Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

Ning Liu, Bo Fan, Xianyong Xiao and Xiaomei Yang
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Ning Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Bo Fan: Power Research Institute of State Grid Ningxia Power Co., Yinchuan 750000, China
Xianyong Xiao: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaomei Yang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2019, vol. 12, issue 18, 1-15

Abstract: Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

Keywords: incipient fault; sparse autoencoder; deep belief network; identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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