Correlation analysis between wind-induced vibration disasters and influencing factors of transmission lines based on machine learning technology

Abstract

With the continuous expansion of power grid scale and the improvement of power grid informatization level, China has accumulated a certain amount of wire wind vibration data in years of transmission line operation, including structured and unstructured data such as text, statistical charts, images, videos, etc, providing strong data support for data-driven wire wind vibration disaster mechanism mining. Therefore, it is necessary to integrate multi-disciplinary knowledge such as computer, mathematics, information processing, mechanics, and biology based on data mining and digital machine learning techniques, effectively mine and deeply analyze the historical records of wind induced vibration disasters on transmission lines, meteorological and geographical environmental parameter information, multi-source and wide-area wind induced vibration monitoring information, and line structural parameters, in order to clarify the main influencing factors and disaster mechanisms of wind induced vibration disasters on transmission lines, Improve the prevention and control technology of wind induced vibration disasters on transmission lines, enhance the identification level of wind induced vibration disasters in power grids, and provide decision-making support for the safe operation of power grids.