Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

Condition based maintenance; Condition monitoring; Deep learning; Electric power transmission networks; Fault detection; Testing; Ultrasonic applications; 'current; Arcing; Condition-based monitoring; Deep learning; Energy distribution networks; Faults detection; Medium voltage; Medium voltage...

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Main Authors: Alsumaidaee Y.A.M., Yaw C.T., Koh S.P., Tiong S.K., Chen C.P., Ali K.
Other Authors: 57903740900
Format: Review
Published: MDPI 2023
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spelling my.uniten.dspace-267562023-05-29T17:36:33Z Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning Alsumaidaee Y.A.M. Yaw C.T. Koh S.P. Tiong S.K. Chen C.P. Ali K. 57903740900 36560884300 57883863700 15128307800 57883616100 36130958600 Condition based maintenance; Condition monitoring; Deep learning; Electric power transmission networks; Fault detection; Testing; Ultrasonic applications; 'current; Arcing; Condition-based monitoring; Deep learning; Energy distribution networks; Faults detection; Medium voltage; Medium voltage switchgears; Monitoring system; Power energy; Partial discharges In power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems� and components� health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects. � 2022 by the authors. Final 2023-05-29T09:36:33Z 2023-05-29T09:36:33Z 2022 Review 10.3390/en15186762 2-s2.0-85138713163 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138713163&doi=10.3390%2fen15186762&partnerID=40&md5=6d5d0d63ee2d95325cf2e721f76d410c https://irepository.uniten.edu.my/handle/123456789/26756 15 18 6762 All Open Access, Gold MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Condition based maintenance; Condition monitoring; Deep learning; Electric power transmission networks; Fault detection; Testing; Ultrasonic applications; 'current; Arcing; Condition-based monitoring; Deep learning; Energy distribution networks; Faults detection; Medium voltage; Medium voltage switchgears; Monitoring system; Power energy; Partial discharges
author2 57903740900
author_facet 57903740900
Alsumaidaee Y.A.M.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Ali K.
format Review
author Alsumaidaee Y.A.M.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Ali K.
spellingShingle Alsumaidaee Y.A.M.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Ali K.
Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
author_sort Alsumaidaee Y.A.M.
title Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
title_short Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
title_full Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
title_fullStr Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
title_full_unstemmed Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning
title_sort review of medium-voltage switchgear fault detection in a condition-based monitoring system by using deep learning
publisher MDPI
publishDate 2023
_version_ 1806424441916227584
score 13.211869