Open AccessA new method for identifying a fault in T-connected lines based on multiscale S-transform energy entropy and an extreme learning machine
Author(s) -
Hao Wu,
Jie Yang,
Leilei Chen,
Qiaomei Wang
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0220870
Subject(s) - extreme learning machine , electric power transmission , transmission line , computer science , energy (signal processing) , connection (principal bundle) , entropy (arrow of time) , fault (geology) , algorithm , artificial intelligence , transmission (telecommunications) , fault detection and isolation , traveling wave , topology (electrical circuits) , mathematics , engineering , physics , electrical engineering , mathematical analysis , geometry , telecommunications , artificial neural network , statistics , quantum mechanics , seismology , geology , actuator
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.