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A new method for identifying a fault in T-connected lines based on multiscale S-transform energy entropy and an extreme learning machine.

Authors :
Hao Wu
Jie Yang
Leilei Chen
Qiaomei Wang
Source :
PLoS ONE, Vol 14, Iss 8, p e0220870 (2019)
Publication Year :
2019
Publisher :
Public Library of Science (PLoS), 2019.

Abstract

Due to the characteristics of T-connection transmission lines, a new method for T-connection transmission lines fault identification based on current reverse travelling wave multi-scale S-transformation energy entropy and limit learning machine is proposed. S-transform are implemented on the faulty reverse traveling waves measured by each traveling wave protection unit of the T-connection transmission line, the reverse travelling wave energy entropies under eight different frequencies are respectively calculated, and a T-connection transmission line fault characteristic vector sample set are thus formed. Establish an intelligent fault identification model of extreme learning machines, and use the sample set for training and testing to identify the specific faulty branch of the T-connection transmission line. The simulation results show that the proposed algorithm can accurately and quickly identify the branch where the fault is located on the T-connection transmission line under various operation conditions.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
14
Issue :
8
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.194acb9751240aabd56bc8e30347170
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pone.0220870