Steel and Composite Structures

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A data-driven method for the reliability analysis of a transmission line under wind loads

  • Xing Fu (Dalian University of Technology, State Key Laboratory of Coastal and Offshore Engineering) ;
  • Wen-Long Du (Dalian University of Technology, State Key Laboratory of Coastal and Offshore Engineering) ;
  • Gang Li (Dalian University of Technology, State Key Laboratory of Coastal and Offshore Engineering) ;
  • Zhi-Qian Dong (Dalian University of Technology, State Key Laboratory of Coastal and Offshore Engineering) ;
  • Hong-Nan Li (Dalian University of Technology, State Key Laboratory of Coastal and Offshore Engineering)
  • Received : 2021.09.12
  • Accepted : 2024.06.18
  • Published : 2024.08.25
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Abstract

This study focuses on the reliability of a transmission line under wind excitation and evaluates the failure probability using explicit data resources. The data-driven framework for calculating the failure probability of a transmission line subjected to wind loading is presented, and a probabilistic method for estimating the yearly extreme wind speeds in each wind direction is provided to compensate for the incompleteness of meteorological data. Meteorological data from the Xuwen National Weather Station are used to analyze the distribution characteristics of wind speed and wind direction, fitted with the generalized extreme value distribution. Then, the most vulnerable tower is identified to obtain the fragility curves in all wind directions based on uncertainty analysis. Finally, the failure probabilities are calculated based on the presented method. The simulation results reveal that the failure probability of the employed tower increases over time and that the joint probability distribution of the wind speed and wind direction must be considered to avoid overestimating the failure probability. Additionally, the mixed wind climates (synoptic wind and typhoon) have great influence on the estimation of structural failure probability and should be considered.

Keywords

Acknowledgement

This research was supported by the National Natural Science Foundation of China (Grant No. 52078104).

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