Design Method and Application of Wavelet Neural Network for Direct Torque Control System
A torque ripple reduction technique based on wavelet network of direct torque control running in low-speed is presented. The wavelet network can accurately localizes the characteristics of a signal both in the time and frequency domains, and the occurring instants of the signal change can be identif...
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| creator | Ding Guangbin Pang Peilin |
| description | A torque ripple reduction technique based on wavelet network of direct torque control running in low-speed is presented. The wavelet network can accurately localizes the characteristics of a signal both in the time and frequency domains, and the occurring instants of the signal change can be identified by the multi-scale representation of the signal.Taking advantage of complex wavelet transform, combined information can be obtained from both the magnitudes and arguments of complex wavelet transform coefficients to extract the desired feature of the transient signal. The input nodes of the WN are the stator current error and the change in the stator current error and the output node of the WN is the stator resistance error. The synthesized method of recursive orthogonal least squares algorithm and improved Givens rotation is used to fulfill the wavelet network structure initialization and parameter identification, and then the accurate stator flux vector and electromagnetic torque are acquired by means of state estimator, optimizing the inverter control strategy. The simulation results show effectiveness of the proposed control algorithm. |
| doi_str_mv | 10.1109/ICEMI.2007.4351044 |
| format | Conference Proceeding |
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The wavelet network can accurately localizes the characteristics of a signal both in the time and frequency domains, and the occurring instants of the signal change can be identified by the multi-scale representation of the signal.Taking advantage of complex wavelet transform, combined information can be obtained from both the magnitudes and arguments of complex wavelet transform coefficients to extract the desired feature of the transient signal. The input nodes of the WN are the stator current error and the change in the stator current error and the output node of the WN is the stator resistance error. The synthesized method of recursive orthogonal least squares algorithm and improved Givens rotation is used to fulfill the wavelet network structure initialization and parameter identification, and then the accurate stator flux vector and electromagnetic torque are acquired by means of state estimator, optimizing the inverter control strategy. The simulation results show effectiveness of the proposed control algorithm.</description><identifier>ISBN: 1424411351</identifier><identifier>ISBN: 9781424411351</identifier><identifier>EISBN: 9781424411368</identifier><identifier>EISBN: 142441136X</identifier><identifier>DOI: 10.1109/ICEMI.2007.4351044</identifier><language>eng</language><publisher>IEEE</publisher><subject>combined information ; complex wavelet transform ; Data mining ; Design methodology ; direct torque control ; dynamic system identification ; Feature extraction ; Frequency domain analysis ; Induction motor ; Neural networks ; Signal processing ; Stators ; Torque control ; Wavelet domain ; wavelet network ; Wavelet transforms</subject><ispartof>2007 8th International Conference on Electronic Measurement and Instruments, 2007, p.3-821-3-824</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4351044$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4351044$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ding Guangbin</creatorcontrib><creatorcontrib>Pang Peilin</creatorcontrib><title>Design Method and Application of Wavelet Neural Network for Direct Torque Control System</title><title>2007 8th International Conference on Electronic Measurement and Instruments</title><addtitle>ICEMI</addtitle><description>A torque ripple reduction technique based on wavelet network of direct torque control running in low-speed is presented. The wavelet network can accurately localizes the characteristics of a signal both in the time and frequency domains, and the occurring instants of the signal change can be identified by the multi-scale representation of the signal.Taking advantage of complex wavelet transform, combined information can be obtained from both the magnitudes and arguments of complex wavelet transform coefficients to extract the desired feature of the transient signal. The input nodes of the WN are the stator current error and the change in the stator current error and the output node of the WN is the stator resistance error. The synthesized method of recursive orthogonal least squares algorithm and improved Givens rotation is used to fulfill the wavelet network structure initialization and parameter identification, and then the accurate stator flux vector and electromagnetic torque are acquired by means of state estimator, optimizing the inverter control strategy. The simulation results show effectiveness of the proposed control algorithm.</description><subject>combined information</subject><subject>complex wavelet transform</subject><subject>Data mining</subject><subject>Design methodology</subject><subject>direct torque control</subject><subject>dynamic system identification</subject><subject>Feature extraction</subject><subject>Frequency domain analysis</subject><subject>Induction motor</subject><subject>Neural networks</subject><subject>Signal processing</subject><subject>Stators</subject><subject>Torque control</subject><subject>Wavelet domain</subject><subject>wavelet network</subject><subject>Wavelet transforms</subject><isbn>1424411351</isbn><isbn>9781424411351</isbn><isbn>9781424411368</isbn><isbn>142441136X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kL1OwzAYAI0QElDyArD4BVo-21-cZKzSUiK1MFAJtsrxDxjSuDguqG8PEmU63XLDEXLNYMIYVLdNPV81Ew5QTFDkDBBPSFYVJUOOyJiQ5Sm5_JecnZNsGN4BgBUSIccL8jKzg3_t6cqmt2Co6g2d7nad1yr50NPg6LP6sp1N9MHuo-p-kb5D_KAuRDrz0epE1yF-7i2tQ59i6OjTYUh2e0XOnOoGmx05Iuu7-bq-Hy8fF009XY59BWlcSmNQ50woLYwWrkLJOWrnBBaqdUxYXrSGGanzSgPKFoRuheSlANeqkosRufnLemvtZhf9VsXD5vhC_AACJVL7</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Ding Guangbin</creator><creator>Pang Peilin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>Design Method and Application of Wavelet Neural Network for Direct Torque Control System</title><author>Ding Guangbin ; Pang Peilin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-86dd4c513ac3dc3f946224cff347abf13e27bd1d6c59c046b03cb362830fba823</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>combined information</topic><topic>complex wavelet transform</topic><topic>Data mining</topic><topic>Design methodology</topic><topic>direct torque control</topic><topic>dynamic system identification</topic><topic>Feature extraction</topic><topic>Frequency domain analysis</topic><topic>Induction motor</topic><topic>Neural networks</topic><topic>Signal processing</topic><topic>Stators</topic><topic>Torque control</topic><topic>Wavelet domain</topic><topic>wavelet network</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Ding Guangbin</creatorcontrib><creatorcontrib>Pang Peilin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ding Guangbin</au><au>Pang Peilin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Design Method and Application of Wavelet Neural Network for Direct Torque Control System</atitle><btitle>2007 8th International Conference on Electronic Measurement and Instruments</btitle><stitle>ICEMI</stitle><date>2007-08</date><risdate>2007</risdate><spage>3-821</spage><epage>3-824</epage><pages>3-821-3-824</pages><isbn>1424411351</isbn><isbn>9781424411351</isbn><eisbn>9781424411368</eisbn><eisbn>142441136X</eisbn><abstract>A torque ripple reduction technique based on wavelet network of direct torque control running in low-speed is presented. The wavelet network can accurately localizes the characteristics of a signal both in the time and frequency domains, and the occurring instants of the signal change can be identified by the multi-scale representation of the signal.Taking advantage of complex wavelet transform, combined information can be obtained from both the magnitudes and arguments of complex wavelet transform coefficients to extract the desired feature of the transient signal. The input nodes of the WN are the stator current error and the change in the stator current error and the output node of the WN is the stator resistance error. The synthesized method of recursive orthogonal least squares algorithm and improved Givens rotation is used to fulfill the wavelet network structure initialization and parameter identification, and then the accurate stator flux vector and electromagnetic torque are acquired by means of state estimator, optimizing the inverter control strategy. The simulation results show effectiveness of the proposed control algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ICEMI.2007.4351044</doi></addata></record> |
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| ispartof | 2007 8th International Conference on Electronic Measurement and Instruments, 2007, p.3-821-3-824 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | combined information complex wavelet transform Data mining Design methodology direct torque control dynamic system identification Feature extraction Frequency domain analysis Induction motor Neural networks Signal processing Stators Torque control Wavelet domain wavelet network Wavelet transforms |
| title | Design Method and Application of Wavelet Neural Network for Direct Torque Control System |
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