A community self-organization detection method for power network fault diagnosis

Abstract

The invention discloses a community self-organizing detection method for power network fault diagnosis. The method comprises the steps of firstly, collecting network characteristic parameters of power networks, then describing the power networks as weighted network models, constructing local fitness and global fitness functions, starting from grouped solutions of the power networks, which are generated randomly, calculating local fitness of each power node, sequencing the local fitness, selecting the nodes with the poor local fitness according to an expansion evolution probability distribution function, transferring the nodes with the poor local fitness to another group of networks to generate new solutions, comparing global fitness values of the new solutions and the current solutions, reserving the best solutions in the new solutions and the current solutions, enabling the new solutions to serve as initial solutions for the next iteration to repeat above optimization processes until preset end conditions are met, and finally, analyzing and outputting community self-organizing detection results which are used for power network fault diagnosis. Compared with conventional methods, the method has the advantages of being a few in adjusting parameter, simple in detection process, easy to implement and high in detection efficiency and detection precision.

Description

technical field [0001] The invention relates to the field of safety evaluation and fault diagnosis of smart grid systems, in particular to a community self-organization detection method for power network fault diagnosis. Background technique [0002] As one of the networks with the widest coverage and the most complex structure in the world today, the security of the power network is closely related to the national economy and the people's livelihood. However, frequent large-scale power outages at home and abroad in recent years have exposed the vulnerability of complex large power grids, which have brought extremely catastrophic losses and adverse effects to many countries and regions including North America, Europe, and China. Please refer to Table 1 for the description of its basic situation. Therefore, how to ensure the safe and stable operation of the power grid and realize a strong power grid has become one of the research hotspots in the academic and engineering circ...

AI Technical Summary

Problems solved by technology

However, the existing power network detection methods have the disadvantages of complex detection process, detection efficiency and accuracy are not high enough.

It is particularly noteworthy that there are few reports on the detection of community structures in complex networks, especially in complex power networks, based on extreme value optimization theory.

In 2005, J.Duch and A.Arenas[J.Duch,A.Arenas.Community detection in complex networks using extremal optimization.Physical Review E,2005,72,027104] applied the extreme value optimization theory to the community structure analysis of complex networks for the first time, but only limited to computer random Simple analysis and simulation testing of generated and social networks

On this basis, Chen Guoqiang, Wang Yuping [Chen Guoqiang, Wang Yuping. Complex community detection based on extreme value optimization module density. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011,39(4):81-85] in 2011 An extremum optimization module density method is proposed, but it is only limited to the comparative test of the network randomly generated by the computer and the social network, and the proposed method is only optimized for the node with the worst local fitness, which leads to the The method is easy to fall into the local optimal solution

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