Open AccessResearch on Fault Diagnosis Model of Transmission Line under Lightning Stroke Based on Neural Network
Author(s) -
QU Shao-jie,
Zhigang Wang,
Song Yongchao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012151
Subject(s) - fault (geology) , artificial neural network , electric power transmission , transmission line , electric power system , computer science , reliability (semiconductor) , reliability engineering , matlab , wavelet , line (geometry) , transmission (telecommunications) , lightning (connector) , power transmission , power (physics) , engineering , electrical engineering , artificial intelligence , telecommunications , physics , geometry , mathematics , quantum mechanics , seismology , geology , operating system
Transmission lines are an important part of the power system. In recent years, there have been more and more lightning trips, power outages and outages, which have become one of the hazards that threaten the stable operation of the power grid, and seriously endanger the reliability of the power grid operation. It is necessary to diagnose problems with transmission line faults. In this paper, after the information generated by the fault is processed by wavelet, it is input into the neural network fault diagnosis model as the input part, and the fault type of the transmission line is predicted and identified by the neural network operation, and the experiment is carried out through MATLAB simulation software. The prediction results show that the model is more accurate in fault diagnosis of transmission lines.