Contribuciones al modelado y diagnóstico de fallos en PEMFC para mejorar la fiabilidad en sistemas híbridos renovables

[ES] Las pilas de combustibles son dispositivos de un coste elevado y frágiles ante ambientes contaminados o condiciones inadecuadas de operación como: temperaturas extremas o mala gestión del agua producida como residuo de la pila. Para mejorar la fiabilidad de una pila de combustible es necesario diagnosticar de una manera oportuna los fallos y así evitar daños que reduzcan el desempeño del módulo o que lo inhabiliten. Este trabajo busca contribuir al mejoramiento de la fiabilidad de las pilas de combustible de baja temperatura y de esta forma favorecer el uso de hidrógeno en la transición a una energía descarbonizada. Para lograrlo, se realizaron tres actividades principales: modelado de una pila de hidrógeno, ajuste paramétrico del modelo desarrollado y, por último, aplicación de técnicas de diagnóstico de fallos basados en modelos. En el laboratorio de Recursos Energéticos Renovables Distribuidos LabDER de la Universitat Politècnica de València, se estudia el desempeño de sistemas híbridos renovables, incluyendo una línea de hidrógeno, desde la producción, almacenamiento y reconversión en electricidad en una pila de combustible, por tanto, se ha podido validar el modelo. En un primer momento se identificó la necesidad de un modelo que emplee la temperatura como señal de salida y que retroalimente el sistema, y que tuviese en cuenta señales propias del módulo comercial; sin embargo, el uso de la temperatura como señal y la no linealidad de las ecuaciones físicas, químicas, eléctricas y empleadas, generan un modelo altamente complejo. El ajuste paramétrico del modelo se realizó empleando algoritmos de optimización. Tomando como base al algoritmo de Enjambre de Partículas, se desarrolló una nueva propuesta llamada Scout GA, este algoritmo fue utilizado en otras aplicaciones y pruebas de convergencia para verificar su desempeño frente al fenómeno de estancamiento prematuro y logrando mejorar la precisión y velocidad de convergencia de otras propuestas. Como resultado de la validación de este modelo, en una primera simulación usando datos reales de funcionamiento correspondientes a 1500 segundos, el error de simulación fue del 2,21% en la señal de tensión y del 1,97% en la señal de temperatura, obteniendo un error medio del 2,09%. En un segundo conjunto de datos de algo más de 2.500 segundos de funcionamiento, el error de simulación fue del 2,40% y del 1,96% para las señales de tensión y temperatura, respectivamente. Se estima que el error medio de simulación para ambas señales y condiciones de funcionamiento similares es inferior al 2,5%. Buscando mejorar la fiabilidad de la pila, se realizó el trabajo de diagnóstico de fallos, este partió de la simulación de fallos, mediante la modificación de algunas señales de entrada del modelo, los fallos se caracterizaron mediante el tratamiento estadístico de 12 residuos, obteniendo firmas de fallos, que, en su conjunto, formaron una matriz de fallos. Luego, un algoritmo de diagnóstico propuesto permitió identificar y aislar 14 fallos. permitiendo concluir que, el modelo predice eficazmente los fallos de las pilas PEMFC y podría extrapolarse a otras pilas de combustible.[CA] Les piles de combustibles són dispositius d'un cost elevat i fràgils davant ambients contaminats o condicions inadequades d'operació com: temperatures extremes o dolenta gestió de l'aigua produïda com a residu de la pila. Per a millorar la fiabilitat d'una pila de combustible és necessari diagnosticar d'una manera oportuna les fallades i així evitar danys que reduïsquen l'acompliment del mòdul o que l'inhabiliten. Este treball busca contribuir al millorament de la fiabilitat de les piles de combustible de baixa temperatura i d'esta manera afavorir l'ús d'hidrogen en la transició a una energia *descarbonizada. Per a aconseguir-ho, es van realitzar tres activitats principals: modelatge d'una pila d'hidrogen, ajust paramètric del model desenvolupat i, finalment, aplicació de tècniques de diagnòstic de fallades basades en models. En el laboratori de Recursos Energètics Renovables Distribuïts *LabDER de la Universitat Politècnica de València, s'estudia l'acompliment de sistemes híbrids renovables, incloent-hi una línia d'hidrogen, des de la producció, emmagatzematge i reconversió en electricitat en una pila de combustible, per tant, s'ha pogut validar el model. En un primer moment es va identificar la necessitat d'un model que empre la temperatura com a senyal d'eixida i que retroalimente el sistema, i que tinguera en compte senyals propis del mòdul comercial, no obstant això, l'ús de la temperatura i la no linealitat de les equacions físiques, químiques, elèctriques i tèrmiques empleades, deriven en un model altament complex. L'ajust paramètric del model de pila de combustible es va realitzar emprant algorismes d'optimització. Prenent com a base a l'algorisme d'Eixam de Partícules, es va desenvolupar una nova proposta anomenada Scout GA, aquest algorisme va ser utilitzat en altres aplicacions i proves de convergència per a verificar el seu acompliment enfront del fenomen d'estancament prematur i aconseguint millorar la precisió i velocitat de convergència d'altres propostes. La simulació i identificació del model té un cost computacional entre 7 i 20 ms per iteració, on es van aconseguir errors de simulació menors al 2.5% Com a resultat de la validació d'aquest model, en una primera simulació usant dades reals de funcionament corresponents a 1500 segons, l'error de simulació va ser del 2,21% en el senyal de tensió, del 1,97% en el senyal de temperatura i un error mitjà del 2,09%. En un segon conjunt de dades d'una mica més de 2.500 segons de funcionament, l'error de simulació va ser del 2,40% i del 1,96% per als senyals de tensió i temperatura, respectivament. S'estima que l'error mitjà de simulació per a tots dos senyals i condicions de funcionament similars és inferior al 2,5%. Buscant millorar la fiabilitat de la pila, es va fer el treball de diagnòstic de fallades, aquest va partir de la simulació de fallades, mitjançant la modificació d'alguns senyals d'entrada del model, les fallades es van caracteritzar mitjançant el tractament estadístic de 12 residus, obtenint signatures de fallades, que en el seu conjunt, van formar una matriu de fallades. després un algorisme de diagnòstic proposat, va permetre identificar i aïllar 14 fallades. Permetent concloure que, el model prediu eficaçment les fallades de les piles PEMFC i podria extrapolar-se a altres piles de combustible.[EN] Fuel cells are high-cost devices that are fragile in contaminated environments or in inadequate operating conditions, such as extreme temperatures or poor water management, produced as battery waste. To improve the reliability of a fuel cell, it is necessary to diagnose failures promptly and thus avoid damage that reduces the module's performance or disables it. This work seeks to contribute to improving the reliability of low-temperature fuel cells and thus promote the use of hydrogen in the transition to decarbonized energy. To achieve this, three main activities were carried out: modeling a hydrogen fuel cell, parametric adjustment of the developed model, and application of model-based fault diagnosis techniques. In the LabDER Distributed Renewable Energy Resources laboratory of the Polytechnic University of Valencia, the performance of renewable hybrid systems is studied, including a hydrogen line, from production, storage, and reconversion into electricity in a fuel cell, therefore, has been able to validate the model. Initially, a fuel cell model that uses temperature as an in/output signal is required. Also, the model must be able to use the reals signals supplied for the commercial module. However, using temperature and an equation set that includes the non-linearity of the physical, chemical, electrical, and thermal equations resulted in a highly complex model. The parametric adjustment of the fuel cell model was performed using optimization algorithms. Based on the Particle Swarm algorithm, a new proposal called Scout GA was developed. This algorithm was used in other applications and convergence tests to verify its performance against the premature stagnation phenomenon and improved the accuracy and speed of convergence of other proposals. The simulation and identification of the model have a computational cost between 7 and 20 ms per iteration, where simulation errors of less than 2.5% were achieved. As a result of the validation of this model, in a first simulation using real operating data corresponding to 1,500 seconds, the simulation error was 2.21% for the voltage signal, 1.97% for the temperature signal, and an average error of 2.09%. In a second data set for slightly more than 2500 seconds of operation, the simulation error was 2.40% and 1.96% for the voltage and temperature signals, respectively. The average simulation error for both signals and similar operating conditions is estimated to be less than 2.5%. To improve the reliability of the stack, the fault diagnosis work was carried out, starting from the simulation of faults by modifying some input signals of the model; the faults were characterized by the statistical treatment of 12 residuals, obtaining fault signatures, which formed a fault matrix. Then, a proposed diagnostic algorithm allowed to identify and isolate 14 faults. Allowing to conclude that the model effectively predicts the PEMFC stack faults and could be extrapolated to other fuel cells.Ariza Chacón, HE. (2024). Contribuciones al modelado y diagnóstico de fallos en PEMFC para mejorar la fiabilidad en sistemas híbridos renovables [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/20361

Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

[EN] Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.This research was funded by COLCIENCIAS (Administrative department of science, technology and innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-ColombiaAriza-Chacón, HE.; Correcher Salvador, A.; Sánchez-Diaz, C.; Pérez-Navarro, Á.; García Moreno, E. (2018). Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm. Energies. 11(8):1-15. https://doi.org/10.3390/en11082099S115118Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6Wang, Y., Chen, K. S., Mishler, J., Cho, S. C., & Adroher, X. C. (2011). A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research. Applied Energy, 88(4), 981-1007. doi:10.1016/j.apenergy.2010.09.030Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., & Harris, T. J. (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. doi:10.1149/1.2043866Tao, S., Si-jia, Y., Guang-yi, C., & Xin-jian, Z. (2005). Modelling and control PEMFC using fuzzy neural networks. Journal of Zhejiang University-SCIENCE A, 6(10), 1084-1089. doi:10.1631/jzus.2005.a1084Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., & Rodrigues, A. (1996). A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources, 61(1-2), 183-188. doi:10.1016/s0378-7753(96)02360-9Mo, Z.-J., Zhu, X.-J., Wei, L.-Y., & Cao, G.-Y. (2006). Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research, 30(8), 585-597. doi:10.1002/er.1170YE, M., WANG, X., & XU, Y. (2009). Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization. International Journal of Hydrogen Energy, 34(2), 981-989. doi:10.1016/j.ijhydene.2008.11.026Askarzadeh, A., & Rezazadeh, A. (2011). A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. International Journal of Hydrogen Energy, 36(8), 5047-5053. doi:10.1016/j.ijhydene.2011.01.070El-Fergany, A. A. (2018). Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser. IET Renewable Power Generation, 12(1), 9-17. doi:10.1049/iet-rpg.2017.0232Li, Q., Chen, W., Wang, Y., Liu, S., & Jia, J. (2011). Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization. IEEE Transactions on Industrial Electronics, 58(6), 2410-2419. doi:10.1109/tie.2010.2060456Ali, M., El-Hameed, M. A., & Farahat, M. A. (2017). Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer. Renewable Energy, 111, 455-462. doi:10.1016/j.renene.2017.04.036Sun, Z., Wang, N., Bi, Y., & Srinivasan, D. (2015). Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm. Energy, 90, 1334-1341. doi:10.1016/j.energy.2015.06.081Gong, W., Yan, X., Liu, X., & Cai, Z. (2015). Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy, 86, 139-151. doi:10.1016/j.energy.2015.03.117Turgut, O. E., & Coban, M. T. (2016). Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm. Ain Shams Engineering Journal, 7(1), 347-360. doi:10.1016/j.asej.2015.05.003Al-Othman, A. K., Ahmed, N. A., Al-Fares, F. S., & AlSharidah, M. E. (2015). Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method. Arabian Journal for Science and Engineering, 40(9), 2619-2628. doi:10.1007/s13369-015-1711-0Methekar, R. N., Prasad, V., & Gudi, R. D. (2007). Dynamic analysis and linear control strategies for proton exchange membrane fuel cell using a distributed parameter model. Journal of Power Sources, 165(1), 152-170. doi:10.1016/j.jpowsour.2006.11.047KUNUSCH, C., HUSAR, A., PULESTON, P., MAYOSKY, M., & MORE, J. (2008). Linear identification and model adjustment of a PEM fuel cell stack. International Journal of Hydrogen Energy, 33(13), 3581-3587. doi:10.1016/j.ijhydene.2008.04.052Li, C.-H., Zhu, X.-J., Cao, G.-Y., Sui, S., & Hu, M.-R. (2008). Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. Journal of Power Sources, 175(1), 303-316. doi:10.1016/j.jpowsour.2007.09.049Fontes, G., Turpin, C., & Astier, S. (2010). A Large-Signal and Dynamic Circuit Model of a $\hbox{H}_{2}/\hbox{O}_{2}$ PEM Fuel Cell: Description, Parameter Identification, and Exploitation. IEEE Transactions on Industrial Electronics, 57(6), 1874-1881. doi:10.1109/tie.2010.2044731Cheng, S.-J., & Liu, J.-J. (2015). Nonlinear modeling and identification of proton exchange membrane fuel cell (PEMFC). International Journal of Hydrogen Energy, 40(30), 9452-9461. doi:10.1016/j.ijhydene.2015.05.109Buchholz, M., & Krebs, V. (2007). Dynamic Modelling of a Polymer Electrolyte Membrane Fuel Cell Stack by Nonlinear System Identification. Fuel Cells, 7(5), 392-401. doi:10.1002/fuce.200700013Meiler, M., Schmid, O., Schudy, M., & Hofer, E. P. (2008). Dynamic fuel cell stack model for real-time simulation based on system identification. Journal of Power Sources, 176(2), 523-528. doi:10.1016/j.jpowsour.2007.08.051Wang, C., Nehrir, M. H., & Shaw, S. R. (2005). Dynamic Models and Model Validation for PEM Fuel Cells Using Electrical Circuits. IEEE Transactions on Energy Conversion, 20(2), 442-451. doi:10.1109/tec.2004.842357Restrepo, C., Konjedic, T., Garces, A., Calvente, J., & Giral, R. (2015). Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy. IEEE Transactions on Industrial Informatics, 11(2), 548-559. doi:10.1109/tii.2014.2317982Salim, R., Nabag, M., Noura, H., & Fardoun, A. (2015). The parameter identification of the Nexa 1.2 kW PEMFC’s model using particle swarm optimization. Renewable Energy, 82, 26-34. doi:10.1016/j.renene.2014.10.012Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.03

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

[EN] Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at "El Choco", Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems.This research was supported by "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco"-BPIN:20130000100285; COLCIENCIAS (Administrative Department of Science, Technology and Innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-Colombia.Ariza-Chacón, HE.; Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro, Á.; Morant Anglada, FJ. (2018). Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies. 11(9):1-14. https://doi.org/10.3390/en11092361S11411

MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response

[EN] A Model Predictive Control (MPC) strategy based on the Evolutionary Algorithms (EA) is proposed for the optimal dispatch of renewable generation units and demand response in a grid-tied hybrid system. The generating system is based on the experimental setup installed in a Distributed Energy Resources Laboratory (LabDER), which includes an AC micro-grid with small scale PV/Wind/Biomass systems. Energy storage is by lead-acid batteries and an H2 system (electrolyzer, H2 cylinders and Fuel Cell). The energy demand is residential in nature, consisting of a base load plus others that can be disconnected or moved to other times of the day within a demand response program. Based on the experimental data from each of the LabDER renewable generation and storage systems, a micro-grid operating model was developed in MATLAB(C) to simulate energy flows and their interaction with the grid. The proposed optimization algorithm seeks the minimum hourly cost of the energy consumed by the demand and the maximum use of renewable resources, using the minimum computational resources. The simulation results of the experimental micro-grid are given with seasonal data and the benefits of using the algorithm are pointed out.Acevedo-Arenas, CY.; Correcher Salvador, A.; Sánchez-Diaz, C.; Ariza-Chacón, HE.; Alfonso-Solar, D.; Vargas-Salgado, C.; Petit-Suarez, JF. (2019). MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response. Energy Conversion and Management. 186:241-257. https://doi.org/10.1016/j.enconman.2019.02.044S24125718

Experimental verification of hybrid renewable systems as feasible energy sources

[EN] Renewable energies are a central element in the search for energy sustainability, so they are becoming a substantial component of the energy scenario of every country, both as systems connected to the grid or in stand-alone applications. Feasibility of these renewable energy systems could be necessary not only in their application in isolated areas, but also in systems connected to the grid, in this last case when their contribution reaches a substantial fraction of the total electricity demand. To overcome this reliability problem, hybrid renewable systems could become essential and activities to optimize their design should be addressed, both in the simulation and in the experimental areas. In this paper, a laboratory to simulate and verify the reliability of hybrid renewable systems is presented and its application to the feasibility analysis of multicomponent systems including photovoltaic panels, wind generator and biomass gasification plant, plus energy storage in a battery bank, are described.Pérez-Navarro, Á.; Alfonso-Solar, D.; Ariza-Chacón, HE.; Cárcel Carrasco, FJ.; Correcher Salvador, A.; Escrivá-Escrivá, G.; Hurtado, E.... (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy. 86(2):384-391. doi:10.1016/j.renene.2015.08.030S38439186