Node Importance Research of Temporal CPPS Networks for Information Fusion

被引:1
作者
Li, Yan [1 ]
Zhao, Ying [2 ]
Xu, Tianqi [1 ]
Wu, Senlin [3 ]
机构
[1] Yunnan Minzu Univ, Key Lab Cyber Phys Power Syst, Yunnan Coll & Univ, Kunming 650500, Peoples R China
[2] Yunnan Power Grid Co Ltd, Kunming Adm Power Supply, Kunming 650011, Peoples R China
[3] Nanjing Daqo Elect Co Ltd, Nanjing 211106, Peoples R China
基金
中国国家自然科学基金;
关键词
Communication networks; Indexes; Couplings; Time series analysis; Correlation; Artificial neural networks; Power system reliability; Information fusion; susceptible-infected-recovered (SIR) model; temporal cyber-physical power system (CPPS) network; temporal characteristic index; LINK PREDICTION;
D O I
10.1109/TR.2023.3329124
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In space-time evolution, a temporal network can more accurately describe the relationship between power network nodes and communication network nodes in a cyber-physical power system (CPPS) network. Critical nodes are special nodes that affect the structure and function of the network to a greater extent than other nodes in the network. In this article, the critical nodes of the power network in a temporal CPPS network are identified from the perspective of network topology and information fusion, and a dynamic susceptible-infected-recovered (SIR) model is used to evaluate node importance research on temporal CPPS networks. First, a temporal information fusion model is established based on an association degree matrix of the temporal network, and a temporal association index is defined to identify the critical nodes of the temporal CPPS network. Second, based on the interlayer temporal coupling relationship of the temporal CPPS network, a static network structure characteristic index is expanded, and a temporal characteristic index is defined to sort the node importances. Finally, an SIR model is used to verify that the node importance research based on the temporal network has high accuracy. The node importance assessment method based on temporal network information fusion is more effective.
引用
收藏
页码:1291 / 1301
页数:11
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