Research on Galloping Characteristics of Iced Overhead Conductor and Related Early Warning Technology

王伟; 郭晨晨; 刘志明; 李志鹏; 常计东; 李沛原; 李琪冉

doi:10.3969/j.issn.1008-0198.2025.06.006

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Research on Galloping Characteristics of Iced Overhead Conductor and Related Early Warning Technology

更新时间：2026-02-06

- Research on Galloping Characteristics of Iced Overhead Conductor and Related Early Warning Technology
- Vol. 45, Issue 6, Pages: 42-49(2025)
- 作者机构：
  
  国网冀北电力有限公司唐山供电公司,河北,唐山,063000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.06.006
  CLC： TM752.5
- Online First：13 January 2026，
  
  Published：2025
- 稿件说明：
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王伟, 郭晨晨, 刘志明, 李志鹏, 常计东, 李沛原, 李琪冉. 架空导线覆冰舞动特性与预警技术研究[J]. 湖南电力, 2025, 45(6): 42-49.

王伟, 郭晨晨, 刘志明, et al. Research on Galloping Characteristics of Iced Overhead Conductor and Related Early Warning Technology[J]. 2025, 45(6): 42-49.
王伟, 郭晨晨, 刘志明, 李志鹏, 常计东, 李沛原, 李琪冉. 架空导线覆冰舞动特性与预警技术研究[J]. 湖南电力, 2025, 45(6): 42-49. DOI： 10.3969/j.issn.1008-0198.2025.06.006.

王伟, 郭晨晨, 刘志明, et al. Research on Galloping Characteristics of Iced Overhead Conductor and Related Early Warning Technology[J]. 2025, 45(6): 42-49. DOI： 10.3969/j.issn.1008-0198.2025.06.006.

摘要

针对覆冰架空导线舞动问题

基于有限元仿真方法建立覆冰导线的三维动力学模型

系统分析不同风速、风向及气流攻角下导线的舞动机械与电气特性。研究发现

覆冰厚度与风速是影响舞动最敏感的参数。在此基础上

提出一套融合5G通信、传感器监测与智能分类算法的舞动预警体系

采用支持向量机模型预测区域易舞气象条件

并结合AdaBoost分类器实现线路舞动风险分级预警。该体系具备多源感知与智能决策能力

有效提高了电网对舞动灾害的预警精度与防控水平。

Abstract

Aiming at the galloping problem of iced overhead conductors

a three-dimensional dynamic model of iced conductors is established based on the finite element simulation method

and the mechanical and electrical characteristics of conductor galloping under different wind speeds

wind directions

and airflow attack angles are systematically analyzed. The study finds that ice thickness and wind speed are the most sensitive parameters affecting galloping. On this basis

a galloping early warning system integrating 5G communication

sensor monitoring

and intelligent classification algorithms is proposed. The system employs an support vector machine (SVM) model to predict meteorological conditions prone to galloping in the area and combines an AdaBoost classifier to achieve a graded early warning for transmission line galloping risk. This system

with its multi-source perception and intelligent decision-making capabilities

effectively enhances the accuracy in early warning and prevention and control levels of the power grid against galloping disasters.

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references

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