Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor
This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate its efficiency. Applying statistical methods, the number of runs needed to obtain a...
- Autores:
-
Heredia López, Alfonso Jesús
Ramos, Guillermo A
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11410
- Palabra clave:
- Algorithms
Algoritmos
Motores eléctricos de inducción
Electric motors, Induction
Evolutionary algorithms
Estimation of parameters
AC motor
Comparison of algorithms
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
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Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| title |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| spellingShingle |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor Algorithms Algoritmos Motores eléctricos de inducción Electric motors, Induction Evolutionary algorithms Estimation of parameters AC motor Comparison of algorithms |
| title_short |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| title_full |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| title_fullStr |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| title_full_unstemmed |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| title_sort |
Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor |
| dc.creator.fl_str_mv |
Heredia López, Alfonso Jesús Ramos, Guillermo A |
| dc.contributor.author.none.fl_str_mv |
Heredia López, Alfonso Jesús Ramos, Guillermo A |
| dc.subject.lemb.eng.fl_str_mv |
Algorithms |
| topic |
Algorithms Algoritmos Motores eléctricos de inducción Electric motors, Induction Evolutionary algorithms Estimation of parameters AC motor Comparison of algorithms |
| dc.subject.lemb.spa.fl_str_mv |
Algoritmos |
| dc.subject.armarc.spa.fl_str_mv |
Motores eléctricos de inducción |
| dc.subject.armarc.eng.fl_str_mv |
Electric motors, Induction |
| dc.subject.proposal.eng.fl_str_mv |
Evolutionary algorithms Estimation of parameters AC motor Comparison of algorithms |
| description |
This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate its efficiency. Applying statistical methods, the number of runs needed to obtain a confidence level of 95% is calculated. With this value each algorithm is used to estimate the motor's parameters and, according to the results, is possible to find the best |
| publishDate |
2018 |
| dc.date.issued.spa.fl_str_mv |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2019-11-06T14:37:30Z |
| dc.date.available.none.fl_str_mv |
2019-11-06T14:37:30Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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info:eu-repo/semantics/article |
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publishedVersion |
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9781538667408 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10614/11410 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-030-03023-0_11 |
| identifier_str_mv |
9781538667408 |
| url |
http://hdl.handle.net/10614/11410 https://doi.org/10.1007/978-3-030-03023-0_11 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
| dc.relation.eng.fl_str_mv |
2018 IEEE 1st Colombian conference on applications in computational intelligence, ColCACI. (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, 5 October 2018) |
| dc.relation.cites.eng.fl_str_mv |
Ramos G.A., Lopez J.A. (2018) Comparison of Evolutionary Algorithms for Estimation of Parameters of the Equivalent Circuit of an AC Motor. In: Orjuela-Cañón A., Figueroa-García J., Arias-Londoño J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_11 |
| dc.relation.references.none.fl_str_mv |
1. Santos, V.S.: Procedimiento para determinar la eficiencia de motores asincrónicos en presencia de desbalance y armónicos en la tensión. Tesis doctoral, Universidad Central de las Villas, Santa Clara, Cuba (2014) 2. Gómez, J.R.: Determinación de la eficiencia de los motores asincrónicos con tensiones desbalanceadas en condiciones de campo. Tesis doctoral, Universidad Central de las Villas, Santa Clara, Cuba (2006) 3. Valencia García, D.F., et al.: Estudio Del Efecto De La Distorsión Armónica De Tensión Sobre La Eficiencia Y La Potencia Del Motor Trifásico De Inducción Mediante Modelos Eléctricos Y Térmicos. Proyecto grado maestría en ingeniería énfasis en energética. Universidad autónoma de occidente (2014) 4. Gómez, J.R., Quispe, E.C., De Armas, M.A., Viego, P.R.: Estimation of induction motor efficiency in-situ under unbalanced voltages using genetic algorithms. In: 18th International Conference on Electrical Machines, ICEM 2008, pp. 1–4. IEEE (2008) 5. Sakthivel, V.P., Subramanian, S.: On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization. Energy 36(3), 1713–1720 (2011) 6. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. Control Syst. 22(3), 52–67 (2002) 7. Mateu, E., Casal, J.: Tamaño de la muestra. Rev. Epidem. Med. Prev. 1, 8–14 (2003) 8. Alonge, F., et al.: Parameter identification of induction motor model using genetic algorithms. IEE Proc.-Control Theory. Appl. 145, 587–593 (1998) 9. Kennedy, J.: Particle swarm optimization. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1153-7_200581 10. Muñoz, M.A., López, J.A., Caicedo, E.F.: Inteligencia de enjambres: sociedades para la solución de problemas (una revisión) Ingeniería e Investigación. Universidad Nacional 2008, vol. 28, no. 2, pp. 119–130 (2008) 11. Koza, J.R.: Genetic Programing. On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992) 12. Tech Effigy Tutorials. (http://techeffigytutorials.blogspot.com.co/), http://techeffigytutorials.blogspot.com.co/2015/02/the-genetic-algorithm-explained.html. Accessed 25 May 2018 13. Song, H.M., Ibrahim, W.I., Abdullah, N.R.H.: Optimal load frequency control in single área power system using PID controller based on bacterial foraging & particle swarm optimization. ARPN J. Eng. Appl. Sci. 10(22), 10733–10739 (2015) |
| dc.rights.spa.fl_str_mv |
Derechos Reservados - Universidad Autónoma de Occidente |
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http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Derechos Reservados - Universidad Autónoma de Occidente https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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Páginas 126-136 |
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Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí |
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Springer |
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https://link.springer.com/chapter/10.1007/978-3-030-03023-0_11 https://ieeexplore.ieee.org/document/8484857 |
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Heredia López, Alfonso Jesúsf02ab3a5e87bde77748931c93e5166a9Ramos, Guillermo A85239b070062a6194cc1990cbc1d5974Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-06T14:37:30Z2019-11-06T14:37:30Z20189781538667408http://hdl.handle.net/10614/11410https://doi.org/10.1007/978-3-030-03023-0_11This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate its efficiency. Applying statistical methods, the number of runs needed to obtain a confidence level of 95% is calculated. With this value each algorithm is used to estimate the motor's parameters and, according to the results, is possible to find the bestConference Location: Medellín, Colombiaapplication/pdfPáginas 126-136engSpringer2018 IEEE 1st Colombian conference on applications in computational intelligence, ColCACI. (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, 5 October 2018)Ramos G.A., Lopez J.A. (2018) Comparison of Evolutionary Algorithms for Estimation of Parameters of the Equivalent Circuit of an AC Motor. In: Orjuela-Cañón A., Figueroa-García J., Arias-Londoño J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_111. Santos, V.S.: Procedimiento para determinar la eficiencia de motores asincrónicos en presencia de desbalance y armónicos en la tensión. Tesis doctoral, Universidad Central de las Villas, Santa Clara, Cuba (2014)2. Gómez, J.R.: Determinación de la eficiencia de los motores asincrónicos con tensiones desbalanceadas en condiciones de campo. Tesis doctoral, Universidad Central de las Villas, Santa Clara, Cuba (2006)3. Valencia García, D.F., et al.: Estudio Del Efecto De La Distorsión Armónica De Tensión Sobre La Eficiencia Y La Potencia Del Motor Trifásico De Inducción Mediante Modelos Eléctricos Y Térmicos. Proyecto grado maestría en ingeniería énfasis en energética. Universidad autónoma de occidente (2014)4. Gómez, J.R., Quispe, E.C., De Armas, M.A., Viego, P.R.: Estimation of induction motor efficiency in-situ under unbalanced voltages using genetic algorithms. In: 18th International Conference on Electrical Machines, ICEM 2008, pp. 1–4. IEEE (2008)5. Sakthivel, V.P., Subramanian, S.: On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization. Energy 36(3), 1713–1720 (2011)6. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. Control Syst. 22(3), 52–67 (2002)7. Mateu, E., Casal, J.: Tamaño de la muestra. Rev. Epidem. Med. Prev. 1, 8–14 (2003)8. Alonge, F., et al.: Parameter identification of induction motor model using genetic algorithms. IEE Proc.-Control Theory. Appl. 145, 587–593 (1998)9. Kennedy, J.: Particle swarm optimization. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1153-7_20058110. Muñoz, M.A., López, J.A., Caicedo, E.F.: Inteligencia de enjambres: sociedades para la solución de problemas (una revisión) Ingeniería e Investigación. Universidad Nacional 2008, vol. 28, no. 2, pp. 119–130 (2008)11. Koza, J.R.: Genetic Programing. On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)12. Tech Effigy Tutorials. (http://techeffigytutorials.blogspot.com.co/), http://techeffigytutorials.blogspot.com.co/2015/02/the-genetic-algorithm-explained.html. Accessed 25 May 201813. Song, H.M., Ibrahim, W.I., Abdullah, N.R.H.: Optimal load frequency control in single área power system using PID controller based on bacterial foraging & particle swarm optimization. ARPN J. Eng. Appl. Sci. 10(22), 10733–10739 (2015)Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2https://link.springer.com/chapter/10.1007/978-3-030-03023-0_11https://ieeexplore.ieee.org/document/8484857Comparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motorArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85AlgorithmsAlgoritmosMotores eléctricos de inducciónElectric motors, InductionEvolutionary algorithmsEstimation of parametersAC motorComparison of algorithmsPublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://dspace7-uao.metacatalogo.com/bitstreams/0f40a24b-1fd0-47ac-8117-bc5ef7970e99/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://dspace7-uao.metacatalogo.com/bitstreams/8ab6e1f7-f583-4ca0-b521-b1c0e45f3b5d/download20b5ba22b1117f71589c7318baa2c560MD53TEXTComparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor.pdf.txtComparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor.pdf.txtExtracted texttext/plain17018https://dspace7-uao.metacatalogo.com/bitstreams/19b752d3-b73f-47a2-a191-b748ea7b3569/download4c8c049130f43069565cfe33eba7918cMD55THUMBNAILComparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor.pdf.jpgComparison of evolutionary algorithms for estimation of parameters of the equivalent circuit of an AC motor.pdf.jpgGenerated Thumbnailimage/jpeg12471https://dspace7-uao.metacatalogo.com/bitstreams/c8aaea69-ffae-45ae-9362-154a52c386f8/download6fc3af4a5ea06b36bd994001ce2d9c82MD5610614/11410oai:dspace7-uao.metacatalogo.com:10614/114102024-01-19 16:03:50.073https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidentemetadata.onlyhttps://dspace7-uao.metacatalogo.comRepositorio UAOrepositorio@uao.edu.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 |