Automatic Detection Method of Power Communication Network Breakpoint Data under Cloud Computing

Abstract

This paper presents two methods based on neural network and clustering analysis are used to automatically detect break-point data of power communication network. It has a low recall rate and hinders the realization of power network planning objectives. In view of the above situation, an automatic detection method of power communication network break-point data under cloud computing is studied. This method is divided into four parts. Firstly, the connection model is constructed by the omni-directional construction mode of FP-tree micro-cellular, then the break-point data signal is collected by network tracker. In addition, the break-point data signal is filtered by wavelet filtering method, and finally the break-point data of power communication network is automatically detected by firefly algorithm. The results show that, compared with the two detection methods based on neural network and clustering analysis. Additionally, the recall rate of this detection method is increased by 3.1% and 2.27%, basically realizing the goal of power grid planning.