Pseudo-optimal five-level DCC modulation based on machine learning

This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested “if-else” statements. The o...

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Detalhes bibliográficos
Autores: Montero Robina, Pablo, Gordillo Álvarez, Francisco, Gómez-Estern, Fabio, Cuesta Rojo, Federico
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2023
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/152993
Acesso em linha:https://hdl.handle.net/11441/152993
https://doi.org/10.1016/j.ijepes.2023.109677
Access Level:Acceso aberto
Palavra-chave:Classification and regression trees
Diode-clamped converter
Mixed-integer linear optimization
Multilevel converter
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spelling Pseudo-optimal five-level DCC modulation based on machine learningMontero Robina, PabloGordillo Álvarez, FranciscoGómez-Estern, FabioCuesta Rojo, FedericoClassification and regression treesDiode-clamped converterMixed-integer linear optimizationMultilevel converterThis paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested “if-else” statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations.ElsevierIngeniería de Sistemas y AutomáticaTEP102: Ingeniería Automática y Robótica2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/152993https://doi.org/10.1016/j.ijepes.2023.109677reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Electrical Power and Energy Systems, 109677.https://www.sciencedirect.com/science/article/pii/S0142061523007342info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1529932026-06-17T12:51:07Z
dc.title.none.fl_str_mv Pseudo-optimal five-level DCC modulation based on machine learning
title Pseudo-optimal five-level DCC modulation based on machine learning
spellingShingle Pseudo-optimal five-level DCC modulation based on machine learning
Montero Robina, Pablo
Classification and regression trees
Diode-clamped converter
Mixed-integer linear optimization
Multilevel converter
title_short Pseudo-optimal five-level DCC modulation based on machine learning
title_full Pseudo-optimal five-level DCC modulation based on machine learning
title_fullStr Pseudo-optimal five-level DCC modulation based on machine learning
title_full_unstemmed Pseudo-optimal five-level DCC modulation based on machine learning
title_sort Pseudo-optimal five-level DCC modulation based on machine learning
dc.creator.none.fl_str_mv Montero Robina, Pablo
Gordillo Álvarez, Francisco
Gómez-Estern, Fabio
Cuesta Rojo, Federico
author Montero Robina, Pablo
author_facet Montero Robina, Pablo
Gordillo Álvarez, Francisco
Gómez-Estern, Fabio
Cuesta Rojo, Federico
author_role author
author2 Gordillo Álvarez, Francisco
Gómez-Estern, Fabio
Cuesta Rojo, Federico
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
TEP102: Ingeniería Automática y Robótica
dc.subject.none.fl_str_mv Classification and regression trees
Diode-clamped converter
Mixed-integer linear optimization
Multilevel converter
topic Classification and regression trees
Diode-clamped converter
Mixed-integer linear optimization
Multilevel converter
description This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested “if-else” statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/152993
https://doi.org/10.1016/j.ijepes.2023.109677
url https://hdl.handle.net/11441/152993
https://doi.org/10.1016/j.ijepes.2023.109677
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Electrical Power and Energy Systems, 109677.
https://www.sciencedirect.com/science/article/pii/S0142061523007342
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 15,198674

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