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

Jingshan Han; Hongzhi Su; Lei Zhang; Yi Wang; Guiqing Liu; Zhongyang Xu

doi:10.1117/12.3025862

1 April 2024 Correlation analysis between wind-induced vibration disasters and influencing factors of transmission lines based on machine learning technology

Jingshan Han, Hongzhi Su, Lei Zhang, Yi Wang, Guiqing Liu, Zhongyang Xu

Author Affiliations +

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810V (2024) https://doi.org/10.1117/12.3025862
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingshan Han, Hongzhi Su, Lei Zhang, Yi Wang, Guiqing Liu, and Zhongyang Xu "Correlation analysis between wind-induced vibration disasters and influencing factors of transmission lines based on machine learning technology", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810V (1 April 2024); https://doi.org/10.1117/12.3025862

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KEYWORDS

Vibration

Data modeling

Data transmission

Binary data

Humidity

Machine learning

Ice

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