Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines

Multiphase machines have recently gained interest in the research community for their use in applications where high power density, wide speed range and fault-tolerant capabilities are required. The optimal control of such drives requires the consideration of voltage and current limits imposed by th...

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Autores: Bermúdez Guzmán, Mario, Gomozov, O., Kestelyn, Xavier, Barrero, Federico, Nguyen, N.K., Semail, E.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/151459
Acceso en línea:https://hdl.handle.net/11441/151459
https://doi.org/10.1016/j.matcom.2018.07.005
Access Level:acceso abierto
Palabra clave:Multiphase drives
Model predictive control
Current and voltage limits
Optimal reference currents
Real-time simulation environments
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spelling Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machinesBermúdez Guzmán, MarioGomozov, O.Kestelyn, XavierBarrero, FedericoNguyen, N.K.Semail, E.Multiphase drivesModel predictive controlCurrent and voltage limitsOptimal reference currentsReal-time simulation environmentsMultiphase machines have recently gained interest in the research community for their use in applications where high power density, wide speed range and fault-tolerant capabilities are required. The optimal control of such drives requires the consideration of voltage and current limits imposed by the power converter and the machine. While conventional three-phase drives have been extensively analyzed taking into account such limits, the same cannot be said in the multiphase drives’ case. This paper deals with this issue, where a novel two-stage Model Predictive optimal Control (2S-MPC) technique is presented, and a five-phase permanent magnet synchronous multiphase machine (PMSM) is used as a case example. The proposed method first applies a Continuous-Control-Set Model Predictive Control (CCS-MPC) stage to obtain the optimal real-time stator current reference for given DC-link voltage and stator current limits, exploiting the maximum performance characteristics of the multiphase drive. Then, a Finite-Control-Set Model Predictive Control (FCS-MPC) stage is utilized to generate the switching state in the power converter and force the stator current tracking. An experimental validation of the proposed controller is finally provided using a real-time simulation environment based on OPAL-RT technologiesMinisterio de Economía y Competitividad DPI2016-76144-RElsevierIngeniería EléctricaIngeniería ElectrónicaTEP196: Sistemas de Energía EléctricaTIC201: ACE-TiMinisterio de Economía y Competitividad (MINECO). España2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/151459https://doi.org/10.1016/j.matcom.2018.07.005reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMathematics and Computers in Simulation, 158, 148-161.DPI2016-76144-Rhttps://www.sciencedirect.com/science/article/pii/S0378475418301897info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1514592026-06-17T12:51:07Z
dc.title.none.fl_str_mv Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
title Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
spellingShingle Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
Bermúdez Guzmán, Mario
Multiphase drives
Model predictive control
Current and voltage limits
Optimal reference currents
Real-time simulation environments
title_short Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
title_full Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
title_fullStr Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
title_full_unstemmed Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
title_sort Model predictive optimal control considering current and voltage limitations: Real-time validation using OPAL-RT technologies and five-phase permanent magnet synchronous machines
dc.creator.none.fl_str_mv Bermúdez Guzmán, Mario
Gomozov, O.
Kestelyn, Xavier
Barrero, Federico
Nguyen, N.K.
Semail, E.
author Bermúdez Guzmán, Mario
author_facet Bermúdez Guzmán, Mario
Gomozov, O.
Kestelyn, Xavier
Barrero, Federico
Nguyen, N.K.
Semail, E.
author_role author
author2 Gomozov, O.
Kestelyn, Xavier
Barrero, Federico
Nguyen, N.K.
Semail, E.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica
Ingeniería Electrónica
TEP196: Sistemas de Energía Eléctrica
TIC201: ACE-Ti
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Multiphase drives
Model predictive control
Current and voltage limits
Optimal reference currents
Real-time simulation environments
topic Multiphase drives
Model predictive control
Current and voltage limits
Optimal reference currents
Real-time simulation environments
description Multiphase machines have recently gained interest in the research community for their use in applications where high power density, wide speed range and fault-tolerant capabilities are required. The optimal control of such drives requires the consideration of voltage and current limits imposed by the power converter and the machine. While conventional three-phase drives have been extensively analyzed taking into account such limits, the same cannot be said in the multiphase drives’ case. This paper deals with this issue, where a novel two-stage Model Predictive optimal Control (2S-MPC) technique is presented, and a five-phase permanent magnet synchronous multiphase machine (PMSM) is used as a case example. The proposed method first applies a Continuous-Control-Set Model Predictive Control (CCS-MPC) stage to obtain the optimal real-time stator current reference for given DC-link voltage and stator current limits, exploiting the maximum performance characteristics of the multiphase drive. Then, a Finite-Control-Set Model Predictive Control (FCS-MPC) stage is utilized to generate the switching state in the power converter and force the stator current tracking. An experimental validation of the proposed controller is finally provided using a real-time simulation environment based on OPAL-RT technologies
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/151459
https://doi.org/10.1016/j.matcom.2018.07.005
url https://hdl.handle.net/11441/151459
https://doi.org/10.1016/j.matcom.2018.07.005
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Mathematics and Computers in Simulation, 158, 148-161.
DPI2016-76144-R
https://www.sciencedirect.com/science/article/pii/S0378475418301897
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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