Support Vector Machine-Based Impact Analysis of Multidimensional Ice-Covering Factors

In recent years, snow and ice disasters have occurred frequently, and the icing of transmission lines poses a great threat to power grid operation. Therefore, it is very important to analyze and study the icing factors of transmission lines and get the icing law. In this paper, we first analyze the correlation between various meteorological factors and icing degree based on Python and use principal component analysis to determine the key influencing factors of transmission line icing as the input of the icing classification model and use the support vector machine to classify the icing data, and obtain the classification results with high accuracy, and summarize the general law of transmission line icing affected by meteorological factors, which provides the basis for transmission line operation and maintenance. The results of the classification are highly accurate.