Research on Data Mining of Wind Disaster of Power Transmission Line Based on Clustering Analysis

This Paper conducts effective mining to wind vibration disaster data of power transmission line conductor by using the clustering analysis method. The main influential factors which cause different types of wind vibration disasters are analyzed. This paper laying a foundation for the subsequent correlation analysis and machine learning model. On the basis of K-Means method, the significant degrees of different influential factors on forecast of galloping, wind deviation and sub-span oscillation disaster are studied. The results show that: the span, conductor icing thickness and wind direction are main influential factors of conductor galloping; the included angle between wind and conductor and span are main influential factors of insulator wind vibration; the span, spacer span, and number of spacers are main influential factors of subspan oscillation.