Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids
- Autores
- Avila, Luis Omar; de Paula, Mariano; Trimboli, Maximiliano Daniel; Carlucho, Ignacio
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent's policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.
Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina
Fil: Trimboli, Maximiliano Daniel. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carlucho, Ignacio. State University of Louisiana; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Materia
-
DEEP RL
MPPT
OPENAI GYM
PV SYSTEMS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/136641
Ver los metadatos del registro completo
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Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart GridsAvila, Luis Omarde Paula, MarianoTrimboli, Maximiliano DanielCarlucho, IgnacioDEEP RLMPPTOPENAI GYMPV SYSTEMShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent's policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Trimboli, Maximiliano Daniel. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carlucho, Ignacio. State University of Louisiana; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2020-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136641Avila, Luis Omar; de Paula, Mariano; Trimboli, Maximiliano Daniel; Carlucho, Ignacio; Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids; Elsevier Science; Applied Soft Computing; 97; 12-2020; 1-391568-4946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1568494620306499info:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2020.106711info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:44:06Zoai:ri.conicet.gov.ar:11336/136641instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:44:06.998CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
title |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
spellingShingle |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids Avila, Luis Omar DEEP RL MPPT OPENAI GYM PV SYSTEMS |
title_short |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
title_full |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
title_fullStr |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
title_full_unstemmed |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
title_sort |
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids |
dc.creator.none.fl_str_mv |
Avila, Luis Omar de Paula, Mariano Trimboli, Maximiliano Daniel Carlucho, Ignacio |
author |
Avila, Luis Omar |
author_facet |
Avila, Luis Omar de Paula, Mariano Trimboli, Maximiliano Daniel Carlucho, Ignacio |
author_role |
author |
author2 |
de Paula, Mariano Trimboli, Maximiliano Daniel Carlucho, Ignacio |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
DEEP RL MPPT OPENAI GYM PV SYSTEMS |
topic |
DEEP RL MPPT OPENAI GYM PV SYSTEMS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent's policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point. Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina Fil: Trimboli, Maximiliano Daniel. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias. Laboratorio de Control Automático; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Carlucho, Ignacio. State University of Louisiana; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
description |
Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent's policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/136641 Avila, Luis Omar; de Paula, Mariano; Trimboli, Maximiliano Daniel; Carlucho, Ignacio; Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids; Elsevier Science; Applied Soft Computing; 97; 12-2020; 1-39 1568-4946 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/136641 |
identifier_str_mv |
Avila, Luis Omar; de Paula, Mariano; Trimboli, Maximiliano Daniel; Carlucho, Ignacio; Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids; Elsevier Science; Applied Soft Computing; 97; 12-2020; 1-39 1568-4946 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1568494620306499 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2020.106711 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844614477902774272 |
score |
13.070432 |