A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions

Ilustraciones a color, diagramas

Autores:: Bula Oyuela, Carlos Mauricio

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
dc.title.translated.spa.fl_str_mv	Control de la frecuencia de carga basado en el aprendizaje por refuerzo para sistemas de potencia teniendo en cuenta las no linealidades y otras interacciones de control
title	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
spellingShingle	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions 530 - Física::537 - Electricidad y electrónica 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Aprendizaje por refuerzo Control automático de frecuencia Reinforcement learning (Machine learning) Automatic frequency control load frequency control reinforcement learning proximal policy optimization renewable energies aprendizaje por refuerzo control de frecuencia energías renovables Energías renovables
title_short	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
title_full	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
title_fullStr	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
title_full_unstemmed	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
title_sort	A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions
dc.creator.fl_str_mv	Bula Oyuela, Carlos Mauricio
dc.contributor.advisor.none.fl_str_mv	Rivera Rodríguez, Sergio Raúl
dc.contributor.author.none.fl_str_mv	Bula Oyuela, Carlos Mauricio
dc.contributor.researchgroup.spa.fl_str_mv	Inteligencia Computacional Aplicada a Sistemas de Potencia
dc.subject.ddc.spa.fl_str_mv	530 - Física::537 - Electricidad y electrónica 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	530 - Física::537 - Electricidad y electrónica 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Aprendizaje por refuerzo Control automático de frecuencia Reinforcement learning (Machine learning) Automatic frequency control load frequency control reinforcement learning proximal policy optimization renewable energies aprendizaje por refuerzo control de frecuencia energías renovables Energías renovables
dc.subject.lemb.spa.fl_str_mv	Aprendizaje por refuerzo Control automático de frecuencia
dc.subject.lemb.eng.fl_str_mv	Reinforcement learning (Machine learning) Automatic frequency control
dc.subject.proposal.eng.fl_str_mv	load frequency control reinforcement learning proximal policy optimization renewable energies
dc.subject.proposal.spa.fl_str_mv	aprendizaje por refuerzo control de frecuencia energías renovables
dc.subject.bne.spa.fl_str_mv	Energías renovables
description	Ilustraciones a color, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-17T18:50:48Z
dc.date.available.none.fl_str_mv	2024-07-17T18:50:48Z
dc.date.issued.none.fl_str_mv	2024-01-31
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
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dc.type.content.spa.fl_str_mv	Text
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86539
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86539 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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Wang, Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing. Wiley-IEEE Press, 2003. H. Bevrani, Robust Power System Frequency Control Second. 2 ed., 2014. F. Report, ‘‘First Report of Power System Stability,’’ Transactions of the American Institute of Electrical Engineers, vol. 56, no. 2, pp. 261--282, 1937. Y. Liu, S. You, J. Tan, Y. Zhang, and Y. Liu, ‘‘Frequency response assessment and enhancement of the u.s. power grids toward extra-high photovoltaic generation penetrations—an industry perspective,’’ IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 3438--3449, 2018. P. Kundur, Power System Stability and Control. 2009. J. Machowski, J. W. Bialek, and J. R. Bumby, Power System Dynamics: Stability and Control. John Wiley and Sons, Ltd, 2 ed., 2008. E. Benham, ‘‘Si measurement system chart,’’ 2017-08-24 2017. National Fire Protection Association, NFPA 70: National Electrical Code. Quincy, MA, USA: NFPA, 2023 ed., 2023. 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spelling	Reconocimiento 4.0 Internacionalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rivera Rodríguez, Sergio Raúlebc09c48c256e8bad61b48321e3a32c5Bula Oyuela, Carlos Mauricio1788f9d0060ffc946f5ae5471a539652Inteligencia Computacional Aplicada a Sistemas de Potencia2024-07-17T18:50:48Z2024-07-17T18:50:48Z2024-01-31https://repositorio.unal.edu.co/handle/unal/86539Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones a color, diagramasConsidering the advances of civilizations and the limited existence of resources, concerns about the sustainability of that progress are increasing. There is a rising need for affordable, reliable, and sustainable energy sources. Different international organisms recognize the crucial role of energy in achieving global sustainability, highlighting the need for a transition to modern and green energy alternatives. This transition to renewable energy introduces complexities in control systems, requiring reliable load-frequency controllers. This document discusses the limitations of traditional PI control strategies and studies the use of a reinforcement learning-based algorithm, Proximal Policy Optimization (PPO), in power system control. Results demonstrate the efficacy of PPO in effectively reducing area control error, handling nonlinearities, and adapting to disturbances in the system. The adaptability of PPO to different situations and system changes, with its system-agnostic nature, makes it a promising candidate for improving power system control. Finally, practical considerations such as computational requirements, communication delays, measurement noise, and hardware constraints are acknowledged as challenges that need further exploration for the real-world deployment of PPO in power grid environments. The paper concludes by calling for additional research to address these challenges and optimize the algorithm for real-time applications, emphasizing its advantages in the transition to renewable energies.Dada la evolución de las civilizaciones y la existencia limitada de recursos, hay crecientes preocupaciones sobre la sostenibilidad de ese progreso. Existe una creciente necesidad de fuentes de energía asequibles, confiables y sostenibles. Diferentes organismos internacionales reconocen el papel crucial de la energía en conseguir la sostenibilidad global, destacando la necesidad de una transición hacia alternativas energéticas modernas y ecológicas. Esta transición a la energía renovable añade complejidades en los sistemas de control, requiriendo controladores de frecuencia confiables. Este documento discute las limitaciones de las estrategias tradicionales de control proporcional-integral (PI) y estudia el uso de un algoritmo basado en aprendizaje por refuerzo, específicamente el algoritmo de Optimización de Política Próxima (PPO), en el control del sistema de potencia. Los resultados demuestran la eficacia de PPO al reducir eficazmente el error de control de área, manejar no linealidades y adaptarse a las perturbaciones en el sistema. La capacidad de adaptación de PPO a diferentes situaciones y cambios en el sistema, junto con su naturaleza agnóstica del sistema, lo convierte en una opción prometedora para mejorar el control del sistema de potencia. Finalmente, se reconocen consideraciones prácticas como requisitos computacionales, retrasos en la comunicación, ruido de medición y limitaciones de hardware, como desafíos que necesitan una exploración adicional para la implementación del mundo real de PPO en entornos de redes eléctricas. El artículo concluye instando a investigaciones adicionales para abordar estos desafíos y optimizar el algoritmo para aplicaciones en el mundo real, haciendo énfasis en sus ventajas para la transición hacia las energías renovables. (Texto tomado de la fuente)MaestríaMágister en Ingeniería - Automatización IndustrialReinforcement Learningxi, 66 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física::537 - Electricidad y electrónica620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAprendizaje por refuerzoControl automático de frecuenciaReinforcement learning (Machine learning)Automatic frequency controlload frequency controlreinforcement learningproximal policy optimizationrenewable energiesaprendizaje por refuerzocontrol de frecuenciaenergías renovablesEnergías renovablesA reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactionsControl de la frecuencia de carga basado en el aprendizaje por refuerzo para sistemas de potencia teniendo en cuenta las no linealidades y otras interacciones de controlTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMGrammarly, ‘‘Grammarly handbook,’’ 2024. 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Accessed: January 30, 2024.BibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86539/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1018497046.2024.pdf1018497046.2024.pdfTesis en Magíster en Automatización Industrial - Investigaciónapplication/pdf3417658https://repositorio.unal.edu.co/bitstream/unal/86539/2/1018497046.2024.pdfdfb842b5493e2e535dd96f8100302150MD52THUMBNAIL1018497046.2024.pdf.jpg1018497046.2024.pdf.jpgGenerated Thumbnailimage/jpeg5002https://repositorio.unal.edu.co/bitstream/unal/86539/3/1018497046.2024.pdf.jpg8eecca23491ff467dc4b3684c3f00022MD53unal/86539oai:repositorio.unal.edu.co:unal/865392024-07-17 23:05:48.839Repositorio Institucional Universidad Nacional de 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A reinforcement learning based load frequency control for power systems considering nonlinearities and other control interactions

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