Open AccessResearch on Adaptive Power Transmission Line Fault Inspection
Author(s) -
Jianxiang Wu,
Shuang Shao,
Dongping Jiang,
Hongfang Yao,
Caiguo Ma,
Ke-Kui Xu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/787/1/012050
Subject(s) - ampacity , electric power transmission , fault (geology) , transmission line , power transmission , power (physics) , transmission (telecommunications) , reliability engineering , computer science , line (geometry) , cluster analysis , electronic engineering , real time computing , engineering , electrical engineering , artificial intelligence , mathematics , electrical conductor , physics , geometry , quantum mechanics , seismology , geology
Power transmission line is one of the most important infrastructures of power system, and its safety monitoring is of great significance. The conventional way of fault monitoring of power transmission lines by only setting threshold value on single temperature data of strain clamp turned out to multiple misjudgements and delayed alarms, causing the increment of operation risk of power transmission line. In this paper, various types of time-varying sensors data such as strain clamp temperature data, environmental data, and cable ampacity data are accounted. Also, an unsupervised machine learning algorithm - K-means clustering algorithm was introduced to build a discriminant model in detecting the defects of power transmission line. Experimental results proved that the proposed method is able to avoid delayed alarms as well as misjudgement incurred from conventional method. As a result, the operation safety of power transmission line and inspection efficiency can be improved. The inspection cost would be reduced as well.