The Improvement of the Small-world Network Model and Its Application Research in Bulk Power System

Ding, Jian; Bai, Xiaoqing; Zhao, Wei; Fang, Zhu; Li, Zaihua; Liu, Min

doi:10.1109/icpst.2006.321710

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…Han et al [48] investigate the topological characteristics of two Power Grids sections belonging to the Chinese High Voltage network: the Anhui Power Grid (84 nodes and 112 edges) and the bigger East China Power Grid (769 nodes and 1029 edges). Also focused on the Chinese High Voltage Grid is the work of Ding et al [51] analyze the topological properties of two samples of the Chinese High Voltage Power Grid. In particular, the Sichuan-Chongqing Grid (724 nodes and 771 edges) and the Guangdong Grid (956 nodes and 1049 edges).…”

Section: Basic Power Grid Characteristicsmentioning

confidence: 99%

“…Ding et al [51] in addition to compute the usual metrics to evaluate the satisfaction of the small-world property for the two Chinese Grid under investigation, concentrate their efforts on modifying and adapting the small-world model to consider a weighted graph. They adapt the clustering coefficient by weighting the traditional value of clustering coefficient by the ratio between the average impedence of the line in the neighborhood of the considered node and the average impedence of all the lines in the network.…”

Section: 3mentioning

confidence: 99%

“…Here we describe the most important aspects of each work under investigation. In particular, the works that are considered in this review are: [31,32,33,34,35,36,37,38,39,24,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62]. These have been chosen based on the following factors: they are specifically about the Power Grid, they cover US, European, Chinese Grids or synthetic topologies of electrical engineering literature, they have samples of different sizes and, most importantly, these are the best-known and most representative works on the topic of CNA and Power Grid.…”

Section: The Power Grid As a Complex Networkmentioning

confidence: 99%

“…In [48] weights are represented by the reactance of the transmission lines which are assigned as the values of the adjacency matrix representing the transmission lines. Another study that uses weights assigned to the edges representing the impedence of the transmission line is [51] where two samples of the High Voltage Chinese Grid are analyzed. Other examples that take into account weights are [52,53] where the the graph representing the network is considered directed, since the Power Grid is assumed in a steady state, and the weights are represented by the admittance matrix characterizing the lines.…”

Section: Statistical Global Graph Propertiesmentioning

confidence: 99%

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The Power Grid as a complex network: A survey

Pagani

Aiello

2013

Physica A: Statistical Mechanics and its Applications

647

361

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The statistical tools of Complex Network Analysis are of great use to understand salient properties of complex systems, may these be natural or pertaining human engineered infrastructures. One of these that is receiving growing attention for its societal relevance is that of electricity distribution. In this paper, we present a survey of the most important scientific studies investigating the properties of several Power Grids infrastructures using Complex Network Analysis techniques and methodologies. We categorize and explore the most relevant literature works considering general topological properties, differences between the various graph-related indicators and reliability aspects. Example:The graph G shown in Figure 1 is characterized by the set of vertexes V : V = {1, 2, 3, 4, 5} and by the set of edges E: E = {(1, 2), (1, 5), (2, 3), (2, 5), (3, 4)} the order and size of G are |V | = 5 and |E| = 5 respectively.An important property that characterizes a vertex of a graph is the degree, that is the number of vertexes the node is adjacent to. More formally, this quantity is defined as:Definition 4 (Neighborhood and degree). The set of vertexes adjacent to a vertex v ∈ V represents the neighborhood of v that is denoted byFrom order and size it is possible to have a global value for the connectivity of the vertexes of the graph, known as average node degree . That is < k >= 2M N .

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Section: Basic Power Grid Characteristicsmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

Section: The Power Grid As a Complex Networkmentioning

confidence: 99%

Section: Statistical Global Graph Propertiesmentioning

confidence: 99%

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The Power Grid as a complex network: A survey

Pagani

Aiello

2013

Physica A: Statistical Mechanics and its Applications

647

361

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“…Although the above network models have been applied in real-world network optimization, industrial networks cover a wider range. For example, air traffic network [29] [30], power network [19] [31][32][33] occupies an important position in industrial network optimization. Networks between different industrial categories are quite different.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Network Models applied in the Industrial Network Optimization

Chen¹,

Xiong²,

Xue³

et al. 2022

Preprint

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Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric -system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

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