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

1. School of Automation, Wuhan University of Technology, Wuhan 430070, China; 2. Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China; 3. Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China

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    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.

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HOU Hui, GENG Hao, HUANG Yong,et al.Multi-factor Correction Based Failure Prediction Method of Transmission Line with Typhoon Disaster[J].Automation of Electric Power Systems,2019,43(23):193-201.DOI:10.7500/AEPS20181212007

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  • Received:December 12,2018
  • Revised:October 24,2019
  • Adopted:October 10,2019
  • Online: October 23,2019
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