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Switchgear Digitalization—Research Path, Status, and Future Work
To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-volta...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611866/ https://www.ncbi.nlm.nih.gov/pubmed/36298270 http://dx.doi.org/10.3390/s22207922 |
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author | Kaštelan, Nediljko Vujović, Igor Krčum, Maja Assani, Nur |
author_facet | Kaštelan, Nediljko Vujović, Igor Krčum, Maja Assani, Nur |
author_sort | Kaštelan, Nediljko |
collection | PubMed |
description | To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice. |
format | Online Article Text |
id | pubmed-9611866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96118662022-10-28 Switchgear Digitalization—Research Path, Status, and Future Work Kaštelan, Nediljko Vujović, Igor Krčum, Maja Assani, Nur Sensors (Basel) Review To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice. MDPI 2022-10-18 /pmc/articles/PMC9611866/ /pubmed/36298270 http://dx.doi.org/10.3390/s22207922 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Review Kaštelan, Nediljko Vujović, Igor Krčum, Maja Assani, Nur Switchgear Digitalization—Research Path, Status, and Future Work |
title | Switchgear Digitalization—Research Path, Status, and Future Work |
title_full | Switchgear Digitalization—Research Path, Status, and Future Work |
title_fullStr | Switchgear Digitalization—Research Path, Status, and Future Work |
title_full_unstemmed | Switchgear Digitalization—Research Path, Status, and Future Work |
title_short | Switchgear Digitalization—Research Path, Status, and Future Work |
title_sort | switchgear digitalization—research path, status, and future work |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611866/ https://www.ncbi.nlm.nih.gov/pubmed/36298270 http://dx.doi.org/10.3390/s22207922 |
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