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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This study proposes a recognition method based on symmetrized dot pattern (SDP) analysis and convolutional neural network (CNN) for rapid and accurate diagnosis of insulation defect problems by detecting the partial discharge (PD) signals of XLPE power cables. First, a normal and three power cable models with different insulation defects are built. The PD signals resulting from power cable insulation defects are measured. The frequency and amplitude variations of PD signals from different defects are reflected by comprehensible images using the proposed SDP analysis method. The features of different power cable defects are presented. Finally, the feature image is trained and identified by CNN to achieve a power cable insulation fault diagnosis system. The experimental results show that the proposed method could accurately diagnose the fault types of power cable insulation defects with a recognition accuracy of 98%. The proposed method is characterized by a short detection time and high diagnostic accuracy. It can effectively detect the power cable PD to identify the fault type of the insulation defect.

Details

Title
Hybrid Methodology Based on Symmetrized Dot Pattern and Convolutional Neural Networks for Fault Diagnosis of Power Cables
Author
Meng-Hui, Wang 1   VIAFID ORCID Logo  ; Hong-Wei, Sian 2   VIAFID ORCID Logo  ; Shiue-Der Lu 1   VIAFID ORCID Logo 

 Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan 
 Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan 
First page
2009
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
22279717
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2728515492
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.