High-risk areas identification of transmission lines based on historical warning information

Abstract

With the upgrade of transmission line maintenance technology, the visual remote inspection of transmission line channel is widely used. However, the application of these marked data is currently in the stage of statistical analysis and report form, a deeper data mining work has not been carried out, such as the identification of areas with high incidence of alarm. This paper presents a method of analysis of high risk area of transmission line channel based on historical early warning data, which can be applied to the field of transmission line maintenance. By preprocessing the transmission line visual alarm data, using the improved k-means algorithm, analyze the clustering results, and finding out the series of modeling and data analysis. In addition, when determining the initial points, we propose the maximum and minimum longitude and latitude coordinates of the alarm dataset, divide a certain number of longitude and latitude grid, obtain the candidate data within each grid, and the method of obtaining the initial point set after screening. Based on the initial point set, the alarm area distribution is reasonable, and the regional stability does not drift. This paper can identify the visual alarm data of transmission lines with high alarm incidence areas, and provide effective data support for maintenance personnel to guide the deployment of human resources and ensure the safe operation of transmission lines.