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Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching

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Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching

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dc.contributor.author Fernandez-Serantes, Luis Alfonso es_ES
dc.contributor.author Casteleiro-Roca, Jose Luis es_ES
dc.contributor.author Calvo-Rolle, Jose Luis es_ES
dc.date.accessioned 2022-10-05T07:41:57Z
dc.date.available 2022-10-05T07:41:57Z
dc.date.issued 2022-09-30
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/187020
dc.description.abstract [EN] In this work, an intelligent control based on artificial intelligence is presented. This novel control strategy aims to ensure thata half-bridge boost converter operates in soft-switching mode. As first step, an analysis of the power circuit is done, presentingthe two possible operating modes: Hard- and Soft-Switching. Then, a hybrid intelligent model is implemented with the aim ofclassifying the converter operating mode. A clustering method and three different classification algorithms are implemented and thecomparison between their results is done. Moreover, the intelligent model is implemented in the control loop of the converter withthe aim of ensuring that the converter operates in Soft-switching mode. es_ES
dc.description.abstract [ES] En este trabajo de investigación se presenta una estrategia de control inteligente implementada en un convertidor elevador con topología de medio puente. El sistema se usa para asegurar que el convertidor funcione en modo "Soft-Switching". El primer paso es realizar el análisis del convertidor de potencia, mostrando los dos posibles modos de funcionamiento: "Hard-Switching" y "Soft-Switching". Posteriormente se implementa un modelo inteligente con el fin de identificar el modo de funcionamiento del convertidor. Este modelo se basa en un algoritmo de clasificación mediante técnicas inteligentes que es capaz de diferenciar entre los dos modos de funcionamiento. Se han obtenido muy buenos resultados de clasificación y una alta precisión, permitiendo la implementación del modelo en la estrategia de control del convertidor. La implementacion de este sistema permite asegurar  que el convertidor funcione en el modo deseado: modo "Soft-Switching". es_ES
dc.description.sponsorship El CITIC, como Centro de Investigación del Sistema Universitario de Galicia, esta financiado por la Conselleria de Educación, Universidade e Formación Profesional de la Xunta de Galicia a través del Fondo Europeo de Desarrollo Regional (FEDER) y la Secretaria Xeral de Universidades (Ref.ED431G2019 / 01). es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Classification es_ES
dc.subject Half-bridge buck es_ES
dc.subject Power electronics es_ES
dc.subject Soft-switching es_ES
dc.subject Hard-switching es_ES
dc.subject Clasificación es_ES
dc.subject Convertidor elevador es_ES
dc.subject Electrónica de potencia es_ES
dc.subject Conmutación suave es_ES
dc.subject Conmutación dura es_ES
dc.title Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching es_ES
dc.title.alternative Hybrid intelligent system for detection of Soft-Switching mode and control of a boost converter es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2022.16656
dc.relation.projectID info:eu-repo/grantAgreement/Xunta de Galicia//ED431G2019%2F01 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Fernandez-Serantes, LA.; Casteleiro-Roca, JL.; Calvo-Rolle, JL. (2022). Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching. Revista Iberoamericana de Automática e Informática industrial. 19(4):356-368. https://doi.org/10.4995/riai.2022.16656 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2022.16656 es_ES
dc.description.upvformatpinicio 356 es_ES
dc.description.upvformatpfin 368 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\16656 es_ES
dc.contributor.funder Xunta de Galicia es_ES
dc.description.references Agrawal, U., Soria, D., Wagner, C., Garibaldi, J., Ellis, I.O., Bartlett, J.M., Cameron, D., Rakha, E.A., Green, A.R., 2019. Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles. Artificial Intelligence in Medicine 97, 27 - 37. https://doi.org/10.1016/j.artmed.2019.05.002 es_ES
dc.description.references Al-bayati, A.M.S., Alharbi, S.S., Alharbi, S.S., Matin, M., 2017. A comparative design and performance study of a non-isolated dc-dc buck converter based on si-mosfet/si-diode, sic-jfet/sic-schottky diode, and gan-transistor/sicschottky diode power devices, in: 2017 North American Power Symposium (NAPS), pp. 1-6. doi:10.1109/NAPS.2017.8107192. https://doi.org/10.1109/NAPS.2017.8107192 es_ES
dc.description.references Beiranvand, R., Rashidian, B., Zolghadri, M.R., Alavi, S.M.H., 2011. Using llc resonant converter for designing wide-range voltage source. IEEE Transactions on Industrial Electronics 58, 1746-1756. doi:10.1109/TIE.2010.2052537. https://doi.org/10.1109/TIE.2010.2052537 es_ES
dc.description.references Düntsch, I., Gediga, G., 2020. Indices for rough set approximation and the application to confusion matrices. International Journal of Approximate Reasoning 118, 155 - 172. doi:https://doi.org/10.1016/j.ijar.2019.12.008 https://doi.org/10.1016/j.ijar.2019.12.008 es_ES
dc.description.references Eraydin, H., Bakan, A.F., 2020. E ciency comparison of asynchronous and synchronous buck converter, in: 2020 6th International Conference on Electric Power and Energy Conversion Systems (EPECS), pp. 30-33. https://doi.org/10.1109/EPECS48981.2020.9304966 es_ES
dc.description.references Fernandez-Serantes, L.A., Berger, H., Stocksreiter, W., Weis, G., 2016. Ultrahigh frequent switching with gan-hemts using the coss-capacitances as nondissipative snubbers, in: PCIM Europe 2016; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, pp. 1-8. es_ES
dc.description.references GaN-Systems, 2018. GS66516T Top-side cooled 650 V E-mode GaN transistor. GaN Systems Inc. Rev 180422. es_ES
dc.description.references Gueguen, P., 2015. How power electronics will reshape to meet the 21st century challenges?, in: 2015 IEEE 27th International Symposium on Power Semiconductor Devices IC's (ISPSD), pp. 17-20. https://doi.org/10.1109/ISPSD.2015.7123378 es_ES
dc.description.references Guillod, T., Papamanolis, P., W. Kolar, J., 2020. Artificial neural network (ann) based fast and accurate inductor modeling and design. IEEE Open Journal of Power Electronics 1, 284-299. doi:10.1109/OJPEL.2020.3012777. https://doi.org/10.1109/OJPEL.2020.3012777 es_ES
dc.description.references Huang, G.C., Liang, T.J., Chen, K.H., 2012. Losses analysis and low standby losses quasi-resonant flyback converter design, in: 2012 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 217-220. https://doi.org/10.1109/ISCAS.2012.6271718 es_ES
dc.description.references Kaski, S., Sinkkonen, J., Klami, A., 2005. Discriminative clustering. Neurocomputing 69, 18-41. https://doi.org/10.1016/j.neucom.2005.02.012 es_ES
dc.description.references Li, Y., Ruan, X., Zhang, L., Dai, J., Jin, Q., 2019. Optimized parameters design and adaptive duty-cycle adjustment for class e dc-dc converter with on-off control. IEEE Transactions on Power Electronics 34, 7728-7744. https://doi.org/10.1109/TPEL.2018.2881170 es_ES
dc.description.references Liu, M.Z., Shao, Y.H., Li, C.N., Chen, W.J., 2020. Smooth pinball loss nonparallel support vector machine for robust classification. Applied Soft Computing , 106840doi:https://doi.org/10.1016/j.asoc.2020.106840. https://doi.org/10.1016/j.asoc.2020.106840 es_ES
dc.description.references Marchesan, G., Muraro, M., Cardoso, G., Mariotto, L., da Silva, C., 2016. Method for distributed generation anti-islanding protection based on singular value decomposition and linear discrimination analysis. Electric Power Systems Research 130, 124 - 131. https://doi.org/10.1016/j.epsr.2015.08.025 es_ES
dc.description.references Mohan, N., Undeland, T.M., Robbins, W.P., 2003. Power electronics: converters, applications, and design. John wiley & sons. es_ES
dc.description.references Neumayr, D., Bortis, D., Kolar, J.W., 2020. The essence of the little box challenge-part a: Key design challenges solutions. CPSS Transactions on Power Electronics and Applications 5, 158-179. https://doi.org/10.24295/CPSSTPEA.2020.00014 es_ES
dc.description.references Qin, A.K., Suganthan, P.N., 2005. Enhanced neural gas network for prototypebased clustering. Pattern recognition 38, 1275-1288. https://doi.org/10.1016/j.patcog.2004.12.007 es_ES
dc.description.references Tahiliani, S., Sreeni, S., Moorthy, C.B., 2019. A multilayer perceptron approach to track maximum power in wind power generation systems, in: TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), pp. 587-591. doi:10.1109/TENCON.2019.8929414. https://doi.org/10.1109/TENCON.2019.8929414 es_ES
dc.description.references Tao Liu, Wenjun Zhang, Zhiping Yu, 2005. Modeling of spiral inductors using artificial neural network, in: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., pp. 2353-2358 vol. 4. doi:10.1109/IJCNN.2005.1556269. es_ES
dc.description.references Thapngam, T., Yu, S., Zhou, W., 2012. Ddos discrimination by linear discriminant analysis (lda), in: 2012 International Conference on Computing, Networking and Communications (ICNC), IEEE. pp. 532-536. https://doi.org/10.1109/ICCNC.2012.6167480 es_ES
dc.description.references Tulbure, A., Kadar, M., 2017. Power electronics methods to improve energy effciency in the public transportation system, in: 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1277-1281. doi:10.1109/ICE.2017.8280027. https://doi.org/10.1109/ICE.2017.8280027 es_ES
dc.description.references Uysal, I., G¨uvenir, H.A., 1999. An overview of regression techniques for knowledge discovery. The Knowledge Engineering Review 14, 319-340. https://doi.org/10.1017/S026988899900404X es_ES
dc.description.references Wang, Z., Lou, Z., Chen, H., 2007. A novel dual-llc resonant soft switching converter for super high frequency induction heating power supplies, in: 2007 IEEE Power Electronics Specialists Conference, pp. 2561-2566. https://doi.org/10.1109/PESC.2007.4342418 es_ES
dc.description.references Wei, C., Zhang, Z., Qiao, W., Qu, L., 2015. Reinforcement-learning-based intelligent maximum power point tracking control for wind energy conversion systems. IEEE Transactions on Industrial Electronics 62, 6360-6370. https://doi.org/10.1109/TIE.2015.2420792 es_ES
dc.description.references Whitaker, B., Barkley, A., Cole, Z., Passmore, B., McNutt, T., Lostetter, A.B., 2013. High-frequency ac-dc conversion with a silicon carbide power module to achieve high-effciency and greatly improved power density, in: 2013 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 1-5. doi:10.1109/PEDG.2013.6785611. https://doi.org/10.1109/PEDG.2013.6785611 es_ES
dc.description.references Zhan, X., Wang, W., Chung, H., 2018. A neural-network-based color control method for multi-color led systems. IEEE Transactions on Power Electronics 34, 7900-7913. https://doi.org/10.1109/TPEL.2018.2880876 es_ES
dc.description.references Zhao, S., Blaabjerg, F., Wang, H., 2021. An overview of artificial intelligence applications for power electronics. IEEE Transactions on Power Electronics 36, 4633-4658. doi:10.1109/TPEL.2020.3024914. https://doi.org/10.1109/TPEL.2020.3024914 es_ES


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