Complex Network Properties of Chinese Power Grid

Xu, Tian; Chen, Jie; He, Daihai

doi:10.1142/s0217979204025749

Cited by 37 publications

(21 citation statements)

References 4 publications

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“…Examples are numerous: these include the Internet [15,16,17], the World Wide Web [18,19,20,21,22], social networks of acquaintance or other relations between individuals [23,24,25,26,27,28,29,30,31,32,33], metabolic networks [34,35,36,37,38], food webs [39,40,41,42,43,44,45] and many others [46,47,48,49,50,51,52,53,54,55,56,57]. The ubiquity of complex networks inspires scientists to construct a general model.…”

Section: Introductionmentioning

confidence: 99%

Maximal planar networks with large clustering coefficient and power-law degree distribution

2005

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In this article, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called Random Apollonian Networks(RAN) as they can be considered as a variation of Apollonian networks. We obtain the analytic results of power-law exponent γ = 3 and clustering coefficient C = 46 3 − 36ln 3 2 ≈ 0.74, which agree very well with the simulation results. We prove that the increasing tendency of average distance of RAN is a little slower than the logarithm of the number of nodes in RAN. Since most reallife networks are both scale-free and small-world networks, RAN may perform well in mimicking the reality. The RAN possess hierarchical structure as C(k) ∼ k −1 that in accord with the observations of many real-life networks. In addition, we prove that RAN are maximal planar networks, which are of particular practicability for layout of printed circuits and so on. The percolation and epidemic spreading process are also studies and the comparison between RAN and Barabási-Albert(BA) as well as Newman-Watts(NW) networks are shown. We find that, when the network order N (the total number of nodes) is relatively small(as N ∼ 10 4 ), the performance of RAN under intentional attack is not sensitive to N , while that of BA networks is much affected by N . And the diseases spread slower in RAN than BA networks during the outbreaks, indicating that the large clustering coefficient may slower the spreading velocity especially in the outbreaks.

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Section: Introductionmentioning

confidence: 99%

Maximal planar networks with large clustering coefficient and power-law degree distribution

2005

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“…Real network mostly has big cluster coefficient and degree distribution index is not only equal to 3. It is located between [2,3].A variety of subtle changes are likely to affect network topology during the evolution process of real network, The evolution of different real networks is very different subjected to factors such as cost, competition and other factors. It is necessary to establish a specific network model for different realistic networks.…”

Section: Advances In Engineering Research Volume 113mentioning

confidence: 99%

The Research of Complex Networks and Its Modeling

Jiao¹,

Han²,

Pei-zhang³

et al. 2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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“…With the discovery of small-world networks [11] and scale-free networks [12], the theory has been widely used in various fields of scientific research. A lot of studies show that power system, as one of the largest and most complex man-made network, also has the characteristics of small-world [13][14][15], the theory of which has been applied to the cascading failure analysis [16,17]. In this paper, complex network theory is used for analysing the transmission network in order to solve the sub-problems of identifying transmission sections.…”

Section: Complex Network Theory-based Automatic Identification Of Tramentioning

confidence: 99%

Automatic identification of transmission sections based on complex network theory

Luo

Shi

Chen

et al. 2014

IET Generation, Transmission & Distribution

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Taking transmission sections as the monitoring objects of power system security and stability level can largely improve the efficiency of analysis and control in power system operation. However, existing approaches for identifying transmission sections mainly depend on years of experience, which is not suitable for complicated and variable power systems with large scales. Thus, a novel method for automatic identification of transmission sections using complex network theory is proposed. The proposed method presents the fundamental conditions of transmission sections and identifies them from three levels: transmission lines, key transmission links and partition sections. First, based on the small-world characteristics of power grid, the index of transmission betweenness is presented to identify key transmission links from transmission lines. Then clustering algorithm of complex network is used to divide the power grid and to obtain partition sections from the key transmission links. Finally, the combinations of partition sections are selected and ranked as the transmission sections. Numerical results with CEPRI-36 system and a provincial system are provided to demonstrate the effectiveness and adaptability of the proposed method.

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Complex Network Properties of Chinese Power Grid

Cited by 37 publications

References 4 publications

Maximal planar networks with large clustering coefficient and power-law degree distribution

Maximal planar networks with large clustering coefficient and power-law degree distribution

The Research of Complex Networks and Its Modeling

Automatic identification of transmission sections based on complex network theory

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