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Single-ended fault location and early warning method of transmission line based on back propagation neural network
Author(s) -
Han Zhou,
Minghui Liu,
Xin Yu,
Weiyang Wang,
Jingyao Gao
Publication year - 2021
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 - 10.1088/1742-6596/2121/1/012032
Subject(s) - fault (geology) , backpropagation , artificial neural network , fault indicator , wavelet , computer science , electric power transmission , transmission line , network packet , stuck at fault , line (geometry) , energy (signal processing) , transmission (telecommunications) , real time computing , algorithm , fault detection and isolation , engineering , artificial intelligence , telecommunications , electrical engineering , mathematics , computer network , statistics , geometry , seismology , actuator , geology
For power transmission systems, accurate and reliable fault location methods can ensure rapid recovery of faulty lines and improve power supply reliability. In order to solve the problems of the structural complexity of the transmission system and the difficulty of line fault location, a single-ended fault location and early warning method of transmission line based on back propagation neural network is proposed. First, the fault line selection is performed quickly when the fault occurs. Then, the voltage fault components collected at the measuring point when the fault occurs are decomposed and reconstructed by wavelet packet to obtain the wavelet packet energy, which is used as the input sample to train through the nonlinear fitting ability of back propagation. With the help of backpropagation neural network, arbitrary complex functions can be processed, and the learning results can be accurately used for new knowledge, and circuit faults can be diagnosed conveniently and quickly. Finally, the corresponding fault distance can be output by substituting the wavelet packet energy reflecting the fault location. The simulation results show that the method has strong resistance to transition resistance and high positioning accuracy.

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