Fault Diagnosis of Electric Transmission Lines Using Modular Neural Networks

"(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."This paper proposes a new method for fault diagnosis in electric power systems based on neural networks. With this method the diagnosis is performed by assigning a neural module for each type of component of 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. The neural module for transmission lines 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. (2016). Fault Diagnosis of Electric Transmission Lines Using Modular Neural Networks. IEEE Latin America Transactions. 14(8):3663-3668. doi:10.1109/TLA.2016.7786348S3663366814

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

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

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-

Avances en el diagnóstico de fallas en sistemas eléctricos de transporte mediante redes neuronales: Un enfoque modular

[EN] 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 generic neural module for each type of element within the electric power system, whether it is a transmission line, bus or transformer. A total of three generic neural modules are designed, one for each type of element. The neural modules for buses and transformers comprise two diagnostic levels that take into consideration the logic states of switches and relays, both internal and back up; the neural module for transmission lines, however, also has a third diagnostic level which takes into account waveforms of fault voltages and currents, as well as the frequency spectrums of these waveforms in order to verify if the line had in fact been subjected to a fault, and at the same time to determine which type of fault (L-g, LL-g, LL, LLL, LLL-g),by means of a neural structure. This third diagnostic level can be carried out given the fact that every transportation line subjected to a fault will present fault currents and voltages before the fault is eliminated from the system by the intervention of its protection systems.[ES] En este trabajo se propone un nuevo método para el diagnóstico de fallas en sistemas eléctricos de potencia basado en módulos neuronales. El método realiza el diagnóstico mediante la asignación de un módulo neuronal genérico para cada tipo de componente que conforma al sistema eléctrico de potencia, ya sea línea de transporte, bus o transformador. En total se diseñan tres módulos neuronales genéricos, uno para cada tipo de componente. Los módulos neuronales para buses y transformadores se componen de dos niveles de diagnóstico tomando en cuenta los estados lógicos de interruptores y relevadores tanto propios como de respaldo, a excepción del módulo neuronal para líneas de transporte, que además de los dos niveles de diagnóstico con los que cuentan los módulos neuronales para buses y transformadores, cuenta con un tercer nivel de diagnóstico que toma en consideración los oscilogramas de voltajes y corrientes de falla, así como los espectros de frecuencia de estos oscilogramas, para verificar si la línea de transporte realmente estuvo sujeta a una falla y a la vez determinar el tipo de ésta (L-g, LL-g, LL, LLL, LLL-g), esto a través de una estructura neuronal. Este tercer nivel de diagnóstico, es posible llevarlo a cabo ya que toda línea de transporte que es sometida a una falla presentará corrientes y voltajes de falla, antes de que ésta sea liberada del sistema por la acción de sus esquemas de protecciónFlores Novelo, AA.; Quiles Cucarella, E.; García Moreno, E.; Morant Anglada, FJ. (2011). Avances en el diagnóstico de fallas en sistemas eléctricos de transporte mediante redes neuronales: Un enfoque modular. Revista de Ingeniería Electrónica, Automática y Comunicaciones. XXXII(2):1-14. http://hdl.handle.net/10251/40275S114XXXII

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

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

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

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

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

[EN] Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery's user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current-constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.The authors would like to acknowledge the research project “Implementación de un programa de desarrollo e investigación de energías renovables en el departamento del Chocó, BPIN 2013000100285” and the Universidad Tecnológica del Chocó.Banguero-Palacios, E.; Correcher Salvador, A.; Pérez-Navarro, Á.; Morant Anglada, FJ.; Aristizabal Cardona, AJ. (2018). A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems. Energies. 11(4):1-15. https://doi.org/10.3390/en11041021S11511