Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method
Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically comm...
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creator | Jaganathan, B. Venkatesh, S. Bhardwaj, Y. Sridhar, V. |
description | Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically commutated etc., In the design of such advantageous motors it becomes necessary for the estimation of the performance characteristics parameters such as back EMF, stator current, rotor speed, Torque etc., Many ideas have been proposed for the estimation of these characteristic parameters. This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of `winner takes it all' of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of "Winner takes it all" and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences. |
doi_str_mv | 10.1109/RAICS.2011.6069274 |
format | Conference Proceeding |
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This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of `winner takes it all' of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of "Winner takes it all" and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences.</description><identifier>ISBN: 9781424494781</identifier><identifier>ISBN: 1424494788</identifier><identifier>EISBN: 1424494753</identifier><identifier>EISBN: 9781424494767</identifier><identifier>EISBN: 1424494761</identifier><identifier>EISBN: 9781424494750</identifier><identifier>EISBN: 142449477X</identifier><identifier>EISBN: 9781424494774</identifier><identifier>DOI: 10.1109/RAICS.2011.6069274</identifier><language>eng</language><publisher>IEEE</publisher><subject>Angular Speed ; Artificial Neural Network ; BLDC Motor ; DC motors ; Electromagnetics ; Epoch ; Estimation ; Induction motors ; KSOFM ; Neurons ; Optimal Parameters ; Reluctance motors ; Rotors ; Stator current ; Torque ; Unsupervised learning ; Weight matrix</subject><ispartof>2011 IEEE Recent Advances in Intelligent Computational Systems, 2011, p.068-071</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/6069274$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2057,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6069274$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jaganathan, B.</creatorcontrib><creatorcontrib>Venkatesh, S.</creatorcontrib><creatorcontrib>Bhardwaj, Y.</creatorcontrib><creatorcontrib>Sridhar, V.</creatorcontrib><title>Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method</title><title>2011 IEEE Recent Advances in Intelligent Computational Systems</title><addtitle>RAICS</addtitle><description>Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically commutated etc., In the design of such advantageous motors it becomes necessary for the estimation of the performance characteristics parameters such as back EMF, stator current, rotor speed, Torque etc., Many ideas have been proposed for the estimation of these characteristic parameters. This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of `winner takes it all' of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of "Winner takes it all" and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences.</description><subject>Angular Speed</subject><subject>Artificial Neural Network</subject><subject>BLDC Motor</subject><subject>DC motors</subject><subject>Electromagnetics</subject><subject>Epoch</subject><subject>Estimation</subject><subject>Induction motors</subject><subject>KSOFM</subject><subject>Neurons</subject><subject>Optimal Parameters</subject><subject>Reluctance motors</subject><subject>Rotors</subject><subject>Stator current</subject><subject>Torque</subject><subject>Unsupervised learning</subject><subject>Weight matrix</subject><isbn>9781424494781</isbn><isbn>1424494788</isbn><isbn>1424494753</isbn><isbn>9781424494767</isbn><isbn>1424494761</isbn><isbn>9781424494750</isbn><isbn>142449477X</isbn><isbn>9781424494774</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kD1PwzAYhI0QElDyB2DxxtTgN3bseCzhqyJVJFrmyh9v2qAkjpIs5ddTRLnldM9wOh0ht8BiAKYfPhbLfB0nDCCWTOpEiTNyDSIRQguV8nMSaZX95wwuSTSOX-woKbUEdUU-y36qW9PQ3gymxQmHkeL4i6Y6dDRU1NDH4imnbZjCQO2Bvod96LC7H-kam4qWw8509Xfd7ejK9HSF0z74G3JRmWbE6OQzsnl53uRv86J8XeaLYl5rNs1VYhj3AKl3UjvwGWRaOOOrRHGlBVjFM8etBkArmIcq9cZa8BKddEohn5G7v9oaEbf9cFw9HLanI_gPrshRiQ</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Jaganathan, B.</creator><creator>Venkatesh, S.</creator><creator>Bhardwaj, Y.</creator><creator>Sridhar, V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method</title><author>Jaganathan, B. ; Venkatesh, S. ; Bhardwaj, Y. ; Sridhar, V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-72a03d115dc69c1d81894cadf2737941b738c3b911eb40d1f5dabb1d6ec6c77e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Angular Speed</topic><topic>Artificial Neural Network</topic><topic>BLDC Motor</topic><topic>DC motors</topic><topic>Electromagnetics</topic><topic>Epoch</topic><topic>Estimation</topic><topic>Induction motors</topic><topic>KSOFM</topic><topic>Neurons</topic><topic>Optimal Parameters</topic><topic>Reluctance motors</topic><topic>Rotors</topic><topic>Stator current</topic><topic>Torque</topic><topic>Unsupervised learning</topic><topic>Weight matrix</topic><toplevel>online_resources</toplevel><creatorcontrib>Jaganathan, B.</creatorcontrib><creatorcontrib>Venkatesh, S.</creatorcontrib><creatorcontrib>Bhardwaj, Y.</creatorcontrib><creatorcontrib>Sridhar, V.</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>Jaganathan, B.</au><au>Venkatesh, S.</au><au>Bhardwaj, Y.</au><au>Sridhar, V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method</atitle><btitle>2011 IEEE Recent Advances in Intelligent Computational Systems</btitle><stitle>RAICS</stitle><date>2011-09</date><risdate>2011</risdate><spage>068</spage><epage>071</epage><pages>068-071</pages><isbn>9781424494781</isbn><isbn>1424494788</isbn><eisbn>1424494753</eisbn><eisbn>9781424494767</eisbn><eisbn>1424494761</eisbn><eisbn>9781424494750</eisbn><eisbn>142449477X</eisbn><eisbn>9781424494774</eisbn><abstract>Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically commutated etc., In the design of such advantageous motors it becomes necessary for the estimation of the performance characteristics parameters such as back EMF, stator current, rotor speed, Torque etc., Many ideas have been proposed for the estimation of these characteristic parameters. This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of `winner takes it all' of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of "Winner takes it all" and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences.</abstract><pub>IEEE</pub><doi>10.1109/RAICS.2011.6069274</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Angular Speed Artificial Neural Network BLDC Motor DC motors Electromagnetics Epoch Estimation Induction motors KSOFM Neurons Optimal Parameters Reluctance motors Rotors Stator current Torque Unsupervised learning Weight matrix |
title | Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method |
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