Identifying the Sweet Spot of Padel Rackets with a Robot

[EN] Although the vibration of rackets and the location of the sweet spot for players when hitting the ball is crucial, manufacturers do not specify this behavior precisely. This article analyses padel rackets, provides a solution to determine the sweet spot position (SSP), quantifies its behavior, and determines the level of vibration transmitted along the racket handle. The proposed methods serve to locate the SSP without quantifying it. This article demonstrates the development of equipment capable of analyzing the vibration behavior of padel rackets. To do so, it employs a robot that moves along the surface of the padel racket, striking it along its central line. Accelerometers are placed on a movable cradle where rackets are positioned and adjusted. A method for analyzing accelerometer signals to quantify vibration severity is proposed. The SSP and vibration behavior along the central line are determined and quantified. As a result of the study, 225 padel rackets are analyzed and compared. SSP is independent of the padel racket shape, balance, weight, moment of inertia, and padel racket shape (tear, diamond, or round) and is not located at the same position as the center of percussion.This research was partially funded by Sport Thinkers SL, Ontinyent, Spain.Blanes Campos, C.; Correcher Salvador, A.; Martínez-Turégano, J.; Ricolfe Viala, C. (2023). Identifying the Sweet Spot of Padel Rackets with a Robot. Sensors. 23(24):1-15. https://doi.org/10.3390/s23249908115232

Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators

[EN] In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults caused by partial shading in isolated PV installations using maximum power point tracking (MPPT) regulators. MPPT regulators are comparatively more appropriate than pulse width modulation (PWM) solar regulators in order to implement fault diagnosis systems. MPPT regulators have a physical separation between the electrical parameters belonging to the part of the solar panel with respect to the batteries part. Therefore, these electrical parameters can be used to obtain early predictive symptoms of the effects of partial shading with a greater level of observation and sensitivity. Additionally, modifications are proposed in the PV system assembly to obtain greater homogeneity of all the panels regarding the solar irradiance reception angle.García Moreno, E.; Quiles Cucarella, E.; Correcher Salvador, A.; Morant Anglada, FJ. (2022). Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators. Sensors. 22(20):1-33. https://doi.org/10.3390/s22207819133222

Identification of Stochastic Timed Discrete Event Systems with st-IPN

[EN] This paper presents amethod for the identification of stochastic timed discrete event systems, based on the analysis of the behavior of the input and output signals, arranged in a timeline. To achieve this goal stochastic timed interpreted Petri nets are defined.These nets link timed discrete event systems modelling with stochastic time modelling. The procedure starts with the observation of the input/output signals; these signals are converted into events, so that the sequence of events is the observed language. This language arrives to an identifier that builds a stochastic timed interpreted Petri net which generates the same language. The identified model is a deterministic generator of the observed language.The identification method also includes an algorithm that determines when the identification process is over.This work was supported by a Grant from the Universidad del Cauca, reference 2.3-31.2/05 2011.Muñoz-Añasco, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2014). Identification of Stochastic Timed Discrete Event Systems with st-IPN. Mathematical Problems in Engineering. 2014:1-21. https://doi.org/10.1155/2014/835312S1212014Cassandras, C. G., & Lafortune, S. (Eds.). (2008). Introduction to Discrete Event Systems. doi:10.1007/978-0-387-68612-7Yingwei Zhang, Jiayu An, & Chi Ma. (2013). Fault Detection of Non-Gaussian Processes Based on Model Migration. IEEE Transactions on Control Systems Technology, 21(5), 1517-1526. doi:10.1109/tcst.2012.2217966Ichikawa, A., & Hiraishi, K. (s. f.). Analysis and control of discrete event systems represented by petri nets. Lecture Notes in Control and Information Sciences, 115-134. doi:10.1007/bfb0042308Fanti, M. P., Mangini, A. M., & Ukovich, W. (2013). Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches. IEEE Transactions on Automation Science and Engineering, 10(2), 392-404. doi:10.1109/tase.2012.2203596Cabasino, M. P., Giua, A., & Seatzu, C. (2010). Fault detection for discrete event systems using Petri nets with unobservable transitions. Automatica, 46(9), 1531-1539. doi:10.1016/j.automatica.2010.06.013Hu, H., Zhou, M., Li, Z., & Tang, Y. (2013). An Optimization Approach to Improved Petri Net Controller Design for Automated Manufacturing Systems. IEEE Transactions on Automation Science and Engineering, 10(3), 772-782. doi:10.1109/tase.2012.2201714Hu, H., Zhou, M., & Li, Z. (2011). Supervisor Optimization for Deadlock Resolution in Automated Manufacturing Systems With Petri Nets. IEEE Transactions on Automation Science and Engineering, 8(4), 794-804. doi:10.1109/tase.2011.2156783Hiraishi, K. (1992). Construction of a class of safe Petri nets by presenting firing sequences. Lecture Notes in Computer Science, 244-262. doi:10.1007/3-540-55676-1_14Estrada-Vargas, A. P., López-Mellado, E., & Lesage, J.-J. (2010). A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems. Mathematical Problems in Engineering, 2010, 1-21. doi:10.1155/2010/453254Shaolong Shu, & Feng Lin. (2013). I-Detectability of Discrete-Event Systems. IEEE Transactions on Automation Science and Engineering, 10(1), 187-196. doi:10.1109/tase.2012.2215959Li, L., & Hadjicostis, C. N. (2011). Least-Cost Transition Firing Sequence Estimation in Labeled Petri Nets With Unobservable Transitions. IEEE Transactions on Automation Science and Engineering, 8(2), 394-403. doi:10.1109/tase.2010.2070065Supavatanakul, P., Lunze, J., Puig, V., & Quevedo, J. (2006). Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem. Control Engineering Practice, 14(6), 609-619. doi:10.1016/j.conengprac.2005.03.028Dotoli, M., Fanti, M. P., & Mangini, A. M. (2008). Real time identification of discrete event systems using Petri nets. Automatica, 44(5), 1209-1219. doi:10.1016/j.automatica.2007.10.014Chen, Y., Li, Z., Khalgui, M., & Mosbahi, O. (2011). Design of a Maximally Permissive Liveness- Enforcing Petri Net Supervisor for Flexible Manufacturing Systems. IEEE Transactions on Automation Science and Engineering, 8(2), 374-393. doi:10.1109/tase.2010.2060332Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143Ramirez-Trevino, A., Ruiz-Beltran, E., Aramburo-Lizarraga, J., & Lopez-Mellado, E. (2012). Structural Diagnosability of DES and Design of Reduced Petri Net Diagnosers. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(2), 416-429. doi:10.1109/tsmca.2011.2169950Ramirez-Trevino, A., Ruiz-Beltran, E., Rivera-Rangel, I., & Lopez-Mellado, E. (2007). Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach. IEEE Transactions on Automation Science and Engineering, 4(1), 31-39. doi:10.1109/tase.2006.872120Toutenburg, H. (1974). Fleiss, J. L.: Statistical Methods for Rates and Proportions. John Wiley & Sons, New York-London-Sydney-Toronto 1973. XIII, 233 S. Biometrische Zeitschrift, 16(8), 539-539. doi:10.1002/bimj.19740160814Livingston, E. H., & Cassidy, L. (2005). Statistical Power and Estimation of the Number of Required Subjects for a Study Based on the t-Test: A Surgeon’s Primer. Journal of Surgical Research, 126(2), 149-159. doi:10.1016/j.jss.2004.12.013Ruppert, D. (2011). Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. doi:10.1007/978-1-4419-7787-

Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets

[EN] This proposal presents an online method to detect and isolate faults in stochastic discrete event systems without previous model. A coloured timed interpreted Petri Net generates the normal behavior language after an identification stage.The next step is fault detection that is carried out by comparing the observed event sequences with the expected event sequences. Once a new fault is detected, a learning algorithm changes the structure of the diagnoser, so it is able to learn new fault languages. Moreover, the diagnoser includes timed events to represent and diagnose stochastic languages. Finally, this paper proposes a detectability condition for stochastic DES and the sufficient and necessary conditions are proved.This work was supported by a grant from the Universidad del Cauca, Reference 2.3-31.2/05 2011.Muñoz-Añasco, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2015). Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets. Mathematical Problems in Engineering. 2015:1-13. https://doi.org/10.1155/2015/303107S1132015Jiang, S., & Kumar, R. (2004). Failure Diagnosis of Discrete-Event Systems With Linear-Time Temporal Logic Specifications. IEEE Transactions on Automatic Control, 49(6), 934-945. doi:10.1109/tac.2004.829616Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for Discrete Event Systems. Annual Reviews in Control, 37(2), 308-320. doi:10.1016/j.arcontrol.2013.09.009Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555-1575. doi:10.1109/9.412626Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. C. (1996). Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology, 4(2), 105-124. doi:10.1109/87.486338Estrada-Vargas, A. P., López-Mellado, E., & Lesage, J.-J. (2010). A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems. Mathematical Problems in Engineering, 2010, 1-21. doi:10.1155/2010/453254Cabasino, M. P., Giua, A., & Seatzu, C. (2010). Fault detection for discrete event systems using Petri nets with unobservable transitions. Automatica, 46(9), 1531-1539. doi:10.1016/j.automatica.2010.06.013Prock, J. (1991). A new technique for fault detection using Petri nets. Automatica, 27(2), 239-245. doi:10.1016/0005-1098(91)90074-cAghasaryan, A., Fabre, E., Benveniste, A., Boubour, R., & Jard, C. (1998). Discrete Event Dynamic Systems, 8(2), 203-231. doi:10.1023/a:1008241818642Hadjicostis, C. N., & Verghese, G. C. (1999). Monitoring Discrete Event Systems Using Petri Net Embeddings. Application and Theory of Petri Nets 1999, 188-207. doi:10.1007/3-540-48745-x_12Benveniste, A., Fabre, E., Haar, S., & Jard, C. (2003). Diagnosis of asynchronous discrete-event systems: a net unfolding approach. IEEE Transactions on Automatic Control, 48(5), 714-727. doi:10.1109/tac.2003.811249Genc, S., & Lafortune, S. (2003). Distributed Diagnosis of Discrete-Event Systems Using Petri Nets. Lecture Notes in Computer Science, 316-336. doi:10.1007/3-540-44919-1_21Genc, S., & Lafortune, S. (2007). Distributed Diagnosis of Place-Bordered Petri Nets. IEEE Transactions on Automation Science and Engineering, 4(2), 206-219. doi:10.1109/tase.2006.879916Ramirez-Trevino, A., Ruiz-Beltran, E., Rivera-Rangel, I., & Lopez-Mellado, E. (2007). Online Fault Diagnosis of Discrete Event Systems. A Petri Net-Based Approach. IEEE Transactions on Automation Science and Engineering, 4(1), 31-39. doi:10.1109/tase.2006.872120Dotoli, M., Fanti, M. P., Mangini, A. M., & Ukovich, W. (2009). On-line fault detection in discrete event systems by Petri nets and integer linear programming. Automatica, 45(11), 2665-2672. doi:10.1016/j.automatica.2009.07.021Fanti, M. P., Mangini, A. M., & Ukovich, W. (2013). Fault Detection by Labeled Petri Nets in Centralized and Distributed Approaches. IEEE Transactions on Automation Science and Engineering, 10(2), 392-404. doi:10.1109/tase.2012.2203596Basile, F., Chiacchio, P., & De Tommasi, G. (2009). An Efficient Approach for Online Diagnosis of Discrete Event Systems. IEEE Transactions on Automatic Control, 54(4), 748-759. doi:10.1109/tac.2009.2014932Roth, M., Lesage, J.-J., & Litz, L. (2011). The concept of residuals for fault localization in discrete event systems. Control Engineering Practice, 19(9), 978-988. doi:10.1016/j.conengprac.2011.02.008Roth, M., Schneider, S., Lesage, J.-J., & Litz, L. (2012). Fault detection and isolation in manufacturing systems with an identified discrete event model. International Journal of Systems Science, 43(10), 1826-1841. doi:10.1080/00207721.2011.649369Chung-Hsien Kuo, & Han-Pang Huang. (2000). Failure modeling and process monitoring for flexible manufacturing systems using colored timed Petri nets. IEEE Transactions on Robotics and Automation, 16(3), 301-312. doi:10.1109/70.850648Ramirez-Trevino, A., Ruiz-Beltran, E., Aramburo-Lizarraga, J., & Lopez-Mellado, E. (2012). Structural Diagnosability of DES and Design of Reduced Petri Net Diagnosers. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(2), 416-429. doi:10.1109/tsmca.2011.2169950Cabasino, M. P., Giua, A., & Seatzu, C. (2014). Diagnosability of Discrete-Event Systems Using Labeled Petri Nets. IEEE Transactions on Automation Science and Engineering, 11(1), 144-153. doi:10.1109/tase.2013.2289360Yao, L., Feng, L., & Jiang, B. (2014). Fault Diagnosis and Fault Tolerant Control for Non-Gaussian Singular Time-Delayed Stochastic Distribution Systems. Mathematical Problems in Engineering, 2014, 1-9. doi:10.1155/2014/937583Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143Dotoli, M., Fanti, M. P., & Mangini, A. M. (2008). Real time identification of discrete event systems using Petri nets. Automatica, 44(5), 1209-1219. doi:10.1016/j.automatica.2007.10.014Muñoz, D. M., Correcher, A., García, E., & Morant, F. (2014). Identification of Stochastic Timed Discrete Event Systems with st-IPN. Mathematical Problems in Engineering, 2014, 1-21. doi:10.1155/2014/835312Latorre-Biel, J.-I., Jiménez-Macías, E., Pérez de la Parte, M., Blanco-Fernández, J., & Martínez-Cámara, E. (2014). Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities. Abstract and Applied Analysis, 2014, 1-16. doi:10.1155/2014/821707Cabasino, M. P., Giua, A., Lafortune, S., & Seatzu, C. (2012). A New Approach for Diagnosability Analysis of Petri Nets Using Verifier Nets. IEEE Transactions on Automatic Control, 57(12), 3104-3117. doi:10.1109/tac.2012.2200372Abdelwahed, S., Karsai, G., Mahadevan, N., & Ofsthun, S. C. (2009). Practical Implementation of Diagnosis Systems Using Timed Failure Propagation Graph Models. IEEE Transactions on Instrumentation and Measurement, 58(2), 240-247. doi:10.1109/tim.2008.200595

Diagnosis of a battery energy storage system based on principal component analysis

[EN] This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS. The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions.The authors would like to acknowledge the research project "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco, BPIN 2013000100285 (in Spanish)" and the Universidad TecnolOgica del Choco (in Spanish). The authors would like to thank the anonymous reviewers as well as the editor for their valuable comments that have greatly improved the final version of the paper.Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro Gómez, Á.; García Moreno, E.; Aristizabal, A. (2020). Diagnosis of a battery energy storage system based on principal component analysis. Renewable Energy. 146:2438-2449. https://doi.org/10.1016/j.renene.2019.08.064S24382449146Perera, A. T. D., Attalage, R. A., Perera, K. K. C. K., & Dassanayake, V. P. C. (2013). Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission. Energy, 54, 220-230. doi:10.1016/j.energy.2013.03.028Krieger, E. M., Cannarella, J., & Arnold, C. B. (2013). A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications. Energy, 60, 492-500. doi:10.1016/j.energy.2013.08.029Aksakal, C., & Sisman, A. (2018). On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy. Energies, 11(1), 118. doi:10.3390/en11010118Dhundhara, S., Verma, Y. P., & Williams, A. (2018). Techno-economic analysis of the lithium-ion and lead-acid battery in microgrid systems. Energy Conversion and Management, 177, 122-142. doi:10.1016/j.enconman.2018.09.030Li, X., Shu, X., Shen, J., Xiao, R., Yan, W., & Chen, Z. (2017). An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles. Energies, 10(5), 691. doi:10.3390/en10050691Ariza Chacón, H., Banguero, E., Correcher, A., Pérez-Navarro, Á., & Morant, F. (2018). Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms. Energies, 11(9), 2361. doi:10.3390/en11092361Copetti, J. B., Lorenzo, E., & Chenlo, F. (1993). A general battery model for PV system simulation. Progress in Photovoltaics: Research and Applications, 1(4), 283-292. doi:10.1002/pip.4670010405Guasch, D., & Silvestre, S. (2003). Dynamic battery model for photovoltaic applications. Progress in Photovoltaics: Research and Applications, 11(3), 193-206. doi:10.1002/pip.480Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2016). An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications. Applied Energy, 169, 888-898. doi:10.1016/j.apenergy.2016.02.062Blaifi, S., Moulahoum, S., Colak, I., & Merrouche, W. (2017). Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system. Electrical Engineering, 100(2), 1021-1038. doi:10.1007/s00202-017-0567-6Gao, Z., Cecati, C., & Ding, S. X. (2015). A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757-3767. doi:10.1109/tie.2015.2417501Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304Jiang, Q., Yan, X., & Zhao, W. (2013). Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis. Industrial & Engineering Chemistry Research, 52(4), 1635-1644. doi:10.1021/ie3017016Fan, J., & Wang, Y. (2014). Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis. Information Sciences, 259, 369-379. doi:10.1016/j.ins.2013.06.021Garcia-Alvarez, D., Fuente, M. J., & Sainz, G. I. (2012). Fault detection and isolation in transient states using principal component analysis. Journal of Process Control, 22(3), 551-563. doi:10.1016/j.jprocont.2012.01.007Banguero, E., Aristizábal, A. J., & Murillo, W. (2017). A Verification Study for Grid-Connected 20 kW Solar PV System Operating in Chocó, Colombia. Energy Procedia, 141, 96-101. doi:10.1016/j.egypro.2017.11.019Rahman, M. A., Anwar, S., & Izadian, A. (2016). Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources, 307, 86-97. doi:10.1016/j.jpowsour.2015.12.083Yang, X., Chen, L., Xu, X., Wang, W., Xu, Q., Lin, Y., & Zhou, Z. (2017). Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization. Energies, 10(11), 1811. doi:10.3390/en10111811Kai, H., Yong-Fang, G., Zhi-Gang, L., Hsiung-Cheng, L., & Ling-Ling, L. (2018). Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm. Mathematical Problems in Engineering, 2018, 1-13. doi:10.1155/2018/3793492Venter, G., & Sobieszczanski-Sobieski, J. (2003). Particle Swarm Optimization. AIAA Journal, 41(8), 1583-1589. doi:10.2514/2.2111Layadi, T. M., Champenois, G., Mostefai, M., & Abbes, D. (2015). Lifetime estimation tool of lead–acid batteries for hybrid power sources design. Simulation Modelling Practice and Theory, 54, 36-48. doi:10.1016/j.simpat.2015.03.001Rahmani, M., & Atia, G. K. (2017). Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis. IEEE Transactions on Signal Processing, 65(23), 6260-6275. doi:10.1109/tsp.2017.2749215Bro, R., & Smilde, A. K. (2014). Principal component analysis. Anal. Methods, 6(9), 2812-2831. doi:10.1039/c3ay41907jGranato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology, 72, 83-90. doi:10.1016/j.tifs.2017.12.006Soh, W., Kim, H., & Yum, B.-J. (2015). Application of kernel principal component analysis to multi-characteristic parameter design problems. Annals of Operations Research, 263(1-2), 69-91. doi:10.1007/s10479-015-1889-2Deng, X., Tian, X., Chen, S., & Harris, C. J. (2018). Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis. IEEE Transactions on Neural Networks and Learning Systems, 29(3), 560-572. doi:10.1109/tnnls.2016.2635111De Ketelaere, B., Hubert, M., & Schmitt, E. (2015). Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data. Journal of Quality Technology, 47(4), 318-335. doi:10.1080/00224065.2015.11918137Vanhatalo, E., Kulahci, M., & Bergquist, B. (2017). On the structure of dynamic principal component analysis used in statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 167, 1-11. doi:10.1016/j.chemolab.2017.05.016Zhao, C., Wang, F., Gao, F., Lu, N., & Jia, M. (2007). Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data. Industrial & Engineering Chemistry Research, 46(14), 4943-4953. doi:10.1021/ie061320fNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. 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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. 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A modular neural network scheme applied to fault diagnosis in electric power systems

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer.The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.Flores, A.; Quiles Cucarella, E.; García Moreno, E.; Morant Anglada, FJ.; Correcher Salvador, A. (2014). A modular neural network scheme applied to fault diagnosis in electric power systems. Scientific World Journal. 2014:1-13. doi:10.1155/2014/176463S1132014Yongli, Z., Limin, H., & Jinling, L. (2006). Bayesian Networks-Based Approach for Power Systems Fault Diagnosis. IEEE Transactions on Power Delivery, 21(2), 634-639. doi:10.1109/tpwrd.2005.858774Aggarwal, R., & Song, Y. (1997). Artificial neural networks in power systems. Part 1: General introduction to neural computing. Power Engineering Journal, 11(3), 129-134. doi:10.1049/pe:19970306Faria, L., Silva, A., Vale, Z., & Marques, A. (2009). Training Control Centers’ Operators in Incident Diagnosis and Power Restoration Using Intelligent Tutoring Systems. IEEE Transactions on Learning Technologies, 2(2), 135-147. doi:10.1109/tlt.2009.16Rigatos, G., Piccolo, A., & Siano, P. (2009). Neural network-based approach for early detection of cascading events in electric power systems. IET Generation, Transmission & Distribution, 3(7), 650-665. doi:10.1049/iet-gtd.2008.0475Guo, W., Wen, F., Ledwich, G., Liao, Z., He, X., & Liang, J. (2010). An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers. IEEE Transactions on Power Delivery, 25(3), 1393-1401. doi:10.1109/tpwrd.2010.2048344Ravikumar, B., Thukaram, D., & Khincha, H. P. (2008). Application of support vector machines for fault diagnosis in power transmission system. IET Generation, Transmission & Distribution, 2(1), 119. doi:10.1049/iet-gtd:20070071Aggarwal, R., & Yonghua Song. (1998). Artificial neural networks in power systems. Part 2: Types of artificial neural networks. Power Engineering Journal, 12(1), 41-47. doi:10.1049/pe:19980110Salim, R. H., de Oliveira, K., Filomena, A. D., Resener, M., & Bretas, A. S. (2008). Hybrid Fault Diagnosis Scheme Implementation for Power Distribution Systems Automation. IEEE Transactions on Power Delivery, 23(4), 1846-1856. doi:10.1109/tpwrd.2008.91791

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

Augmentation Channel Design for a Marine Current Turbine in a Floating Cogenerator

[EN] In this paper we present a Hydro-Wind Kinetics Integrated Module for Renewable Energy Generation. HYWIKIM is a floating device combining wind and marine current generators for generating renewable energy. Its purpose is to exploit resources in an integrated manner using wind and current turbines in offshore plants thereby optimizing the financial investment. Our research focuses on the design and analysis of different types of augmentation channels to increase efficiency using shrouded Marine Current Turbines (MCTs) in conditions of low intensity flows.García Moreno, E.; Pizá Fernández, R.; Quiles Cucarella, E.; Correcher Salvador, A.; Morant Anglada, FJ. (2017). Augmentation Channel Design for a Marine Current Turbine in a Floating Cogenerator. IEEE Latin America Transactions. 15(6):1068-1076. doi:10.1109/TLA.2017.7932694S1068107615

Self-growing Colored Petri Net for offshore wind turbines maintenance systems

The offshore wind turbines have been developed in a lot of aspects in the last years, but the big companies are still researching for new techniques that help improve the systems. We propose a new methodology to implement the automatic maintenance system using self-growing colored Petri nets developed in Labview, extendable to other industry systems.Perez Collada, MJ.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ.; Quiles Cucarella, E. (2011). Self-growing Colored Petri Net for offshore wind turbines maintenance systems. Renewable energy & power quality journal. (9):381-386. http://hdl.handle.net/10251/45123S381386