Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder

Cable incipient fault is an intermittent arc fault, and may evolve into a permanent fault. Due to the short duration of the fault, the conventional overcurrent protection device cannot detect it. A cable incipient fault identification method is proposed in this study, using restricted Boltzmann mach...

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Bibliographic Details
Published in:IET generation, transmission & distribution Vol. 14; no. 7; pp. 1242 - 1250
Main Authors: Wang, Ying, Lu, Hong, Xiao, Xianyong, Yang, Xiaomei, Zhang, Wenhai
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 14.04.2020
Subjects:
arcs (electric)
backpropagation
belief networks
Boltzmann machines
cable incipient fault identification method
compressed data
conventional overcurrent protection device
disturbance current waveforms data
fault diagnosis
feature extraction
high identification accuracy
identification result
intermittent arc fault
layer‐by‐layer pre‐training
learning (artificial intelligence)
neural nets
overcurrent protection
permanent fault
power distribution faults
RBM
Research Article
restricted Boltzmann machine
SAE network
stacked autoencoder
compressed data
SAE network
fault diagnosis
disturbance current waveforms data
intermittent arc fault
identification result
stacked autoencoder
conventional overcurrent protection device
arcs (electric)
power distribution faults
layer-by-layer pre-training
high identification accuracy
Boltzmann machines
feature extraction
overcurrent protection
backpropagation
restricted Boltzmann machine
permanent fault
belief networks
learning (artificial intelligence)
cable incipient fault identification method
RBM
neural nets
ISSN:1751-8687, 1751-8695
Online Access:Get full text
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Description
Summary:Cable incipient fault is an intermittent arc fault, and may evolve into a permanent fault. Due to the short duration of the fault, the conventional overcurrent protection device cannot detect it. A cable incipient fault identification method is proposed in this study, using restricted Boltzmann machine (RBM) and stacked autoencoder (SAE). Firstly, disturbance current waveforms data is effectively compressed by RBM, which can improve analysis efficiency and obtain the shallow features of the data. Then, the compressed data is used as the input of SAE, and the optimal network parameters are obtained through layer-by-layer pre-training and fine-tuning. Finally, a well-trained SAE network is used to learn deep features from the input data to identify cable incipient fault, and softmax outputs identification result. In addition, the performance of the proposed method is compared with other methods. The accuracy of the proposed method is 98.33/95.62% for simulated data/measured data, and is 1.66/1.09%, 3.33/1.76%, 17.31/28.48% and 40.17/46.1% higher than the accuracies of convolutional neural network, deep belief network, random forest and back propagation neural network, respectively. The results show that the proposed method has high identification accuracy and feasibility.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2019.0743