NDLI: Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder

Content Provider	IET Digital Library
Author	Wang, Ying Lu, Hong Xiao, Xianyong Yang, Xiaomei Zhang, Wenhai
Abstract	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.
Starting Page	1242
Ending Page	1250
Page Count	9
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 7, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.0743
Journal	IET Generation, Transmission & Distribution
Publisher Date	2019-12-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Arcs (electric) Backpropagation Belief Network Boltzmann Machines Cable Incipient Fault Identification Method Compressed Data Conventional Overcurrent Protection Device Distribution Network Disturbance Current Waveforms Data Fault Diagnosis Feature Extraction High Identification Accuracy Identification Result Intermittent Arc Fault Knowledge Engineering Technique Layer-by-layer Pre-training Learning in AI Neural Computing Technique Neural Nets Overcurrent Protection Permanent Fault Power Distribution Fault RBM Restricted Boltzmann Machine SAE Network Stacked Autoencoder Statistics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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