Importance evaluation of power network nodes based on community division and characteristics of coupled network

Liu, Fangyu; Xie, Gang; Zhao, Zhengpeng

doi:10.1016/j.epsr.2022.108015

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…0 ij w and 1 ij w are represented as the reliability weights of two relevant topological nodes, which represent the reliability of i v to node j v in the transmission topology when there is no anomaly or when there is an anomaly. This weight can prevent switching between the above two states of the transmitted signal, so as to eliminate the interference of the signal and obtain new reliability updates based on the detection results [12]. If no exception occurs, the topology node that is abnormal or reported as 1 loses its weight.…”

Section: Abnormal Topology Node Miningmentioning

confidence: 99%

Anomaly attribute mining method for topological nodes of power system based on graph theory

Tan,

Lu,

Zhao

et al. 2024

J. Phys.: Conf. Ser.

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Due to the large scale, high dimension and time series characteristics of power system data, and the normal samples far exceed the abnormal samples, the sample imbalance phenomenon occurs, and it is difficult to mine the abnormal attributes of nodes. Therefore, a method of mining abnormal attributes of topological nodes of power systems based on graph theory is developed. The logical relation between nodes is analyzed by graph theory, and directed graph and undirected graph are obtained. The topology structure of the power system is constructed, the noise in data is removed by the adaptive clustering algorithm, the weight of topological nodes is set, and the abnormal malicious attacks of topological nodes are discovered by the double threshold method. The experimental results show that the proposed method can accurately detect the abnormal topological nodes in the network, perform better in the energy consumption of the neighbor topological nodes, and greatly reduce the energy consumption of the neighbor topological nodes during mining.

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Section: Abnormal Topology Node Miningmentioning

confidence: 99%

Anomaly attribute mining method for topological nodes of power system based on graph theory

Tan,

Lu,

Zhao

et al. 2024

J. Phys.: Conf. Ser.

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“…In real networks, a relatively good association division can correctly reflect the actual connectivity characteristics of the network and thus be able to accurately predict the association division in case of network evolution. The most popular ones are the modularity function and the mutual information function [26][27].…”

Section: Associational Effectsmentioning

confidence: 99%

Research on the Era Value and Communication Path of Human Destiny Community and Socialist Core Values in the Context of Big Data

Ren,

Yang

2023

Applied Mathematics and Nonlinear Sciences

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This paper analyzes the socialist core values and explores the contemporary characteristics and values of socialist core values under the background of big data. On this basis, in order to better practice the socialist core values, through the communication path of big data, combined with the belief propagation algorithm and the association effect, it constructs a node influence propagation NIP model based on the improvement of knowledge graph to realize the propagation of socialist core values. At the same time, the algorithm constructed in this paper is subjected to performance verification experiments, and the effect of socialist core value dissemination and the value of the times are explored. The results show that the influence range of the national level and the individual level are roughly the same as 1200 and 1190, respectively, both slightly higher than the individual influence range of 1000, and the propagation time of the state, society and individual is within 150s, 200s and 300s respectively. Resistance to Western ideology rose by 0.13, the deepening of socialist theory grew by 0.09, and the process of constructing a world value system increased by 0.06. Based on this paper, the study of the value of time and the path of dissemination has a positive impact on the development of socialism.

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“…Liu et al, explored overlapping clusters in power grids based on a link-based segmentation method and identified the node importance in a singlelayer network by using the Betweenness Centrality Based on Neighbor Nodes (BCBNN) algorithm. Then, they proposed a node importance identification method that considered the single-layer network topology, grid overlap structure, and dual-grid coupling [26]. Some results have been achieved by assessing the importance of nodes in coupled networks, but entropy-based metrics are still missing when considering the global information of nodes.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Zeng,

Sun,

Zhang

2024

Entropy

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Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric’s performance in important node identification is greatly improved, and it demonstrates good network resilience.

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Importance evaluation of power network nodes based on community division and characteristics of coupled network

Cited by 9 publications

References 11 publications

Anomaly attribute mining method for topological nodes of power system based on graph theory

Anomaly attribute mining method for topological nodes of power system based on graph theory

Research on the Era Value and Communication Path of Human Destiny Community and Socialist Core Values in the Context of Big Data

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

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