Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers
This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedfo...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on power delivery 2007-04, Vol.22 (2), p.904-910 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 910 |
|---|---|
| container_issue | 2 |
| container_start_page | 904 |
| container_title | IEEE transactions on power delivery |
| container_volume | 22 |
| creator | Hell, M. Costa, P. Gomide, F. |
| description | This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches |
| doi_str_mv | 10.1109/TPWRD.2006.874613 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671332979</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4141123</ieee_id><sourcerecordid>34452636</sourcerecordid><originalsourceid>FETCH-LOGICAL-c418t-3aa2d5e6c46385b4813495bb82689b57c9a5b46be7aafc1e75c37a17e532fe0c3</originalsourceid><addsrcrecordid>eNp9kUtLAzEQx4MoWKsfQLwsgo_L1mTzPopaFXwhFY8hm87q6najSZdiP72pFQUPnmaY-c3zj9A2wQNCsD4a3T3enw4KjMVASSYIXUE9oqnMWYHVKuphpXiutJTraCPGF4wxwxr30PAeXBcCtNPsBrrgq24-_0judObDa1a32egZwsQ22bUfQ1O3T5mvsjs_g5CNgm1j5cMEQtxEa5VtImx92z56GJ6NTi7yq9vzy5Pjq9wxoqY5tbYYcxCOCap4yRShTPOyVIVQuuTSaZuiogRpbeUISO6otEQCp0UF2NE-Olj2fQv-vYM4NZM6Omga24LvotGYCio1Zonc_5ekjPFCJLqPDv8FiZCE0kJLndDdP-iL70KbDjZKMMlIOiZBZAm54GMMUJm3UE9s-DAEm4VW5ksrs9DKLLVKNXvfjW10tqnSZ10dfwuV4OlfiwV2llwNAD_pNJeQgtJPaveb_w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>864741495</pqid></control><display><type>article</type><title>Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers</title><source>IEEE Electronic Library (IEL)</source><creator>Hell, M. ; Costa, P. ; Gomide, F.</creator><creatorcontrib>Hell, M. ; Costa, P. ; Gomide, F.</creatorcontrib><description>This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches</description><identifier>ISSN: 0885-8977</identifier><identifier>EISSN: 1937-4208</identifier><identifier>DOI: 10.1109/TPWRD.2006.874613</identifier><identifier>CODEN: ITPDE5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Aging ; Applied sciences ; Artificial neural networks ; Computation ; Computational modeling ; Computer networks ; Condition monitoring ; Electrical engineering. Electrical power engineering ; Exact sciences and technology ; Feedforward ; Multilayers ; Networks ; Nonlinear dynamical systems ; Power electronics, power supplies ; Power system modeling ; Power transformers ; Radial basis function ; recurrent neurofuzzy networks (RNFNs) ; Robustness ; Temperature ; thermal modeling ; Transformers ; Transformers and inductors</subject><ispartof>IEEE transactions on power delivery, 2007-04, Vol.22 (2), p.904-910</ispartof><rights>2007 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-3aa2d5e6c46385b4813495bb82689b57c9a5b46be7aafc1e75c37a17e532fe0c3</citedby><cites>FETCH-LOGICAL-c418t-3aa2d5e6c46385b4813495bb82689b57c9a5b46be7aafc1e75c37a17e532fe0c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4141123$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4141123$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18653859$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Hell, M.</creatorcontrib><creatorcontrib>Costa, P.</creatorcontrib><creatorcontrib>Gomide, F.</creatorcontrib><title>Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers</title><title>IEEE transactions on power delivery</title><addtitle>TPWRD</addtitle><description>This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches</description><subject>Aging</subject><subject>Applied sciences</subject><subject>Artificial neural networks</subject><subject>Computation</subject><subject>Computational modeling</subject><subject>Computer networks</subject><subject>Condition monitoring</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Exact sciences and technology</subject><subject>Feedforward</subject><subject>Multilayers</subject><subject>Networks</subject><subject>Nonlinear dynamical systems</subject><subject>Power electronics, power supplies</subject><subject>Power system modeling</subject><subject>Power transformers</subject><subject>Radial basis function</subject><subject>recurrent neurofuzzy networks (RNFNs)</subject><subject>Robustness</subject><subject>Temperature</subject><subject>thermal modeling</subject><subject>Transformers</subject><subject>Transformers and inductors</subject><issn>0885-8977</issn><issn>1937-4208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kUtLAzEQx4MoWKsfQLwsgo_L1mTzPopaFXwhFY8hm87q6najSZdiP72pFQUPnmaY-c3zj9A2wQNCsD4a3T3enw4KjMVASSYIXUE9oqnMWYHVKuphpXiutJTraCPGF4wxwxr30PAeXBcCtNPsBrrgq24-_0judObDa1a32egZwsQ22bUfQ1O3T5mvsjs_g5CNgm1j5cMEQtxEa5VtImx92z56GJ6NTi7yq9vzy5Pjq9wxoqY5tbYYcxCOCap4yRShTPOyVIVQuuTSaZuiogRpbeUISO6otEQCp0UF2NE-Olj2fQv-vYM4NZM6Omga24LvotGYCio1Zonc_5ekjPFCJLqPDv8FiZCE0kJLndDdP-iL70KbDjZKMMlIOiZBZAm54GMMUJm3UE9s-DAEm4VW5ksrs9DKLLVKNXvfjW10tqnSZ10dfwuV4OlfiwV2llwNAD_pNJeQgtJPaveb_w</recordid><startdate>20070401</startdate><enddate>20070401</enddate><creator>Hell, M.</creator><creator>Costa, P.</creator><creator>Gomide, F.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>20070401</creationdate><title>Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers</title><author>Hell, M. ; Costa, P. ; Gomide, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-3aa2d5e6c46385b4813495bb82689b57c9a5b46be7aafc1e75c37a17e532fe0c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Aging</topic><topic>Applied sciences</topic><topic>Artificial neural networks</topic><topic>Computation</topic><topic>Computational modeling</topic><topic>Computer networks</topic><topic>Condition monitoring</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Exact sciences and technology</topic><topic>Feedforward</topic><topic>Multilayers</topic><topic>Networks</topic><topic>Nonlinear dynamical systems</topic><topic>Power electronics, power supplies</topic><topic>Power system modeling</topic><topic>Power transformers</topic><topic>Radial basis function</topic><topic>recurrent neurofuzzy networks (RNFNs)</topic><topic>Robustness</topic><topic>Temperature</topic><topic>thermal modeling</topic><topic>Transformers</topic><topic>Transformers and inductors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hell, M.</creatorcontrib><creatorcontrib>Costa, P.</creatorcontrib><creatorcontrib>Gomide, F.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power delivery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hell, M.</au><au>Costa, P.</au><au>Gomide, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers</atitle><jtitle>IEEE transactions on power delivery</jtitle><stitle>TPWRD</stitle><date>2007-04-01</date><risdate>2007</risdate><volume>22</volume><issue>2</issue><spage>904</spage><epage>910</epage><pages>904-910</pages><issn>0885-8977</issn><eissn>1937-4208</eissn><coden>ITPDE5</coden><abstract>This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TPWRD.2006.874613</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0885-8977 |
| ispartof | IEEE transactions on power delivery, 2007-04, Vol.22 (2), p.904-910 |
| issn | 0885-8977 1937-4208 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1671332979 |
| source | IEEE Electronic Library (IEL) |
| subjects | Aging Applied sciences Artificial neural networks Computation Computational modeling Computer networks Condition monitoring Electrical engineering. Electrical power engineering Exact sciences and technology Feedforward Multilayers Networks Nonlinear dynamical systems Power electronics, power supplies Power system modeling Power transformers Radial basis function recurrent neurofuzzy networks (RNFNs) Robustness Temperature thermal modeling Transformers Transformers and inductors |
| title | Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A20%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Recurrent%20Neurofuzzy%20Network%20in%20Thermal%20Modeling%20of%20Power%20Transformers&rft.jtitle=IEEE%20transactions%20on%20power%20delivery&rft.au=Hell,%20M.&rft.date=2007-04-01&rft.volume=22&rft.issue=2&rft.spage=904&rft.epage=910&rft.pages=904-910&rft.issn=0885-8977&rft.eissn=1937-4208&rft.coden=ITPDE5&rft_id=info:doi/10.1109/TPWRD.2006.874613&rft_dat=%3Cproquest_RIE%3E34452636%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=864741495&rft_id=info:pmid/&rft_ieee_id=4141123&rfr_iscdi=true |