Analysis of heavy overload transmission section based on association rules and complex networks

This paper aims to use the machine learning algorithm and complex network analysis technology to mine information on heavy overload transmission sections. Through the statistical description of the heavy overload sections of one region in Chinese power grid, the distribution law according to different time characteristic is found. The association rules are used to mine the information between the transmission sections, and the network is established by taking the transmission section as node. The time series of the node state is determined according to whether the section is heavy overload, and the similarity matrix is constructed by using Pearson similarity and partial correlation coefficient respectively. The results of data analysis show that the association rules and the complex network analysis reveal the internal relationship from different aspects between the sections, which provides strong support for grid operation and maintenance. This paper also expands the application of big data analysis algorithms in smart grid.