Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial
Se proponen Deep Q-Networks, un algoritmo de aprendizaje por refuerzo profundo, para hacer el control MPPT de un arreglo de paneles solares en caso de sombreado parcial. Se comprueba y compara su funcionamiento en simulaciones y en una implementación física.
- Autores:
-
Torres Villamizar, María Isabella
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/58974
- Acceso en línea:
- http://hdl.handle.net/1992/58974
- Palabra clave:
- Aprendizaje por refuerzo profundo
DQN
MPPT
Paneles solares
Sombreado parcial
Energía solar fotovoltaica
Reinforcement learning
Condición de sombreado parcial
Ingeniería
- Rights
- openAccess
- License
- Attribution-NoDerivatives 4.0 Internacional
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Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| dc.title.alternative.none.fl_str_mv |
Deep reinforcement learning approach for maximum power point tracking of photovoltaic system under partial shading conditions based on actor-critic agents |
| title |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| spellingShingle |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial Aprendizaje por refuerzo profundo DQN MPPT Paneles solares Sombreado parcial Energía solar fotovoltaica Reinforcement learning Condición de sombreado parcial Ingeniería |
| title_short |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| title_full |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| title_fullStr |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| title_full_unstemmed |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| title_sort |
Aprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcial |
| dc.creator.fl_str_mv |
Torres Villamizar, María Isabella |
| dc.contributor.advisor.none.fl_str_mv |
Bressan, Michael |
| dc.contributor.author.none.fl_str_mv |
Torres Villamizar, María Isabella |
| dc.contributor.jury.none.fl_str_mv |
Giraldo Trujillo, Luis Felipe |
| dc.subject.keyword.none.fl_str_mv |
Aprendizaje por refuerzo profundo DQN MPPT Paneles solares Sombreado parcial Energía solar fotovoltaica Reinforcement learning Condición de sombreado parcial |
| topic |
Aprendizaje por refuerzo profundo DQN MPPT Paneles solares Sombreado parcial Energía solar fotovoltaica Reinforcement learning Condición de sombreado parcial Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
Se proponen Deep Q-Networks, un algoritmo de aprendizaje por refuerzo profundo, para hacer el control MPPT de un arreglo de paneles solares en caso de sombreado parcial. Se comprueba y compara su funcionamiento en simulaciones y en una implementación física. |
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2022 |
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2022-07-19T20:34:01Z |
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2022-07-19T20:34:01Z |
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2022 |
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Trabajo de grado - Pregrado |
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Statistics time series, trends in renewable energy. https://www.irena.org/Statistics/View-Data-by-Topic/Capacity-and-Generation/ Statistics-Time-Series, publisher=IRENA. K. Ishaque and Z. Salam, A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition, IEEE Transactions on Industrial Electronics, 2012. S. K. Pandey, S. L. Patil, D. Ginoya, U. M. Chaskar, and S. B. Phadke, Robust control of mismatched buck dc dc converters by pwm-based sliding mode control schemes, Control Engineering Practice, 2019. G. Hou, Y. Ke, and C. Huang, A flexible constant power generation scheme for photovoltaic system by error-based active disturbance rejection control and perturb observe, Energy, 2021. K.-Y. Chou, S.-T. Yang, C.-S. Yang, and Y.-P. Chen, Maximum power point tracking of photovoltaic system based on reinforcement learning, Institute of Electrical and Control Engineering, 2019. V. Smil, Examining energy transitions: A dozen insights based on performance, Elservier, 2016. Energy outlook. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2022.pdf, 2020. Cuota de energías renovables en la producción de electricidad. https://datos.enerdata.net/energias-renovables/produccion-electricidad-renovable.html. S. H. Hanzaei, S. A. Gorji, and M. Ektesabi, A scheme-based review of mppt techniques with respect to input variables including solar irradiance and pv arrays¿ temperature, 2020. F. Belhachat and C. Larbes, A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions, Renewable and Sustainable Energy Reviews, 2018. F. Belhachat and C. Larbes, Comprehensive review on global maximum power point tracking techniques for pv systems subjected to partial shading conditions, Solar Energy, 2019. H. Rezk, A. Fathy, and A. Y. Abdelaziz, A comparison of different global mppt techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions, Renewable and Sustainable Energy Reviews, 2017. S. Motahhira, A. E. Hammoumib, and A. E. Ghzizalb, The most used mppt algorithms: Review and the suitable low-cost embedded board for each algorithm, Journal of Cleaner Production, 2020. M. S. Wasim, S. H. Muhammad Amjad and, M. A. Abbasi, A. R. Bhatti, and S. Muyeen, A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions, Energy Reports, 2022. M. A. Zeddini, M. Turki, and M. F. Mimouni, Optimization of pv energy conversion system using reinforcement learning algorithm, Sciences and Techniques of Automatic control computer engineering, 2020. B. C. Phan, Y.-C. Lai, and C. E. Lin, A deep reinforcement learning-based mppt control for pv systems under partial shading condition, Sensors, 2020. L. Avila, M. D. Paula, M. Trimboli, and I. Carlucho, Deep reinforcement learning approach for mppt control of partially shaded pv systems in smart grids, Applied Soft Computing Journal, 2020. M. Alqarni and M. K. Darwish, Maximum power point tracking for photovoltaic system: Modified perturb and observe algorithm, 47th International Universities Power Engineering Conference (UPEC), 2012. J. P. Ara ujo, M. A. Figueiredo, and M. A. Botto, Control with adaptive q-learning: A comparison for two classical control problems, Engineering Applications of Artificial Intelligence, 2022. Deep q-network agents. https://la.mathworks.com/help/reinforcement-learning/ug/dqn-agents.html. Accessed: 2022-05-20. M. K. Giri and S. Majumder, Deep q-learning based optimal resource allocation method for energy harvested cognitive radio networks, Physical Communication, 2022. K. Wang, D. Hong, J. Ma, K. L. Man, K. Huang, and X. Huang, Maximum power point tracking of photovoltaic systems using deep q-networks, IEEE 18th International Conference on Industrial Informatics, 2020. Solmetric, Pva-1000s pv analyzer kit. https://sep.yimg.com/ty/cdn/yhst-77580361692593/PVA1500_ProductSheet_sm3.pdf?t=1656620570&. J. F. Gaviria, G. Narváez, C. Guillen, L. F. Giraldo, and M. Bressan, Machine learning in photovoltaic systems: A review. T. Instruments, Tl494 pulse-width-modulation control circuits. https://www.ti.com/lit/gpn/tl494. |
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Attribution-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bressan, Michaelvirtual::2970-1Torres Villamizar, María Isabella1053c544-2d85-4656-ba80-a5c50d707cfc600Giraldo Trujillo, Luis Felipe2022-07-19T20:34:01Z2022-07-19T20:34:01Z2022http://hdl.handle.net/1992/58974instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Se proponen Deep Q-Networks, un algoritmo de aprendizaje por refuerzo profundo, para hacer el control MPPT de un arreglo de paneles solares en caso de sombreado parcial. Se comprueba y compara su funcionamiento en simulaciones y en una implementación física.Ingeniero ElectrónicoPregrado44 páginasapplication/pdfspaUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAprendizaje por refuerzo profundo para extraer la máxima transferencia de potencia para sistemas fotovoltaicos bajo el efecto de sombreado parcialDeep reinforcement learning approach for maximum power point tracking of photovoltaic system under partial shading conditions based on actor-critic agentsTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPAprendizaje por refuerzo profundoDQNMPPTPaneles solaresSombreado parcialEnergía solar fotovoltaicaReinforcement learningCondición de sombreado parcialIngenieríaStatistics time series, trends in renewable energy. https://www.irena.org/Statistics/View-Data-by-Topic/Capacity-and-Generation/ Statistics-Time-Series, publisher=IRENA.K. Ishaque and Z. Salam, A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition, IEEE Transactions on Industrial Electronics, 2012.S. K. Pandey, S. L. Patil, D. Ginoya, U. M. Chaskar, and S. B. Phadke, Robust control of mismatched buck dc dc converters by pwm-based sliding mode control schemes, Control Engineering Practice, 2019.G. Hou, Y. Ke, and C. Huang, A flexible constant power generation scheme for photovoltaic system by error-based active disturbance rejection control and perturb observe, Energy, 2021.K.-Y. Chou, S.-T. Yang, C.-S. Yang, and Y.-P. Chen, Maximum power point tracking of photovoltaic system based on reinforcement learning, Institute of Electrical and Control Engineering, 2019.V. Smil, Examining energy transitions: A dozen insights based on performance, Elservier, 2016.Energy outlook. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2022.pdf, 2020.Cuota de energías renovables en la producción de electricidad. https://datos.enerdata.net/energias-renovables/produccion-electricidad-renovable.html.S. H. Hanzaei, S. A. Gorji, and M. Ektesabi, A scheme-based review of mppt techniques with respect to input variables including solar irradiance and pv arrays¿ temperature, 2020.F. Belhachat and C. Larbes, A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions, Renewable and Sustainable Energy Reviews, 2018.F. Belhachat and C. Larbes, Comprehensive review on global maximum power point tracking techniques for pv systems subjected to partial shading conditions, Solar Energy, 2019.H. Rezk, A. Fathy, and A. Y. Abdelaziz, A comparison of different global mppt techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions, Renewable and Sustainable Energy Reviews, 2017.S. Motahhira, A. E. Hammoumib, and A. E. Ghzizalb, The most used mppt algorithms: Review and the suitable low-cost embedded board for each algorithm, Journal of Cleaner Production, 2020.M. S. Wasim, S. H. Muhammad Amjad and, M. A. Abbasi, A. R. Bhatti, and S. Muyeen, A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions, Energy Reports, 2022.M. A. Zeddini, M. Turki, and M. F. Mimouni, Optimization of pv energy conversion system using reinforcement learning algorithm, Sciences and Techniques of Automatic control computer engineering, 2020.B. C. Phan, Y.-C. Lai, and C. E. Lin, A deep reinforcement learning-based mppt control for pv systems under partial shading condition, Sensors, 2020.L. Avila, M. D. Paula, M. Trimboli, and I. Carlucho, Deep reinforcement learning approach for mppt control of partially shaded pv systems in smart grids, Applied Soft Computing Journal, 2020.M. Alqarni and M. K. Darwish, Maximum power point tracking for photovoltaic system: Modified perturb and observe algorithm, 47th International Universities Power Engineering Conference (UPEC), 2012.J. P. Ara ujo, M. A. Figueiredo, and M. A. Botto, Control with adaptive q-learning: A comparison for two classical control problems, Engineering Applications of Artificial Intelligence, 2022.Deep q-network agents. https://la.mathworks.com/help/reinforcement-learning/ug/dqn-agents.html. Accessed: 2022-05-20.M. K. Giri and S. Majumder, Deep q-learning based optimal resource allocation method for energy harvested cognitive radio networks, Physical Communication, 2022.K. Wang, D. Hong, J. Ma, K. L. Man, K. Huang, and X. Huang, Maximum power point tracking of photovoltaic systems using deep q-networks, IEEE 18th International Conference on Industrial Informatics, 2020.Solmetric, Pva-1000s pv analyzer kit. https://sep.yimg.com/ty/cdn/yhst-77580361692593/PVA1500_ProductSheet_sm3.pdf?t=1656620570&.J. F. Gaviria, G. Narváez, C. Guillen, L. F. Giraldo, and M. Bressan, Machine learning in photovoltaic systems: A review.T. 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