The correlation of nodes in double-star networks and power systems is “degree to degree,” whereas “degree to betweenness” is the correlation for mesh networks. Furthermore, the interactive model between power grids and dispatching data networks is presented by a dynamic power flow model. Taking the IEEE 39-bus system and China's Guangdong 500-kV system as examples, in the case of random attacks on the interdependent system, simulation results show that the power grid coupled with double-star dispatching data networks has lower probability of catastrophic failures than with the mesh structure, because the double-star dispatching data network has outstanding capability of delivering information even though some communication nodes are out of order. In contrast, under intentional attacks, the decrement of the transmission performance of the double-star network is more serious than that in the mesh network. Therefore, the power system exhibits much higher vulnerability when coupled with the double-star network.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Cai et al. (2015) studied this question.

synapsesocial.com/papers/6a4110ace0c93843bc080fafhttps://doi.org/10.1109/tsg.2015.2478888
Ask AI
Helpful
Bookmark
Share
View Full Paper