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Multi-factor Correction Based Failure Prediction Method of Transmission Line with Typhoon Disaster

被引：0
|
作者：
Hou H. [1 ]
Geng H. [1 ]
Huang Y. [2 ]
Wang H. [3 ]
Li X. [1 ]
Wu X. [1 ]
机构：
[1] School of Automation, Wuhan University of Technology, Wuhan
[2] Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou
[3] Guangzhou Power Supply Bureau Co., Ltd., Guangzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 23期
关键词：
Data driven; Failure prediction; Model driven; Multi-factor; Natural disaster; Tower-line system; Transmission line; Typhoon;
D O I：
10.7500/AEPS20181212007
中图分类号：
学科分类号：
摘要：
The typhoon disaster is one of the main reasons leading to the damages of transmission lines in the coastal areas, such as falling towers, breaking lines, etc. In order to improve the ability of power grid to against the typhoon disaster, it is of great significance to predict the transmission line failure under typhoon disaster. Therefore, the transmission line is regarded as a tower-line system composed of towers and lines, and a method for predicting the damage of transmission tower-line system under typhoon disaster based on multi-factor correction is proposed. The method combines the traditional wind load model and the multi-factor analysis data mining technology, and is divided into two parts: model-driven and data-driven. The model-driven part considers the actual wind load and the design wind load of tower and transmission line, and a physical mechanics model is established.The data-driven part considers multi-factor information such as power system information, meteorological information, and geographic information. Through the analysis of sample data under previous typhoon disasters, combined with current forecast information, the correction coefficient is obtained to correct the model-driven results, and then the comprehensive failure probability of the transmission tower-line system is obtained and visualized. Finally, the method is proven to be scientific and effective by case studies. © 2019 Automation of Electric Power Systems Press.
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页码：193 / 201and233
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