Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm
Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algor...
Gespeichert in:
Veröffentlicht in: | Journal of nanomaterials 2022, Vol.2022 (1) |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | |
container_title | Journal of nanomaterials |
container_volume | 2022 |
creator | Liu, Bowen Lv, Fangcheng Fan, Xiaozhou Sui, Yueyi Wang, Jiaxue Yin, Shengdong |
description | Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%. |
doi_str_mv | 10.1155/2022/7507729 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2653898722</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2653898722</sourcerecordid><originalsourceid>FETCH-LOGICAL-c294t-cb470781f63da29bca31c4c12104fe3d6e12b5b8d3b9a0daeea01bad91c812413</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKs3f0DAo67NZD9zrG39gIqCel5mk2ybsptdky3Sf29Ki0dPM_A-8w48hFwDuwdI0wlnnE_ylOU5FydkBFmRRwlwcfq3AzsnF95vGEtSkfIR6ReNloMzEhv6rl3duRat1PQDaz3s6FwPITadpV1Npw5bo-gMvbEr-oBeKxqS1y5UbBt0dL6zgZCeolXhVPd0qdHZPT1tVp0zw7q9JGc1Nl5fHeeYfD0uPmfP0fLt6WU2XUaSi2SIZJXkLC-gzmKFXFQSY5CJBA4sqXWsMg28SqtCxZVAplBrZFChEiAL4AnEY3Jz6O1d973Vfig33dbZ8LLkWRoXosg5D9TdgZKu897puuydadHtSmDl3mm5d1oenQb89oCvjVX4Y_6nfwEK3Hal</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2653898722</pqid></control><display><type>article</type><title>Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm</title><source>Wiley-Blackwell Open Access Titles</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><creator>Liu, Bowen ; Lv, Fangcheng ; Fan, Xiaozhou ; Sui, Yueyi ; Wang, Jiaxue ; Yin, Shengdong</creator><contributor>Velmurugan, Palanivel ; Palanivel Velmurugan</contributor><creatorcontrib>Liu, Bowen ; Lv, Fangcheng ; Fan, Xiaozhou ; Sui, Yueyi ; Wang, Jiaxue ; Yin, Shengdong ; Velmurugan, Palanivel ; Palanivel Velmurugan</creatorcontrib><description>Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%.</description><identifier>ISSN: 1687-4110</identifier><identifier>EISSN: 1687-4129</identifier><identifier>DOI: 10.1155/2022/7507729</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accidents ; Aging ; Algorithms ; Complexity ; Composite materials ; Deep learning ; Design ; Dynamic stability ; Electric fields ; Electric potential ; Electric power systems ; Electrical properties ; Electricity distribution ; Feature extraction ; Field strength ; Finite element analysis ; High temperature ; Insulation ; Lasers ; Machine learning ; Moisture content ; Moisture effects ; Molecular dynamics ; Nanomaterials ; Performance evaluation ; Performance tests ; Powder metallurgy ; Random variables ; Reliability ; Safety ; Stability analysis ; Transformers ; Voltage ; Yield stress</subject><ispartof>Journal of nanomaterials, 2022, Vol.2022 (1)</ispartof><rights>Copyright © 2022 Bowen Liu et al.</rights><rights>Copyright © 2022 Bowen Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-cb470781f63da29bca31c4c12104fe3d6e12b5b8d3b9a0daeea01bad91c812413</cites><orcidid>0000-0002-2991-3861</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4009,27902,27903,27904</link.rule.ids></links><search><contributor>Velmurugan, Palanivel</contributor><contributor>Palanivel Velmurugan</contributor><creatorcontrib>Liu, Bowen</creatorcontrib><creatorcontrib>Lv, Fangcheng</creatorcontrib><creatorcontrib>Fan, Xiaozhou</creatorcontrib><creatorcontrib>Sui, Yueyi</creatorcontrib><creatorcontrib>Wang, Jiaxue</creatorcontrib><creatorcontrib>Yin, Shengdong</creatorcontrib><title>Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm</title><title>Journal of nanomaterials</title><description>Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%.</description><subject>Accidents</subject><subject>Aging</subject><subject>Algorithms</subject><subject>Complexity</subject><subject>Composite materials</subject><subject>Deep learning</subject><subject>Design</subject><subject>Dynamic stability</subject><subject>Electric fields</subject><subject>Electric potential</subject><subject>Electric power systems</subject><subject>Electrical properties</subject><subject>Electricity distribution</subject><subject>Feature extraction</subject><subject>Field strength</subject><subject>Finite element analysis</subject><subject>High temperature</subject><subject>Insulation</subject><subject>Lasers</subject><subject>Machine learning</subject><subject>Moisture content</subject><subject>Moisture effects</subject><subject>Molecular dynamics</subject><subject>Nanomaterials</subject><subject>Performance evaluation</subject><subject>Performance tests</subject><subject>Powder metallurgy</subject><subject>Random variables</subject><subject>Reliability</subject><subject>Safety</subject><subject>Stability analysis</subject><subject>Transformers</subject><subject>Voltage</subject><subject>Yield stress</subject><issn>1687-4110</issn><issn>1687-4129</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kE1LAzEQhoMoWKs3f0DAo67NZD9zrG39gIqCel5mk2ybsptdky3Sf29Ki0dPM_A-8w48hFwDuwdI0wlnnE_ylOU5FydkBFmRRwlwcfq3AzsnF95vGEtSkfIR6ReNloMzEhv6rl3duRat1PQDaz3s6FwPITadpV1Npw5bo-gMvbEr-oBeKxqS1y5UbBt0dL6zgZCeolXhVPd0qdHZPT1tVp0zw7q9JGc1Nl5fHeeYfD0uPmfP0fLt6WU2XUaSi2SIZJXkLC-gzmKFXFQSY5CJBA4sqXWsMg28SqtCxZVAplBrZFChEiAL4AnEY3Jz6O1d973Vfig33dbZ8LLkWRoXosg5D9TdgZKu897puuydadHtSmDl3mm5d1oenQb89oCvjVX4Y_6nfwEK3Hal</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Liu, Bowen</creator><creator>Lv, Fangcheng</creator><creator>Fan, Xiaozhou</creator><creator>Sui, Yueyi</creator><creator>Wang, Jiaxue</creator><creator>Yin, Shengdong</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>L7M</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-2991-3861</orcidid></search><sort><creationdate>2022</creationdate><title>Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm</title><author>Liu, Bowen ; Lv, Fangcheng ; Fan, Xiaozhou ; Sui, Yueyi ; Wang, Jiaxue ; Yin, Shengdong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-cb470781f63da29bca31c4c12104fe3d6e12b5b8d3b9a0daeea01bad91c812413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accidents</topic><topic>Aging</topic><topic>Algorithms</topic><topic>Complexity</topic><topic>Composite materials</topic><topic>Deep learning</topic><topic>Design</topic><topic>Dynamic stability</topic><topic>Electric fields</topic><topic>Electric potential</topic><topic>Electric power systems</topic><topic>Electrical properties</topic><topic>Electricity distribution</topic><topic>Feature extraction</topic><topic>Field strength</topic><topic>Finite element analysis</topic><topic>High temperature</topic><topic>Insulation</topic><topic>Lasers</topic><topic>Machine learning</topic><topic>Moisture content</topic><topic>Moisture effects</topic><topic>Molecular dynamics</topic><topic>Nanomaterials</topic><topic>Performance evaluation</topic><topic>Performance tests</topic><topic>Powder metallurgy</topic><topic>Random variables</topic><topic>Reliability</topic><topic>Safety</topic><topic>Stability analysis</topic><topic>Transformers</topic><topic>Voltage</topic><topic>Yield stress</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Bowen</creatorcontrib><creatorcontrib>Lv, Fangcheng</creatorcontrib><creatorcontrib>Fan, Xiaozhou</creatorcontrib><creatorcontrib>Sui, Yueyi</creatorcontrib><creatorcontrib>Wang, Jiaxue</creatorcontrib><creatorcontrib>Yin, Shengdong</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Journal of nanomaterials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Bowen</au><au>Lv, Fangcheng</au><au>Fan, Xiaozhou</au><au>Sui, Yueyi</au><au>Wang, Jiaxue</au><au>Yin, Shengdong</au><au>Velmurugan, Palanivel</au><au>Palanivel Velmurugan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm</atitle><jtitle>Journal of nanomaterials</jtitle><date>2022</date><risdate>2022</risdate><volume>2022</volume><issue>1</issue><issn>1687-4110</issn><eissn>1687-4129</eissn><abstract>Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/7507729</doi><orcidid>https://orcid.org/0000-0002-2991-3861</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1687-4110 |
ispartof | Journal of nanomaterials, 2022, Vol.2022 (1) |
issn | 1687-4110 1687-4129 |
language | eng |
recordid | cdi_proquest_journals_2653898722 |
source | Wiley-Blackwell Open Access Titles; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
subjects | Accidents Aging Algorithms Complexity Composite materials Deep learning Design Dynamic stability Electric fields Electric potential Electric power systems Electrical properties Electricity distribution Feature extraction Field strength Finite element analysis High temperature Insulation Lasers Machine learning Moisture content Moisture effects Molecular dynamics Nanomaterials Performance evaluation Performance tests Powder metallurgy Random variables Reliability Safety Stability analysis Transformers Voltage Yield stress |
title | Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T00%3A42%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Electrical%20Performance%20Safety%20Detection%20of%20Aramid%20Casing%20Based%20on%20Molecular%20Dynamics%20and%20Deep%20Learning%20Algorithm&rft.jtitle=Journal%20of%20nanomaterials&rft.au=Liu,%20Bowen&rft.date=2022&rft.volume=2022&rft.issue=1&rft.issn=1687-4110&rft.eissn=1687-4129&rft_id=info:doi/10.1155/2022/7507729&rft_dat=%3Cproquest_cross%3E2653898722%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2653898722&rft_id=info:pmid/&rfr_iscdi=true |