Open AccessHeuristic failure prediction model of transmission line under natural disasters
Author(s) -
Gao Wensheng,
Zhou Ruixu,
Zhao Dongbo
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0872
Subject(s) - typhoon , natural disaster , microclimate , transmission line , computer science , electric power transmission , damages , heuristic , meteorology , tripping , reliability engineering , operations research , environmental science , engineering , geography , artificial intelligence , telecommunications , circuit breaker , electrical engineering , archaeology , law , political science
Natural disasters caused by tropical cyclones such as Typhoon, Hurricane, Tornados, etc., are bringing severe damage to modern power transmission systems throughout the world. These natural disasters each have their individual patterns at specific locations, and the current pre‐disaster analysis is performed primarily relying on simple weather forecasting results, which lacks the acknowledgment of microclimate analytics. This paper presents heuristic transmission line failure model regarding microclimate analysis, which serves as more accurate and active prediction reference for regional transmission lines. Anti‐disaster preventive activities can be thereafter designed prior to the occurrence of the disaster, and post‐disaster remedy actions can be well prepared accordingly as well to prevent severe damages. This paper provides detailed modelling of Typhoon in typical areas of China as examples, and comes up with the corresponding transmission line failure model by accounting the microclimate prediction method and historical line tripping data sets. Validation process of the model returns a passing result of the vast majority of prediction equations for trip‐out rate of transmission lines on the significant test with the confidence level of 95%, and a reasonable prediction ratio of all the transmission lines.