Research on Transmission Line Icing Monitoring, Early Warning and Risk Assessment Technology Based on Numerical Weather and Machine Learning

MENG Lingbo; ZHU Hongjie; YANG Lanjun; DONG Lifan; CHEN Liyan

doi:10.16516/j.ceec.2025-330

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Research on Transmission Line Icing Monitoring, Early Warning and Risk Assessment Technology Based on Numerical Weather and Machine Learning

更新时间：2026-10-14

- Research on Transmission Line Icing Monitoring, Early Warning and Risk Assessment Technology Based on Numerical Weather and Machine Learning
- Southern Energy Construction Vol. 13, Issue 3, Pages: 56-68(2026)
- 作者机构：
  
  1. 西安交通大学电气工程学院,陕西,西安,710048
  2. 国网宝鸡供电公司,陕西,宝鸡,721000
  3. 咸阳市气象局,陕西,咸阳,712000
- 作者简介：
- 基金信息：
- DOI：10.16516/j.ceec.2025-330
  CLC： TM7;P49
- Published：2026
- 稿件说明：
移动端阅览
孟令博, 朱红杰, 杨兰均, 等. 基于图像识别和数值天气预报的输电线覆冰智能监测预警及风险评估[J]. 南方能源建设, 2026, 13(3): 56-68.

.
孟令博, 朱红杰, 杨兰均, 等. 基于图像识别和数值天气预报的输电线覆冰智能监测预警及风险评估[J]. 南方能源建设, 2026, 13(3): 56-68. DOI： 10.16516/j.ceec.2025-330.

. DOI： 10.16516/j.ceec.2025-330.

摘要

目的

为解决输电线路覆冰导致的设备故障与电网安全问题，以宝鸡翻越秦岭区域线路为研究对象，开展覆冰监测、预测与风险评估技术研究。

方法

监测环节采用机器视觉技术，通过图像预处理、导线像素提取和宽度测量算法，结合“观冰精灵”图像传感器，实现覆冰厚度实时监测；预测环节构建周边气象站数据库，基于Haversine公式获取匹配度高的气象数据，基于插值法对气象数据进行精细化处理，将处理后数据输入WRF数值天气模型模拟关键气象要素，用模拟结果驱动Makkonen覆冰模型计算覆冰厚度增长量，结合实时监测值得到预测结果；风险评估环节融合覆冰跳闸样本、杆塔静态数据及气象数据，经特征工程处理后构建XGBoost模型，设计差异化动态预警阈值。最终，基于Python实现以上3个模块功能，并开发可视化监测预警与风险评估系统。

结果

研究表明，覆冰厚度监测结果与人工测量结果对比误差≤3%，覆冰厚度预测结果误差≤5%；风险评估方面，能实现1～3 h和3～6 h覆冰跳闸概率精准预测与分级预警。

结论

开发的可视化监测预警与风险评估系统，能够集成监测、预测、评估3个功能，为宝鸡电网输电线智能运维提供决策支持，有效降低覆冰灾害损失。

Abstract

Objective

To address the equipment failure and grid safety issues caused by ice accumulation on transmission lines

this study takes the lines crossing the Qinling Mountains in Baoji as the research object and conducts research on ice accumulation monitoring

prediction

and risk assessment technologies.

Method

In the monitoring phase

machine vision technolo

gy was adopted. Through image preprocessing

conductor pixel extraction

and width measurement algorithms

combined with the "Ice Observation Spirit Spirit" image sensor

real-time monitoring of ice thickness was achieved. In the prediction phase

a database of surrounding meteorological stations was constructed. Based on the Haversine formula

meteorological data with high matching degrees were obtained. Then

the meteorological data were refined through interpolation methods. The processed data were input into the WRF numerical weather model to simulate key meteorological elements. The simulation results drove the Makkonen ice accumulation model to calculate the increase in ice thickness. Combined with real-time monitoring values

the prediction results were obtained. In the risk assessment phase

ice accumulation tripping samples

tower static data

and meteorological data were integrated. After feature engineering processing

an XGBoost model was constructed

and differentiated dynamic early warning thresholds were designed. Ultimately

the functions of the three modules were implemented based on Python

and a visualization monitoring

early warning

and risk assessment system was developed.

Result

The research shows that the error between the ice thickness monitoring results and the manual measurement results is ≤ 3%

and the error of the ice thickness prediction results is ≤ 5%. In terms of risk assessment

it can accurately predict the probability of ice accumulation tripping within 1~3 hours and 3~6 hours and issue graded early warnings.

Conclusion

The developed visualization monitoring

early warning

and risk assessment system can integrate monitoring

prediction

and assessment functions

providing decision support for the intelligent operation and maintenance of transmission lines in the Baoji power grid and effectively reducing the losses caused by ice accumulation disasters.

关键词

Keywords

references

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