- -

Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

Mostrar el registro completo del ítem

Sierra-García, JE.; Santos, M. (2021). Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial. 18(4):327-335. https://doi.org/10.4995/riai.2021.16111

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/173784

Ficheros en el ítem

Thumbnail
Nombre: Sierra-GarciaSantos ...
Tamaño: 3.071Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas
Otro titulo: Neural networks and reinforcement learning in wind turbine control
Autor: Sierra-García, J. E. Santos, M.
Fecha difusión:
Resumen:
[EN] Pitch control of wind turbines is complex due to the intrinsic non-linear behavior of these devices, and the external disturbances they are subjected to related to changing wind conditions and other meteorological ...[+]


[ES] El control del ángulo de las palas de las turbinas eólicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que están sometidas debido a las condiciones ...[+]
Palabras clave: Wind turbines , Pitch control , Intelligent control , Neural networks reinforcement learning , Turbinas eólicas , Aerogeneradores , Control del ángulo de las palas , Control inteligente , Redes neuronales aprendizaje por refuerzo
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2021.16111
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2021.16111
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094902-B-C21/ES/ANALISIS Y CONTROL DE UN DISPOSITIVO FLOTANTE HIBRIDO DE ENERGIA EOLICA Y MARINA/
Agradecimientos:
Ministerio de Ciencia, Innovación y Universidades: Proyecto MCI AEI/FEDER RTI2018- 094902-B-C21
Tipo: Artículo

References

Abouheaf, M., Gueaieb, W., Sharaf, A. 2018. Model-free adaptive learning control scheme for wind turbines with doubly fed induction generators. IET Renewable Power Generation 12(14), 1675-1686. https://doi.org/10.1049/iet-rpg.2018.5353

Alvarez-Ramos, C. M., Santos, M., López, V. 2010. Reinforcement learning vs. A* in a role playing game benchmark scenario. In Computational Intelligence: Foundations and Applications (pp. 644-650). https://doi.org/10.1142/9789814324700_0097

Asghar, A. B., Liu, X. 2018a. Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine. Neurocomputing 272, 495-504. https://doi.org/10.1016/j.neucom.2017.07.022 [+]
Abouheaf, M., Gueaieb, W., Sharaf, A. 2018. Model-free adaptive learning control scheme for wind turbines with doubly fed induction generators. IET Renewable Power Generation 12(14), 1675-1686. https://doi.org/10.1049/iet-rpg.2018.5353

Alvarez-Ramos, C. M., Santos, M., López, V. 2010. Reinforcement learning vs. A* in a role playing game benchmark scenario. In Computational Intelligence: Foundations and Applications (pp. 644-650). https://doi.org/10.1142/9789814324700_0097

Asghar, A. B., Liu, X. 2018a. Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine. Neurocomputing 272, 495-504. https://doi.org/10.1016/j.neucom.2017.07.022

Asghar, A. B., Liu, X. 2018b. Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology. Neurocomputing, 287, 58-67. https://doi.org/10.1016/j.neucom.2018.01.077

Casteleiro-Roca, J.L., Quintián, H., Calvo-Rolle, J.L, Méndez-Pérez, J. A., Perez-Castelo, F.J., Corchado, E. 2020. Lithium iron phosphate power cell fault detection system based on hybrid intelligent system. Logic Journal of the IGPL, 28(1), 71-82. https://doi.org/10.1093/jigpal/jzz072

Chavero-Navarrete, E., Trejo-Perea, M., Jáuregui-Correa, J. C., Carrillo- Serrano, R. V., Ronquillo-Lomeli, G., Ríos-Moreno, J. G. 2020. Hierarchical pitch control for small wind turbines based on fuzzy logic and anticipated wind speed measurement. Applied Sciences, 10(13), 4592. https://doi.org/10.3390/app10134592

Chen, P., Han, D., Tan, F., Wang, J. 2020. Reinforcement-based robust variable pitch control of wind turbines. IEEE Access 8, 20493-20502. https://doi.org/10.1109/ACCESS.2020.2968853

Demirdelen, T., Tekin, P., Aksu, I. O., Ekinci, F. 2019. The prediction model of characteristics for wind turbines based on meteorological properties using neural network swarm intelligence. Sustainability, 11(17), 4803. https://doi.org/10.3390/su11174803

Deng, X., Yang, J., Sun, Y., Song, D., Xiang, X., Ge, X., Joo, Y. H. 2019. Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine. Energy, 186, 115790. https://doi.org/10.1016/j.energy.2019.07.120

Deng, X., Yang, J., Sun, Y., Song, D., Yang, Y., Joo, Y. H. 2020. An effective wind speed estimation based extended optimal torque control for maximum wind energy capture. IEEE Access, 8, 65959-65969. https://doi.org/10.1109/ACCESS.2020.2984654

Du, J., Wang, B. 2020. Pitch Control of wind turbines based on BP neural network PI. In Journal of Physics: Conference Series (Vol. 1678, No. 1, p. 012060). IOP Publishing. https://doi.org/10.1088/1742-6596/1678/1/012060

El Maati, Y. A., El Bahir, L. 2020. Optimal fault tolerant control of large-scale wind turbines in the case of the pitch actuator partial faults. Complexity. https://doi.org/10.1155/2020/6210407

Fernandez-Gauna, B., Fernandez-Gamiz, U., Grana, M. 2017. Variable speed wind turbine controller adaptation by reinforcement learning. Integrated Computer-Aided Engineering 24(1), 27-39. https://doi.org/10.3233/ICA-160531

Fernandez-Gauna, B., Osa, J. L., Graña, M. 2018. Experiments of conditioned reinforcement learning in continuous space control tasks. Neurocomputing 271, 38-47. https://doi.org/10.1016/j.neucom.2016.08.155

Guo, C., Wang, D. 2019. Frequency regulation and coordinated control for complex wind power systems. Complexity, 2019. https://doi.org/10.1155/2019/8525397

Hosseini, E., Aghadavoodi, E., Ramírez, L. M. F. 2020. Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning. Renewable Energy, 157, 897-910. https://doi.org/10.1016/j.renene.2020.05.060

IRENA. 2019. Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf

Jeon, T., Paek, I. 2021. Design and verification of the LQR controller based on fuzzy logic for large wind turbine. Energies, 14(1), 230. https://doi.org/10.3390/en14010230

Jie, W., Jingchun, C., Lin, Y., Wenliang, W., Jian, D. 2020. Pitch control of wind turbine based on deep neural network. In IOP Conference Series: Earth and Environmental Science (Vol. 619, No. 1, p. 012034). IOP Publishing https://doi.org/10.1088/1755-1315/619/1/012034

Jove, E., Casteleiro‐Roca, J. L., Quintián, H., Méndez‐Pérez, J. A., Calvo‐Rolle, J. L. 2019. A fault detection system based on unsupervised techniques for industrial control loops. Expert Systems, 36(4), e12395. https://doi.org/10.1111/exsy.12395

Jove, E., Casteleiro-Roca, J., Quintián, H., Méndez-Pérez, J. A., Calvo-Rolle, J. L. 2020. Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador. Revista Iberoamericana de Automática e Informática industrial, 17(1), 84-93. https://doi.org/10.4995/riai.2019.11055

Li, M., Wang, S. 2019. Dynamic fault monitoring of pitch system in wind turbines using selective ensemble small-world neural networks. Energies, 12(17), 3256. https://doi.org/10.3390/en12173256

Mikati, M., Santos, M., Armenta, C. 2013. Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. Renewable energy, 57, 587-593. https://doi.org/10.1016/j.renene.2013.02.018

Moodi, H., Bustan, D. 2019. Wind turbine control using TS systems with nonlinear consequent parts. Energy, 172, 922-931. https://doi.org/10.1016/j.energy.2019.01.133

Naciones Unidas. 2021. https://sdgs.un.org/2030agenda. Accedido por última vez en 15/08/2021

Ngo, Q. V., Chai, Y., Nguyen, T. T. 2020. The fuzzy-PID based-pitch angle controller for small-scale wind turbine. International Journal of Power Electronics and Drive Systems, 11(1), 135. https://doi.org/10.11591/ijpeds.v11.i1.pp135-142

Our World in Data. 2021. https://ourworldindata.org/renewable-energy. Accedido por última vez en 15/08/2021.

Phan, B. C., Lai, Y. C. 2019. Control strategy of a hybrid renewable energy system based on reinforcement learning approach for an isolated microgrid. Applied Sciences, 9(19), 4001. https://doi.org/10.3390/app9194001

Ren, H., Hou, B., Zhou, G., Shen, L., Wei, C., Li, Q. 2020. Variable pitch active disturbance rejection control of wind turbines based on BP neural network PID. IEEE Access, 8, 71782-71797. https://doi.org/10.1109/ACCESS.2020.2987912

Rubio, P. M., Quijano, J. F., López, P. Z., Lozano, J. J. F., Cerezo, A. G., Casanova, J. O. 2019. Control inteligente para mejorar el rendimiento de una plataforma semisumergible híbrida con aerogeneradores y convertidores de oleaje: sistema de control borroso para la turbina. Revista Iberoamericana de Automática e Informática industrial, 16(4), 480-491. https://doi.org/10.4995/riai.2019.10972

Saénz-Aguirre, A., Zulueta, E., Fernández-Gamiz, U., Lozano, J., Lopez-Guede, J. M. 2019. Artificial neural network based reinforcement learning for wind turbine yaw control. Energies 12(3), 436. https://doi.org/10.3390/en12030436

Saénz‐Aguirre, A., Zulueta, E., Fernandez‐Gamiz, U., Ulazia, A., Teso‐Fz‐Betono, D. 2020. Performance enhancement of the artificial neural network-based reinforcement learning for wind turbine yaw control. Wind Energy 23(3), 676-690. https://doi.org/10.1002/we.2451

Santos, M. 2011. Un enfoque aplicado del control inteligente. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(4), 283-296. https://doi.org/10.1016/j.riai.2011.09.016

Sedighizadeh, M., Rezazadeh, A. 2008. Adaptive PID controller based on reinforcement learning for wind turbine control. In: Proc. World Academy of Science, Engineering and Technology 27, 257-262.

Sierra, J. E., Santos, M. 2018. Modelling engineering systems using analytical and neural techniques: Hybridization. Neurocomputing, 271, 70-83. https://doi.org/10.1016/j.neucom.2016.11.099

Sierra-García, J. E., Santos, M. 2020a. Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning. Complexity, 2020. https://doi.org/10.1155/2020/4681767

Sierra-García, J. E., Santos, M. 2020b. Exploring reward strategies for wind turbine pitch control by reinforcement learning. Applied Sciences, 10(21), 7462. https://doi.org/10.3390/app10217462

Sierra-García, J. E., Santos, M. 2021a. Improving wind turbine pitch control by effective wind neuro-estimators. IEEE Access, 9, 10413-10425. https://doi.org/10.1109/ACCESS.2021.3051063

Sierra-García, J. E., Santos, M. 2021b. Lookup table and neural network hybrid strategy for wind turbine pitch control. Sustainability, 13(6), 3235. https://doi.org/10.3390/su13063235

Sierra-Garcia, J. E., Santos, M. 2021c. Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control. Neural Computing and Applications, 1-15. https://doi.org/10.1007/s00521-021-06323-w

Sutton, R. S., Barto, A. G. 2015. Reinforcement learning an introduction-Second edition, in progress.

Tomás-Rodríguez, M., Santos, M. 2019. Modelado y control de turbinas eólicas marinas flotantes. Revista Iberoamericana de Automática e Informática Industrial, 16(4), 381-390. https://doi.org/10.4995/riai.2019.11648

Tomin, N., Kurbatsky, V., Guliyev, H. 2019. Intelligent control of a wind turbine based on reinforcement learning. In 16th Conf. on Electrical Machines, Drives and Power Systems ELMA, 1-6. IEEE. https://doi.org/10.1109/ELMA.2019.8771645

Vives, J., Quiles, E., García, E. 2020. AI techniques applied to diagnosis of vibrations failures in wind turbines. IEEE Latin America Transactions, 18(08), 1478-1486. https://doi.org/10.1109/TLA.2020.9111685

Zhao, H., Zhao, J., Qiu, J., Liang, G., Dong, Z. Y. 2020. Cooperative wind farm control with deep reinforcement learning and knowledge assisted learning. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2020.2974037

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem