- -

Sistema híbrido inteligente para la predicción de la tensión de una pila de combustible basada en hidrógeno

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Sistema híbrido inteligente para la predicción de la tensión de una pila de combustible basada en hidrógeno

Mostrar el registro completo del ítem

Casteleiro-Roca, J.; Barragán, AJ.; Segura, F.; Calvo-Rolle, JL.; Andújar, JM. (2019). Sistema híbrido inteligente para la predicción de la tensión de una pila de combustible basada en hidrógeno. Revista Iberoamericana de Automática e Informática. 16(4):492-501. https://doi.org/10.4995/riai.2019.10986

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/126301

Ficheros en el ítem

Thumbnail
Nombre: 10986-48840-2-PB.pdf
Tamaño: 1.417Mb
Formato: PDF
Abrir/Preview

Metadatos del ítem

Título: Sistema híbrido inteligente para la predicción de la tensión de una pila de combustible basada en hidrógeno
Otro titulo: Intelligent hybrid system for the prediction of the voltage-current characteristic curve of a hydrogen-based fuel cell
Autor: Casteleiro-Roca, José-Luis Barragán, Antonio Javier Segura, Francisca Calvo-Rolle, José Luis Andújar, José Manuel
Fecha difusión:
Resumen:
[EN] Due to some reasons like sustainability and energy strategy, there is a clear trend using new ways to obtain energy, more efficient and, usually, renewables. In addition, with other dierent objectives, many researchs ...[+]


[ES] Por razones de sostenibilidad y estrategia energética, entre otras, existe en la actualidad una tendencia clara hacia el uso de nuevas formas de obtención, almacenamiento y gestión de energía, más eficientes y con un ...[+]
Palabras clave: Almacenamiento de energía , Pila de combusible , Hidrógeno , K-Means , ANN , Energy storage , Fuel Cell , Hydrogen
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Revista Iberoamericana de Automática e Informática.. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2019.10986
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2019.10986
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-85540-R/ES/CONFIGURACION Y GESTION DE UNA MICRO-RED RENOVABLE INTELIGENTE HIBRIDADA CON TECNOLOGIAS DE HIDROGENO/
Agradecimientos:
Los autores de este trabajo quieren agradecer el soporte en materia de financiación del Ministerio de Economía, Industria y Competitividad del Gobierno de España a través del proyecto H2SMART-μGRID (DPI2017-85540-R).
Tipo: Artículo

References

Alaiz Moretón, H., Calvo Rolle, J., García, I., Alonso Alvarez, A., 2011. Formalization and practical implementation of a conceptual model for pid controller tuning. Asian Journal of Control 13 (6), 773-784. https://doi.org/10.1002/asjc.264

Alique, A., Haber, R. E., Haber, R. H., Ros, S., Gonzalez, C., 2000. A neural network-based model for the prediction of cutting force in milling process. A progress study on a real case. In: Intelligent Control, 2000. Proceedings of the 2000 IEEE International Symposium on. IEEE, pp. 121-125. https://doi.org/10.1109/ISIC.2000.882910

Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., Harris, T. J., Jan. 1995. Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell i. Mechanistic model development. Journal of the Electrochemical Society 142 (1), 1-8. https://doi.org/10.1149/1.2043866 [+]
Alaiz Moretón, H., Calvo Rolle, J., García, I., Alonso Alvarez, A., 2011. Formalization and practical implementation of a conceptual model for pid controller tuning. Asian Journal of Control 13 (6), 773-784. https://doi.org/10.1002/asjc.264

Alique, A., Haber, R. E., Haber, R. H., Ros, S., Gonzalez, C., 2000. A neural network-based model for the prediction of cutting force in milling process. A progress study on a real case. In: Intelligent Control, 2000. Proceedings of the 2000 IEEE International Symposium on. IEEE, pp. 121-125. https://doi.org/10.1109/ISIC.2000.882910

Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., Harris, T. J., Jan. 1995. Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell i. Mechanistic model development. Journal of the Electrochemical Society 142 (1), 1-8. https://doi.org/10.1149/1.2043866

Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., Rodrigues, A., Feb. 1996. A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources 61 (1-2), 183-188, cited By (since 1996) 216. https://doi.org/10.1016/S0378-7753(96)02360-9

Andújar, J. M., Segura, F., Dec. 2009. Fuel cells: History and updating. A walk along two centuries. Renewable and Sustainable Energy Reviews 13 (9), 2309-2322. https://doi.org/10.1016/j.rser.2009.03.015

Andújar, J. M., Segura, F., Durán, E., Rentería, L. A., Nov. 2011. Optimal interface based on power electronics in distributed generation systems for fuel cells. Renewable Energy 36 (11), 2759-2770. https://doi.org/10.1016/j.renene.2011.04.005

Andújar, J. M., Segura, F., Vasallo, M. J., 2008. A suitable model plant for control of the set fuel cell-DC/DC converter. Renewable Energy 33 (4), 813-826. https://doi.org/10.1016/j.renene.2007.04.013

Ballard, 2009. FCgenTM-1020ACS/FCvelocityTM-1020ACS Fuel Cell Stack. Ballard Product Manual and Integration Guide. Document Number MAN5100192-0GS.

Ballard, 2018. FCgen1020-ACS fuel cell from Ballard Power Systems. URL: http://www.ballard.com/docs/default-source/backup-power-documents/fcgen-1020acs.pdf

Barragán, A. J., Al-Hadithi, B. M., Andújar, J. M., Jiménez, A., 2015. Formal methodology for analyzing the dynamic behavior of nonlinear systems using fuzzy logic. Revista Iberoamericana de Automática e Informática Industrial (RIAI) 12 (4), 434-445. https://doi.org/10.1016/j.riai.2015.09.005

Barragán, A. J., Al-Hadithi, B. M., Jiménez, A., Andújar, J. M., 2014. A general methodology for online TS fuzzy modeling by the extended kalman filter. Applied Soft Computing 18 (0), 277-289. https://doi.org/10.1016/j.asoc.2013.09.005

Baruque, B., Porras, S., Jove, E., Calvo-Rolle, J. L., 2019. Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy 171, 49-60. https://doi.org/10.1016/j.energy.2018.12.207

Bertoluzzo, M., Buja, G., Aug. 2011. Development of electric propulsion systems for light electric vehicles. Industrial Informatics, IEEE Transactions on 7 (3), 428-435. https://doi.org/10.1109/TII.2011.2158840

Calvo-Rolle, J. L., Casteleiro-Roca, J. L., Quintián, H., del Carmen Meizoso-Lopez, M., 2013. A hybrid intelligent system for PID controller using in a steel rolling process. Expert Systems with Applications 40 (13), 5188-5196. https://doi.org/10.1016/j.eswa.2013.03.013

Calvo-Rolle, J. L., Fontenla-Romero, O., Pérez-Sánchez, B., Guijarro-Berdinas, B., 2014. Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25 (3), 401-414. https://doi.org/10.15388/Informatica.2014.20

Calvo-Rolle, J. L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R. F., 2015. Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. Journal of Applied Logic 13 (1), 37-47. https://doi.org/10.1016/j.jal.2014.11.010

Casteleiro-Roca, J.-L., Barragan, A. J., Segura, F., Calvo-Rolle, J. L., Andújar, J. M., 2019. Fuel cell output current prediction with a hybrid intelligent system. Complexity 2019.

Casteleiro-Roca, J. L., Calvo-Rolle, J. L., Meizoso-López, M.-C., Piñón-Pazos, A., Rodríguez-Gómez, B. A., 2015. Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150, 90-98. https://doi.org/10.1016/j.neucom.2014.02.075

Casteleiro-Roca, J.-L., Jove, E., Gonzalez-Cava, J. M., Pérez, J. A. M., Calvo- Rolle, J. L., Alvarez, F. B., 2018. Hybrid model for the ANI index prediction using remifentanil drug and EMG signal. Neural Computing and Applications, 1-10. https://doi.org/10.1007/s00521-018-3605-z

Casteleiro-Roca, J.-L., Jove, E., Sánchez-Lasheras, F., Méndez-Pérez, J.-A., Calvo-Rolle, J.-L., de Cos Juez, F. J., 2017. Power cell SOC modelling for intelligent virtual sensor implementation. Journal of Sensors 2017. https://doi.org/10.1155/2017/9640546

De las Heras, A., Vivas, F., Segura, F., Andújar, J., 2018a. From the cell to the stack. a chronological walk through the techniques to manufacture the pefcs core. Renewable and Sustainable Energy Reviews 96, 29-45. https://doi.org/10.1016/j.rser.2018.07.036

De las Heras, A., Vivas, F., Segura, F., Redondo, M., Andújar, J., 2018b. Aircooled fuel cells: Keys to design and build the oxidant/cooling system. Renewable Energy 125, 1-20. https://doi.org/10.1016/j.renene.2018.02.077

del Brío, B., Molina, A., 2006. Redes neuronales y sistemas borrosos. Ra-Ma.

Famouri, P., Gemmen, R., Jul. 2003. Electrochemical circuit model of a PEM fuel cell. In: Power Engineering Society General Meeting, 2003, IEEE. Vol. 3. pp. 1436-1440. https://doi.org/10.1109/PES.2003.1267364

Fontanet, J. G. G., Cervantes, A. L., Ortiz, I. B., 2016. Alternatives of control for a furuta's pendulum. Revista Iberoamericana de Autom'atica e Informática Industrial RIAI 13 (4), 410 - 420, alternativas de control para un Péndulo de Furuta. https://doi.org/10.1016/j.riai.2016.05.008

Galipienso, M., Quevedo, M., Pardo, O., Ruiz, F., Ortega, M., 2003. Inteligencia artificial. Modelos, técnicas y áreas de aplicación. Editorial Paraninfo.

García, R. F., Rolle, J. L. C., Castelo, J. P., Gomez, M. R., 2014. On the monitoring task of solar thermal fluid transfer systems using NN based models and rule based techniques. Engineering Applications of Artificial Intelligence 27 (0), 129-136. https://doi.org/10.1016/j.engappai.2013.06.011

García, R. F., Rolle, J. L. C., Gomez, M. R., Catoira, A. D., 2013. Expert condition monitoring on hydrostatic self-levitating bearings. Expert Systems with Applications 40 (8), 2975-2984. https://doi.org/10.1016/j.eswa.2012.12.013

Ghanghermeh, A., Roshan, G., Orosa, J. A., Calvo-Rolle, J. L., Costa, A. M., 2013. New climatic indicators for improving urban sprawl: A case study of tehran city. Entropy 15 (3), 999-1013. https://doi.org/10.3390/e15030999

Gordillo, F., Aracil, J., Alamo, T., Jul. 1997. Determining limit cycles in fuzzy control systems. In: IEEE International Conference on Fuzzy Systems. Vol. 1. pp. 193-198. https://doi.org/10.1109/FUZZY.1997.616367

Harston, A. M. C., Pap, R., 2014. Handbook of Neural Computing Applications. Elsevier Science.

Hilera Gonzalez, J. R., Martínez Hernando, V. J., 2000. Redes neuronales artificiales: fundamentos, modelos y aplicaciones. Ra-Ma.

Hou, Y., Yang, Z., Fang, X., 2011. An experimental study on the dynamic process of PEM fuel cell stack voltage. Renewable Energy 36 (1), 325-329. https://doi.org/10.1016/j.renene.2010.06.046

Irigoyen, E., Miñano, G., 2013. A narx neural network model for enhancing cardiovascular rehabilitation therapies. Neurocomputing 109, 9 - 15, new trends on Soft Computing Models in Industrial and Environmental Applications. https://doi.org/10.1016/j.neucom.2012.07.031

Jove, E., Antonio Lopez-Vazquez, J., Isabel Fernandez-Ibanez, M., Casteleiro-Roca, J.-L., Luis Calvo-Rolle, J., 2018a. Hybrid intelligent system to predict the individual academic performance of engineering students. INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION 34 (3), 895-904.

Jove, E., Blanco-Rodríguez, P., Casteleiro-Roca, J. L., Moreno-Arboleda, J., Lopez-V ázquez, J. A., de Cos Juez, F. J., Calvo-Rolle, J. L., 2018b. Attempts prediction by missing data imputation in engineering degree. In: International Joint Conference SOCO'17-CISIS'17-ICEUTE'17 Leon, Spain, September 6-8, 2017, Proceeding. Springer International Publishing, Cham, pp. 167-176.

Jove, E., Gonzalez-Cava, J. M., Casteleiro-Roca, J.-L., Méndez-Pérez, J.-A., Antonio Reboso-Morales, J., Javier Pérez-Castelo, F., Javier de Cos Juez, F., Luis Calvo-Rolle, J., 2018b. Modelling the hypnotic patient response in general anaesthesia using intelligent models. Logic Journal of the IGPL 00(0). https://doi.org/10.1093/jigpal/jzy032

Kim, J., Lee, S.-M., Srinivasan, S., Chamberlin, C. E., Aug. 1995. Modeling of proton exchange membrane fuel cell performance with an empirical equation. Journal of the Electrochemical Society 142 (8), 2670-2674. https://doi.org/10.1149/1.2050072

Kirubakaran, A., Jain, S., Nema, R., Dec. 2009. A review on fuel cell technologies and power electronic interface. Renewable and Sustainable Energy Reviews 13 (9), 2430-2440. https://doi.org/10.1016/j.rser.2009.04.004

Li, X., Deng, Z.-H., Wei, D., Xu, C.-S., Cao, G.-Y., 2011. Parameter optimization of thermal-model-oriented control law for pem fuel cell stack via novel genetic algorithm. Energy Conversion and Management 52 (11), 3290-3300. https://doi.org/10.1016/j.enconman.2011.05.012

López, R., Fernández, J., 2008. Las Redes Neuronales Artificiales. Netbiblo.

López-Baldán, M. J., García-Cerezo, A., Cejudo, J. M., Romero, A., Apr. 2002. Fuzzy modeling of a thermal solar plant. International Journal of Intelligent Systems 17 (4), 369-379. https://doi.org/10.1002/int.10026

Machón-González, I., López-García, H., Calvo-Rolle, J. L., 2010. A hybrid batch som-ng algorithm. In: Neural Networks (IJCNN), The 2010 International Joint Conference on. pp. 1-5. https://doi.org/10.1109/IJCNN.2010.5596812

MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. pp. 281-297.

Márquez, J. M. A., Piña, A. J. B., Arias, M. E. G., 2009. A general and formal methodology for designing stable nonlinear fuzzy control systems. IEEE Transactions on Fuzzy Systems 17 (5), 1081-1091. https://doi.org/10.1109/TFUZZ.2009.2021984

Mehta, V., Cooper, J., 2003. Review and analysis of pem fuel cell design and manufacturing. Journal of Power Sources 114 (1), 32-53. https://doi.org/10.1016/S0378-7753(02)00542-6

Moody, J., Darken, C., 6 1989. Fast learning in networks of locally-tuned processing units. Neural Computation 1 (2), 281-294. https://doi.org/10.1162/neco.1989.1.2.281

Moreira, M. V., da Silva, G. E., Jul. 2009. A practical model for evaluating the performance of proton exchange membrane fuel cells. Renewable Energy 34 (7), 1734-1741. https://doi.org/10.1016/j.renene.2009.01.002

Orallo, J., Quintana, M., Ramírez, C., 2004. Introducción a la miner'ıa de datos. Editorial Alhambra S.A.

Paska, J., Biczel, P., Kłos, M., Nov. 2009. Hybrid power systems - an efective way of utilising primary energy sources. Renewable Energy 34 (11), 2414- 2421. https://doi.org/10.1016/j.renene.2009.02.018

Quintián, H., Calvo-Rolle, J. L., Corchado, E., 2014. A hybrid regression system based on local models for solar energy prediction. Informatica 25 (2), 265-282. https://doi.org/10.15388/Informatica.2014.14

Quintian Pardo, H., Calvo Rolle, J. L., Fontenla Romero, O., 2012. Application of a low cost commercial robot in tasks of tracking of objects. Dyna 79 (175), 24-33.

Ralph, T., Hards, G., Keating, J., Campbell, S., Wilkinson, D., Davis, M., St-Pierre, J., Johnson, M., 1997. Low cost electrodes for proton exchange membrane fuel cells: Performance in single cells and ballard stacks. Journal of the Electrochemical Society 144 (11), 3845-3857. https://doi.org/10.1149/1.1838101

Rolle, J., Gonzalez, I., Garcia, H., 2011. Neuro-robust controller for non-linear systems. Dyna 86 (3), 308-317. https://doi.org/10.6036/3949

Ross, D., Jul. 2003. Power struggle [power supplies for portable equipment]. IEE Review 49 (7), 34-38. https://doi.org/10.1049/ir:20030705

Segura, F., Andújar, J. M., Durán, E., april 2011. Analog current control techniques for power control in PEM fuel-cell hybrid systems: A critical review and a practical application. IEEE Transactions on Industrial Electronics 58 (4), 1171-1184. https://doi.org/10.1109/TIE.2010.2049710

Segura, F., Andújar, J., 2015a. Modular pem fuel cell scada & simulator system. Resources 4 (3), 692-712. https://doi.org/10.3390/resources4030692

Segura, F., Andújar, J., 2015b. Step by step development of a real fuel cell system. Design, implementation, control and monitoring. International Journal of Hydrogen Energy 40 (15), 5496-5508. https://doi.org/10.1016/j.ijhydene.2015.01.178

Segura, F., Bartolucci, V., Andújar, J., 2017. Hardware/software data acquisition system for real time cell temperature monitoring in air-cooled polymer electrolyte fuel cells. Sensors (Switzerland) 17 (7). https://doi.org/10.3390/s17071600

Van Bussel, H., Koene, F., Mallant, R. K., Mar. 1998. Dynamic model of solid polymer fuel cell water management. Journal of Power Sources 71 (1-2), 218-222. https://doi.org/10.1016/S0378-7753(97)02744-4

Viñuela, P., León, I., 2004. Redes de neuronas artificiales: un enfoque práctico. Pearson Educaci'on - Prentice Hall.

Vivas, F., De las Heras, A., Segura, F., And'ujar, J., 2018. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renewable and Sustainable Energy Reviews 82, 126-155. https://doi.org/10.1016/j.rser.2017.09.014

Ziogou, C., Voutetakis, S., Papadopoulou, S., Georgiadis, M., 2011. Modeling, simulation and experimental validation of a pem fuel cell system. Computers and Chemical Engineering 35 (9), 1886-1900. https://doi.org/10.1016/j.compchemeng.2011.03.013

[-]

recommendations

 

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

Mostrar el registro completo del ítem