Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms

[EN] Solar photovoltaic systems are widely used; however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: P...

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Autores: Águila-León, Jesús, Chiñas-Palacios, Cristian, Vargas-Salgado, Carlos|||0000-0002-9259-8374, Díaz-Bello, Dácil|||0000-0001-8416-9601
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/193238
Acesso em linha:https://riunet.upv.es/handle/10251/193238
Access Level:acceso abierto
Palavra-chave:Optimization
Metaheuristic algorithms
Grey Wolf Optimization
Microgrid
Photovoltaic
Maximum Power Point Tracking
Bio-inspired algorithm
INGENIERIA ELECTRICA
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repository_id_str
dc.title.none.fl_str_mv Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
title Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
spellingShingle Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
Águila-León, Jesús
Optimization
Metaheuristic algorithms
Grey Wolf Optimization
Microgrid
Photovoltaic
Maximum Power Point Tracking
Bio-inspired algorithm
INGENIERIA ELECTRICA
title_short Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
title_full Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
title_fullStr Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
title_full_unstemmed Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
title_sort Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms
dc.creator.none.fl_str_mv Águila-León, Jesús
Chiñas-Palacios, Cristian
Vargas-Salgado, Carlos|||0000-0002-9259-8374
Díaz-Bello, Dácil|||0000-0001-8416-9601
author Águila-León, Jesús
author_facet Águila-León, Jesús
Chiñas-Palacios, Cristian
Vargas-Salgado, Carlos|||0000-0002-9259-8374
Díaz-Bello, Dácil|||0000-0001-8416-9601
author_role author
author2 Chiñas-Palacios, Cristian
Vargas-Salgado, Carlos|||0000-0002-9259-8374
Díaz-Bello, Dácil|||0000-0001-8416-9601
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Eléctrica
Instituto Universitario de Investigación de Ingeniería Energética
Escuela Técnica Superior de Ingeniería Industrial
Ajuntament de València
Agencia Estatal de Investigación
Fundació València Clima i Energia
Cátedra de Transición Energética Urbana, Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Optimization
Metaheuristic algorithms
Grey Wolf Optimization
Microgrid
Photovoltaic
Maximum Power Point Tracking
Bio-inspired algorithm
INGENIERIA ELECTRICA
topic Optimization
Metaheuristic algorithms
Grey Wolf Optimization
Microgrid
Photovoltaic
Maximum Power Point Tracking
Bio-inspired algorithm
INGENIERIA ELECTRICA
description [EN] Solar photovoltaic systems are widely used; however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: Perturb and Observe and Incremental Conductance. However, such algorithms perform differently when the Solar photovoltaic system works under sudden solar irradiation changes, temperature, and load changes. This research proposes an opti-mized Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm using the MATLAB/Simulink software as an alternative to the traditional techniques. Global efficiency and Root Mean Square Error evaluate the controller's performance. The response time is analyzed using the Grey Wolf Optimizer algorithm, Wolf Optimizer Algorithm, Simulated Annealing, and Particle Swarm Optimization. These four metaheuristic algorithms are compared to the Perturb and Observe, and Incremental Conductance algorithms. The models are analyzed for the transient state and full-day operation scenarios for constant and variable ir-radiations, temperatures, and loads. The comparative results show that the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm has superior performance, giving an average 6% output power higher than the other controllers under the test scenarios evaluated. The efficiency of the proposed model was, on average, 3% higher than the Incremental Conductance and Perturb & Observe controllers. For the MPPT controller tunning stage, the Grey Wolf Optimizer Algorithm had the best performance with an RMSE of 255.3549 with a compute time of 27.3 min; the worst performing was the Particle Swarm Optimization with an RMSE of 332.4075 and 27.8 min computation time. The proposed GWO optimized MPPT controller had the faster settling time for each irradiation level compared, with an average of 0.175 s. Also, results showed an improvement of the system response throughout the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm since a lower curling effect is obtained at power converter outputs.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/193238
url https://riunet.upv.es/handle/10251/193238
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PID2021-128822OB-I00
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
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:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithmsÁguila-León, JesúsChiñas-Palacios, CristianVargas-Salgado, Carlos|||0000-0002-9259-8374Díaz-Bello, Dácil|||0000-0001-8416-9601OptimizationMetaheuristic algorithmsGrey Wolf OptimizationMicrogridPhotovoltaicMaximum Power Point TrackingBio-inspired algorithmINGENIERIA ELECTRICA[EN] Solar photovoltaic systems are widely used; however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: Perturb and Observe and Incremental Conductance. However, such algorithms perform differently when the Solar photovoltaic system works under sudden solar irradiation changes, temperature, and load changes. This research proposes an opti-mized Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm using the MATLAB/Simulink software as an alternative to the traditional techniques. Global efficiency and Root Mean Square Error evaluate the controller's performance. The response time is analyzed using the Grey Wolf Optimizer algorithm, Wolf Optimizer Algorithm, Simulated Annealing, and Particle Swarm Optimization. These four metaheuristic algorithms are compared to the Perturb and Observe, and Incremental Conductance algorithms. The models are analyzed for the transient state and full-day operation scenarios for constant and variable ir-radiations, temperatures, and loads. The comparative results show that the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm has superior performance, giving an average 6% output power higher than the other controllers under the test scenarios evaluated. The efficiency of the proposed model was, on average, 3% higher than the Incremental Conductance and Perturb & Observe controllers. For the MPPT controller tunning stage, the Grey Wolf Optimizer Algorithm had the best performance with an RMSE of 255.3549 with a compute time of 27.3 min; the worst performing was the Particle Swarm Optimization with an RMSE of 332.4075 and 27.8 min computation time. The proposed GWO optimized MPPT controller had the faster settling time for each irradiation level compared, with an average of 0.175 s. Also, results showed an improvement of the system response throughout the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm since a lower curling effect is obtained at power converter outputs.This research has been funded by the PURPOSED project (ref: PID2021-128822OB-I00), financed by the Spanish State Investigation Agency and by of the Catedra de Transicion Energetica Urbana -a chair hosted at the Universitat Politècnica de València and funded by Ajuntament de València-Las Naves and Fundacio València Clima i Energia.ElsevierDepartamento de Ingeniería EléctricaInstituto Universitario de Investigación de Ingeniería EnergéticaEscuela Técnica Superior de Ingeniería IndustrialAjuntament de ValènciaAgencia Estatal de InvestigaciónFundació València Clima i EnergiaCátedra de Transición Energética Urbana, Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/193238reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PID2021-128822OB-I00open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1932382026-06-13T07:49:27Z
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