A hybrid prediction model for damage warning of power transmission line under typhoon disaster

Hui Hou, Shiwen Yu, Hao Wang, Yan Xu, Xiang Xiao, Yong Huang, Xixiu Wu

Research output: Contribution to journalArticleResearchpeer-review

Abstract

To bolster the resilience of power systems against typhoon disasters, this paper develops a holistic framework of wind disaster warning for transmission lines. This paper proposes a hybrid prediction model to quantify the transmission line damage probability under typhoon disaster based on extreme value type I probability distribution, Monte Carlo method, and Random Forest. Specifically, this paper uses the extreme value type I probability distribution and the Monte Carlo method to simulate the random wind field, and predict the damage probability of transmission lines under each wind field using the Random Forest method. This paper takes typhoon 'Mangkhut' in 2018 as a case study, and compare the performance of the hybrid model based on random wind field with the Random Forest method under predicted and measured wind field. The results demonstrate that the hybrid model can effectively utilize wind speed data to obtain a more reliable prediction and achieves the best synthetic similarity to the actual damage situations.

Original languageEnglish
Pages (from-to)85038-85050
Number of pages13
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 6 May 2020

Keywords

  • extreme value type I probability distribution
  • Monte Carlo
  • power system resilience
  • Random Forest
  • random wind field
  • transmission line
  • Typhoon

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