基于空间多源异构数据的台风下输电杆塔风险评估

侯慧; 于士文; 肖祥; 黄勇; 耿浩; 俞菊芳

doi:10.7500/AEPS20191113002

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基于空间多源异构数据的台风下输电杆塔风险评估

研制与开发 | 更新时间：2025-06-16

- 基于空间多源异构数据的台风下输电杆塔风险评估
- Risk Assessment of Transmission Tower in Typhoon Based on Spatial Multi-source Heterogeneous Data
- 电力系统自动化-方正平台 2020年44卷第10期页码：127-134
- 作者机构：
  
  1.武汉理工大学自动化学院，湖北省武汉市 430070
  2.广东电网有限责任公司电力科学研究院，广东省广州市 510080
- 作者简介：
  
  侯慧(1981—)，女，博士，副教授，硕士生导师，主要研究方向：电力系统继电保护、电力系统可靠性与风险评估、新能源与能源互联网等。E-mail：houhui@whut.edu.cn
  于士文(1995—)，男，硕士研究生，主要研究方向：电力系统可靠性与风险评估、机器学习等。E-mail：yswwhut@163.com
  肖祥(1964—)，男，高级工程师，主要研究方向：电力系统风险评估、机器学习等。E-mail：2112140460@qq.com
  黄勇(1986—)，男，通信作者，硕士，高级工程师，主要研究方向：电网防灾减灾技术。E-mail：huangyong_wh@126.com
- 基金信息：
  
  中央高校基本科研业务费专项资金资助项目(191011005);中国南方电网有限责任公司科技项目(GDKJXM20198441)
- DOI：10.7500/AEPS20191113002
  中图分类号：
- 收稿日期：2019-11-13，
  
  修回日期：2019-12-10，
  
  网络出版日期：2020-03-24，
  
  纸质出版日期：2020-05-25
- 稿件说明：
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侯慧,于士文,肖祥等.基于空间多源异构数据的台风下输电杆塔风险评估[J].电力系统自动化,2020,44(10):127-134.

HOU Hui,YU Shiwen,XIAO Xiang,et al.Risk Assessment of Transmission Tower in Typhoon Based on Spatial Multi-source Heterogeneous Data[J].Automation of Electric Power Systems,2020,44(10):127-134.
侯慧,于士文,肖祥等.基于空间多源异构数据的台风下输电杆塔风险评估[J].电力系统自动化,2020,44(10):127-134. DOI： 10.7500/AEPS20191113002.

HOU Hui,YU Shiwen,XIAO Xiang,et al.Risk Assessment of Transmission Tower in Typhoon Based on Spatial Multi-source Heterogeneous Data[J].Automation of Electric Power Systems,2020,44(10):127-134. DOI： 10.7500/AEPS20191113002.

摘要

台风灾害下电力系统的风险评估及可视化对电力系统防灾减灾具有重要的科学意义和工程应用价值。为了预测高风险区域，优化抢修资源配置和潮流风险调度，文中分数据层、知识提取层和可视化处理层构建了台风灾害下输电杆塔风险评估体系。首先，基于设备运行信息、气象信息以及地理信息等构建空间多源异构信息数据库。再基于参数优化，应用6种机器学习算法建立了杆塔损毁风险预测智能模型，通过指标对比选择相对最优模型，同时，在此基础上提出基于不等权拟合优度法的组合模型。以1 km×1 km的尺度对台风“彩虹”下某中国沿海城市的杆塔损毁风险进行了评估及可视化，将相对最优模型与组合模型进行了详细的对比，测试结果显示：相对最优模型及组合模型均能够识别损毁最严重的区域，但相同风险阈值下组合模型的预测效果更好，验证了所提方法的可行性与合理性。最后，分析了模型通用性和样本数量级对预测效果的影响。

Abstract

Risk assessment and visualization of power system under typhoon disasters has scientific significance and engineering application value for disaster prevention and mitigation of power systems. In order to predict high-risk areas and optimize the emergency material allocation and risk-based dispatch of power flow

the data layer

knowledge extraction layer and visualization layer are used to construct the risk assessment system for power transmission towers under typhoon disasters. Firstly

based on equipment operation information

meteorological information and geographic information

a spatial multi-source heterogeneous information database is built. Then

based on parameter optimization

six machine learning algorithms are used to establish intelligent models for tower damage risk prediction

and a relative optimal model is selected through index comparison. At the same time

a combined model based on goodness of fit method with unequal weight is proposed. The tower damage risk in a Chinese coastal city under the typhoon “Mujigae” is assessed and visualized with dimension of 1 km×1 km. The relative optimal model is compared with the combined model in detail. The results show that both relative optimal model and combined model can identify the most severely damaged area

but the combined model has better prediction with the same risk threshold

which verifies the feasibility and rationality of the proposed method. Finally

the model universality and the influence of sample magnitude on prediction effect are analyzed.

关键词

Keywords

references

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