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

Autores/as

DOI:

https://doi.org/10.4995/riai.2021.16111

Palabras clave:

Turbinas eólicas, aerogeneradores, control del ángulo de las palas, control inteligente, redes neuronales aprendizaje por refuerzo

Resumen

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 cambiantes del viento y otros fenómenos meteorológicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde también les afectan las corrientes marinas y las olas. Las redes neuronales, y otras técnicas del control inteligente, han demostrado ser muy útiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eólicas. Se describe el control directo del ángulo de las palas del aerogenerador y algunas configuraciones híbridas de control. Se expone la utilidad de los neuro-estimadores para la mejora de los controladores. Finalmente, se muestra un ejemplo de aplicación de algunas de estas técnicas en un modelo de turbina terrestre.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

J. E. Sierra-García, Universidad de Burgos

Departamento de Ingeniería Electromecánica

M. Santos, Universidad Complutense de Madrid

Instituto de Tecnología del Conocimiento

Citas

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., Casteleiroâ€Roca, J. L., Quintián, H., Méndezâ€Pérez, J. A., Calvoâ€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

Saénzâ€Aguirre, A., Zulueta, E., Fernandezâ€Gamiz, U., Ulazia, A., Tesoâ€Fzâ€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

Descargas

Publicado

30-09-2021

Cómo citar

Sierra-García, J. E. y Santos, M. (2021) «Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas», Revista Iberoamericana de Automática e Informática industrial, 18(4), pp. 327–335. doi: 10.4995/riai.2021.16111.

Número

Sección

Tutoriales