Torque ripple reduction for switched reluctance motors using global optimization algorithm
Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the curren...
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| Autor*in: |
Ben, Tong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of power electronics - [Singapore] : Springer Singapore, 2020, 22(2022), 11 vom: 03. Aug., Seite 1897-1907 |
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| Übergeordnetes Werk: |
volume:22 ; year:2022 ; number:11 ; day:03 ; month:08 ; pages:1897-1907 |
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| DOI / URN: |
10.1007/s43236-022-00501-2 |
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| Katalog-ID: |
SPR048454761 |
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| 520 | |a Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. | ||
| 650 | 4 | |a Switched reluctance motor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Torque ripple |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Turn-off angle optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Global optimization algorithm |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nie, Heng |4 aut | |
| 700 | 1 | |a Chen, Long |0 (orcid)0000-0001-5477-9935 |4 aut | |
| 700 | 1 | |a Jing, Libing |4 aut | |
| 700 | 1 | |a Yan, Rongge |4 aut | |
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10.1007/s43236-022-00501-2 doi (DE-627)SPR048454761 (SPR)s43236-022-00501-2-e DE-627 ger DE-627 rakwb eng Ben, Tong verfasserin aut Torque ripple reduction for switched reluctance motors using global optimization algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 Nie, Heng aut Chen, Long (orcid)0000-0001-5477-9935 aut Jing, Libing aut Yan, Rongge aut Enthalten in Journal of power electronics [Singapore] : Springer Singapore, 2020 22(2022), 11 vom: 03. Aug., Seite 1897-1907 (DE-627)1689175095 (DE-600)3007272-4 2093-4718 nnns volume:22 year:2022 number:11 day:03 month:08 pages:1897-1907 https://dx.doi.org/10.1007/s43236-022-00501-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 11 03 08 1897-1907 |
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10.1007/s43236-022-00501-2 doi (DE-627)SPR048454761 (SPR)s43236-022-00501-2-e DE-627 ger DE-627 rakwb eng Ben, Tong verfasserin aut Torque ripple reduction for switched reluctance motors using global optimization algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 Nie, Heng aut Chen, Long (orcid)0000-0001-5477-9935 aut Jing, Libing aut Yan, Rongge aut Enthalten in Journal of power electronics [Singapore] : Springer Singapore, 2020 22(2022), 11 vom: 03. Aug., Seite 1897-1907 (DE-627)1689175095 (DE-600)3007272-4 2093-4718 nnns volume:22 year:2022 number:11 day:03 month:08 pages:1897-1907 https://dx.doi.org/10.1007/s43236-022-00501-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 11 03 08 1897-1907 |
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10.1007/s43236-022-00501-2 doi (DE-627)SPR048454761 (SPR)s43236-022-00501-2-e DE-627 ger DE-627 rakwb eng Ben, Tong verfasserin aut Torque ripple reduction for switched reluctance motors using global optimization algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 Nie, Heng aut Chen, Long (orcid)0000-0001-5477-9935 aut Jing, Libing aut Yan, Rongge aut Enthalten in Journal of power electronics [Singapore] : Springer Singapore, 2020 22(2022), 11 vom: 03. Aug., Seite 1897-1907 (DE-627)1689175095 (DE-600)3007272-4 2093-4718 nnns volume:22 year:2022 number:11 day:03 month:08 pages:1897-1907 https://dx.doi.org/10.1007/s43236-022-00501-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 11 03 08 1897-1907 |
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10.1007/s43236-022-00501-2 doi (DE-627)SPR048454761 (SPR)s43236-022-00501-2-e DE-627 ger DE-627 rakwb eng Ben, Tong verfasserin aut Torque ripple reduction for switched reluctance motors using global optimization algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 Nie, Heng aut Chen, Long (orcid)0000-0001-5477-9935 aut Jing, Libing aut Yan, Rongge aut Enthalten in Journal of power electronics [Singapore] : Springer Singapore, 2020 22(2022), 11 vom: 03. Aug., Seite 1897-1907 (DE-627)1689175095 (DE-600)3007272-4 2093-4718 nnns volume:22 year:2022 number:11 day:03 month:08 pages:1897-1907 https://dx.doi.org/10.1007/s43236-022-00501-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 11 03 08 1897-1907 |
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10.1007/s43236-022-00501-2 doi (DE-627)SPR048454761 (SPR)s43236-022-00501-2-e DE-627 ger DE-627 rakwb eng Ben, Tong verfasserin aut Torque ripple reduction for switched reluctance motors using global optimization algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 Nie, Heng aut Chen, Long (orcid)0000-0001-5477-9935 aut Jing, Libing aut Yan, Rongge aut Enthalten in Journal of power electronics [Singapore] : Springer Singapore, 2020 22(2022), 11 vom: 03. Aug., Seite 1897-1907 (DE-627)1689175095 (DE-600)3007272-4 2093-4718 nnns volume:22 year:2022 number:11 day:03 month:08 pages:1897-1907 https://dx.doi.org/10.1007/s43236-022-00501-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 11 03 08 1897-1907 |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. 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Ben, Tong |
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Ben, Tong misc Switched reluctance motor misc Torque ripple misc Turn-off angle optimization misc Global optimization algorithm Torque ripple reduction for switched reluctance motors using global optimization algorithm |
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Torque ripple reduction for switched reluctance motors using global optimization algorithm Switched reluctance motor (dpeaa)DE-He213 Torque ripple (dpeaa)DE-He213 Turn-off angle optimization (dpeaa)DE-He213 Global optimization algorithm (dpeaa)DE-He213 |
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torque ripple reduction for switched reluctance motors using global optimization algorithm |
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Torque ripple reduction for switched reluctance motors using global optimization algorithm |
| abstract |
Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance–current–position and torque–current–position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Torque ripple reduction for switched reluctance motors using global optimization algorithm |
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https://dx.doi.org/10.1007/s43236-022-00501-2 |
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Nie, Heng Chen, Long Jing, Libing Yan, Rongge |
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Nie, Heng Chen, Long Jing, Libing Yan, Rongge |
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10.1007/s43236-022-00501-2 |
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| score |
7.399868 |