An Improved Prediction Algorithm for Noise of Transformer Considering Material Parameter Uncertainty

This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems. To characterize uncertainties in silicon steel, stochastic models of magnetization and magnetostriction behaviors are developed using vine co...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on magnetics s. 1
Hlavní autori:	Yang, Fan, Xia, Yisha, Wang, Jiawei, Wang, Pengbo, Jiang, Hui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	Adaptation models backpropagation algorithms Computational modeling Magnetic material Magnetization Magnetostriction Noise noise level Power transformer insulation Stochastic processes stochastic theory Transformers Uncertainty Vectors
ISSN:	0018-9464, 1941-0069
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems. To characterize uncertainties in silicon steel, stochastic models of magnetization and magnetostriction behaviors are developed using vine copula theory, generating correlated sample curves across flux densities ranging from 0.1-1.9 T. To mitigate computational costs, an adaptive differential evolution-optimized backpropagation neural network (ADE-BP) is introduced for transformer noise analysis. The proposed method is implemented to the finite element model of a transformer to validate its effectiveness and accuracy. Numerical results reveal a 12.7% probability of exceeding permissible noise thresholds due to variations in magnetic parameters, thereby underscoring the necessity for topology optimization in high-efficiency, low-noise transformer design methodologies.
AbstractList	This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems. To characterize uncertainties in silicon steel, stochastic models of magnetization and magnetostriction behaviors are developed using vine copula theory, generating correlated sample curves across flux densities ranging from 0.1-1.9 T. To mitigate computational costs, an adaptive differential evolution-optimized backpropagation neural network (ADE-BP) is introduced for transformer noise analysis. The proposed method is implemented to the finite element model of a transformer to validate its effectiveness and accuracy. Numerical results reveal a 12.7% probability of exceeding permissible noise thresholds due to variations in magnetic parameters, thereby underscoring the necessity for topology optimization in high-efficiency, low-noise transformer design methodologies.
Author	Wang, Jiawei Wang, Pengbo Yang, Fan Jiang, Hui Xia, Yisha
Author_xml	– sequence: 1 givenname: Fan orcidid: 0000-0003-4831-2241 surname: Yang fullname: Yang, Fan organization: School of Electrical Engineering, State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China – sequence: 2 givenname: Yisha orcidid: 0009-0002-3770-3917 surname: Xia fullname: Xia, Yisha email: xiayisha@cqu.edu.cn organization: School of Electrical Engineering, State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China – sequence: 3 givenname: Jiawei orcidid: 0000-0003-1526-110X surname: Wang fullname: Wang, Jiawei organization: School of Electrical Engineering, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, China – sequence: 4 givenname: Pengbo orcidid: 0000-0003-3262-1621 surname: Wang fullname: Wang, Pengbo organization: School of Electrical Engineering, State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China – sequence: 5 givenname: Hui orcidid: 0000-0002-7516-446X surname: Jiang fullname: Jiang, Hui organization: School of Electrical Engineering, State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China
BookMark	eNpFkEFrAjEUhEOxULX9AYUe8gfW5mWz2c1xkVYFbT3oeUmzLzbFTSRZCv77rij09GaGmXf4JmTkg0dCnoHNAJh63W3qxYwzXsxyyaXK-R0ZgxKQMSbViIwZgypTQooHMknpZ7CiADYmbe3pqjvF8Ist3UZsneld8LQ-HkJ0_XdHbYj0I7iENFi6i9qnIekw0nnwybUYnT_Qje4HoY90q6PucDB07w3GXjvfnx_JvdXHhE-3OyX797fdfJmtPxereb3ODORFnwGzJbdamUpYrUtbWCVtKTRDwYwqmGEKclkVX1JUimMFbcUNqqJFU5ZS6XxK4PrXxJBSRNucout0PDfAmgum5oKpuWBqbpiGzct14xDxvw-ccwl5_gcaFWdC
CODEN	IEMGAQ
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION
DOI	10.1109/TMAG.2025.3626932
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
Database_xml	– sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Physics
EISSN	1941-0069
EndPage	1
ExternalDocumentID	10_1109_TMAG_2025_3626932 11222613
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation Joint Fund grantid: U23B20135 – fundername: National Key Research and Development Program of China grantid: 2025ZD0808300 funderid: 10.13039/501100012166
GroupedDBID	-~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TN5 TWZ 5VS AAYXX AETIX AGSQL AI. AIBXA ALLEH CITATION EJD H~9 IAAWW IBMZZ ICLAB IFJZH VH1 VJK XXG
ID	FETCH-LOGICAL-c135t-10f72fa9c84faa7f5f96f74a0e40c950c0913685b64892e81d82ce95dec7769a3
IEDL.DBID	RIE
ISSN	0018-9464
IngestDate	Sat Nov 29 06:59:20 EST 2025 Wed Nov 05 07:09:20 EST 2025
IsPeerReviewed	false
IsScholarly	true
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c135t-10f72fa9c84faa7f5f96f74a0e40c950c0913685b64892e81d82ce95dec7769a3
ORCID	0000-0002-7516-446X 0009-0002-3770-3917 0000-0003-3262-1621 0000-0003-1526-110X 0000-0003-4831-2241
PageCount	1
ParticipantIDs	ieee_primary_11222613 crossref_primary_10_1109_TMAG_2025_3626932
PublicationCentury	2000
PublicationDate	2025-00-00
PublicationDateYYYYMMDD	2025-01-01
PublicationDate_xml	– year: 2025 text: 2025-00-00
PublicationDecade	2020
PublicationTitle	IEEE transactions on magnetics
PublicationTitleAbbrev	TMAG
PublicationYear	2025
Publisher	IEEE
Publisher_xml	– name: IEEE
SSID	ssj0014510
Score	2.441587
Snippet	This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems....
SourceID	crossref ieee
SourceType	Index Database Publisher
StartPage	1
SubjectTerms	Adaptation models backpropagation algorithms Computational modeling Magnetic material Magnetization Magnetostriction Noise noise level Power transformer insulation Stochastic processes stochastic theory Transformers Uncertainty Vectors
Title	An Improved Prediction Algorithm for Noise of Transformer Considering Material Parameter Uncertainty
URI	https://ieeexplore.ieee.org/document/11222613
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 1941-0069 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014510 issn: 0018-9464 databaseCode: RIE dateStart: 19650101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT4MwGG500UQPfswZ51d68GTCVqCF9kiM04NbdtiS3UhX3uoSBcOYif_etjCdBw_egBRC3qfQp-_H8yJ0I0OqNWXcIxqYR2UGHmeQeebbMhjDXBFwIq5P8WjEZzMxborVXS0MALjkM-jZQxfLzwq1sq6yvuEGhi3YHrXbcRzVxVrfIQPK_LrexOe2bzxtQpg-Ef3JMHkwW8GA9az4igiDX4vQRlcVt6gMDv_5OkfooGGPOKnhPkZbkLfR_oamYBvtupxOtTxBWZLj2mcAGR6XNiJjUcDJ63NRLqqXN2wIKx4ViyXgQuPJmsJCiddtPM0T8VBWbpbisbSJXOYET81McZkE1WcHTQf3k7tHr2mq4Ck_ZJX57eo40FIoTrWUsWZaRDqmkgAlSjCirFBoxNk8olwEYOgsDxQIloEyNhcyPEWtvMjhDGEVxVb-TgOZG-sbaqGdvIshgTSeyyDsotu1ldP3WjsjdXsOIlILSWohSRtIuqhjLfwzsDHu-R_XL9Cevb32hlyiVlWu4ArtqI9qsSyv3dT4AvozttA
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELVQAQEDH6WI8umBCSmtk9hJPFaIUkRbdWilbpHjnKESNChNkfj32E4KZWBgSyLLiu5d4mff3TuEboRPlaIscogC5lCRghMxSB39bWmMIZEErIhrPxwOo-mUj6pidVsLAwA2-Qxa5tLG8tNMLs1RWVtzA80WTI_aTUapR8pyre-gAWVuWXHiRqZzPK2CmC7h7fGg86A3gx5rGfkV7nu_lqG1vip2Weke_POFDtF-xR9xpwT8CG3AvI721lQF62jbZnXKxTFKO3NcnhpAike5ickYHHDn9TnLZ8XLG9aUFQ-z2QJwpvB4RWIhx6tGnnpGPBCF9VM8EiaVS9_gifYVm0tQfDbQpHs_vus5VVsFR7o-K_SPV4WeElxGVAkRKqZ4oEIqCFAiOSPSSIUGEUsCGnEPNKGNPAmcpSDDMODCP0G1eTaHU4RlEBoBPAUk0dbX5EJZgRdNA2mYCM9votuVleP3Uj0jtrsOwmMDSWwgiStImqhhLPwzsDLu2R_Pr9FObzzox_3H4dM52jVTlWcjF6hW5Eu4RFvyo5gt8ivrJl-rtLoX
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Improved+Prediction+Algorithm+for+Noise+of+Transformer+Considering+Material+Parameter+Uncertainty&rft.jtitle=IEEE+transactions+on+magnetics&rft.au=Yang%2C+Fan&rft.au=Xia%2C+Yisha&rft.au=Wang%2C+Jiawei&rft.au=Wang%2C+Pengbo&rft.date=2025&rft.pub=IEEE&rft.issn=0018-9464&rft.spage=1&rft.epage=1&rft_id=info:doi/10.1109%2FTMAG.2025.3626932&rft.externalDocID=11222613
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9464&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9464&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9464&client=summon