Synchronous machine model identification using continuous wavelet NARX network
Abstract A new wavelet network structure combining polynomial models with continuous wavelet decomposition is introduced for the identification of a synchronous generator model. The proposed structure uses features of polynomial models and wavelet networks to model non-linearities in the system. In...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering Journal of systems and control engineering, 2009-06, Vol.223 (4), p.467-477 |
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| container_end_page | 477 |
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| container_issue | 4 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering |
| container_volume | 223 |
| creator | Ravan, M Amineh, R K Karrari, M Rosehart, W B Malik, O P |
| description | Abstract
A new wavelet network structure combining polynomial models with continuous wavelet decomposition is introduced for the identification of a synchronous generator model. The proposed structure uses features of polynomial models and wavelet networks to model non-linearities in the system. In this study, a serial-parallel identification model is applied to system modelling. In this structure, real system outputs are exercised for prediction of the future system outputs, so that stability and approximation of the network are guaranteed. This method is applied to identify the model of a synchronous machine from experimental data collected on a physical machine. The results show that the identified model has very good accuracy and may be valid for a broad range of operating conditions. |
| doi_str_mv | 10.1243/09596518JSCE692 |
| format | Article |
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A new wavelet network structure combining polynomial models with continuous wavelet decomposition is introduced for the identification of a synchronous generator model. The proposed structure uses features of polynomial models and wavelet networks to model non-linearities in the system. In this study, a serial-parallel identification model is applied to system modelling. In this structure, real system outputs are exercised for prediction of the future system outputs, so that stability and approximation of the network are guaranteed. This method is applied to identify the model of a synchronous machine from experimental data collected on a physical machine. The results show that the identified model has very good accuracy and may be valid for a broad range of operating conditions.</description><identifier>ISSN: 0959-6518</identifier><identifier>EISSN: 2041-3041</identifier><identifier>DOI: 10.1243/09596518JSCE692</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Approximation ; Continuous wavelet transform ; Decomposition ; Generators ; Mathematical analysis ; Mathematical models ; Mechanical engineering ; Networks ; Neurons ; Nonlinear systems ; Nonlinearity ; Polynomials ; Stability ; Synchronous machines ; Testing ; Wavelet transforms</subject><ispartof>Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, 2009-06, Vol.223 (4), p.467-477</ispartof><rights>2009 Institution of Mechanical Engineers</rights><rights>Copyright Professional Engineering Publishing Ltd Jun 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-ec05c23675fc34984a0d590d8988197e07356200a80afcae7a7028c50643e6a63</citedby><cites>FETCH-LOGICAL-c364t-ec05c23675fc34984a0d590d8988197e07356200a80afcae7a7028c50643e6a63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1243/09596518JSCE692$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1243/09596518JSCE692$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids></links><search><creatorcontrib>Ravan, M</creatorcontrib><creatorcontrib>Amineh, R K</creatorcontrib><creatorcontrib>Karrari, M</creatorcontrib><creatorcontrib>Rosehart, W B</creatorcontrib><creatorcontrib>Malik, O P</creatorcontrib><title>Synchronous machine model identification using continuous wavelet NARX network</title><title>Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering</title><description>Abstract
A new wavelet network structure combining polynomial models with continuous wavelet decomposition is introduced for the identification of a synchronous generator model. The proposed structure uses features of polynomial models and wavelet networks to model non-linearities in the system. In this study, a serial-parallel identification model is applied to system modelling. In this structure, real system outputs are exercised for prediction of the future system outputs, so that stability and approximation of the network are guaranteed. This method is applied to identify the model of a synchronous machine from experimental data collected on a physical machine. The results show that the identified model has very good accuracy and may be valid for a broad range of operating conditions.</description><subject>Approximation</subject><subject>Continuous wavelet transform</subject><subject>Decomposition</subject><subject>Generators</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mechanical engineering</subject><subject>Networks</subject><subject>Neurons</subject><subject>Nonlinear systems</subject><subject>Nonlinearity</subject><subject>Polynomials</subject><subject>Stability</subject><subject>Synchronous machines</subject><subject>Testing</subject><subject>Wavelet transforms</subject><issn>0959-6518</issn><issn>2041-3041</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kU1Lw0AQhhdRsFbPXoOCJ2Mnu9mvYyn1i1LBKngLy2bTpia7NZtY-u_dUA9ScA4zMPO87wwMQpcJ3CU4JSOQVDKaiOfFZMokPkIDDGkSk5CO0aCfxv34FJ15v4YQQvIBmi92Vq8aZ13no1rpVWlNVLvcVFGZG9uWRalVWzobdb60y0i70LNdT2_Vt6lMG83Hrx-RNe3WNZ_n6KRQlTcXv3WI3u-nb5PHePby8DQZz2JNWNrGRgPVmDBOC01SKVIFOZWQCylEIrkBTijDAEqAKrQyXHHAQlNgKTFMMTJEN3vfTeO-OuPbrC69NlWlrAm3ZYRhyinnAbw6ANeua2y4LQv7mQCBaYCu_4MSCVQIQiEN1GhP6cZ535gi2zRlrZpdlkDWvyA7eEFQ3O4VXi3NH89_8B9FaITv</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Ravan, M</creator><creator>Amineh, R K</creator><creator>Karrari, M</creator><creator>Rosehart, W B</creator><creator>Malik, O P</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20090601</creationdate><title>Synchronous machine model identification using continuous wavelet NARX network</title><author>Ravan, M ; Amineh, R K ; Karrari, M ; Rosehart, W B ; Malik, O P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-ec05c23675fc34984a0d590d8988197e07356200a80afcae7a7028c50643e6a63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Approximation</topic><topic>Continuous wavelet transform</topic><topic>Decomposition</topic><topic>Generators</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mechanical engineering</topic><topic>Networks</topic><topic>Neurons</topic><topic>Nonlinear systems</topic><topic>Nonlinearity</topic><topic>Polynomials</topic><topic>Stability</topic><topic>Synchronous machines</topic><topic>Testing</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ravan, M</creatorcontrib><creatorcontrib>Amineh, R K</creatorcontrib><creatorcontrib>Karrari, M</creatorcontrib><creatorcontrib>Rosehart, W B</creatorcontrib><creatorcontrib>Malik, O P</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</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>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering 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><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ravan, M</au><au>Amineh, R K</au><au>Karrari, M</au><au>Rosehart, W B</au><au>Malik, O P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Synchronous machine model identification using continuous wavelet NARX network</atitle><jtitle>Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering</jtitle><date>2009-06-01</date><risdate>2009</risdate><volume>223</volume><issue>4</issue><spage>467</spage><epage>477</epage><pages>467-477</pages><issn>0959-6518</issn><eissn>2041-3041</eissn><abstract>Abstract
A new wavelet network structure combining polynomial models with continuous wavelet decomposition is introduced for the identification of a synchronous generator model. The proposed structure uses features of polynomial models and wavelet networks to model non-linearities in the system. In this study, a serial-parallel identification model is applied to system modelling. In this structure, real system outputs are exercised for prediction of the future system outputs, so that stability and approximation of the network are guaranteed. This method is applied to identify the model of a synchronous machine from experimental data collected on a physical machine. The results show that the identified model has very good accuracy and may be valid for a broad range of operating conditions.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1243/09596518JSCE692</doi><tpages>11</tpages></addata></record> |
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| ispartof | Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, 2009-06, Vol.223 (4), p.467-477 |
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| subjects | Approximation Continuous wavelet transform Decomposition Generators Mathematical analysis Mathematical models Mechanical engineering Networks Neurons Nonlinear systems Nonlinearity Polynomials Stability Synchronous machines Testing Wavelet transforms |
| title | Synchronous machine model identification using continuous wavelet NARX network |
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