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

  1. Fernandez-Serantes, Luis Alfonso 1
  2. Casteleiro-Roca, Jose Luis 1
  3. Calvo-Rolle, Jose Luis 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2022

Volumen: 19

Número: 4

Páginas: 356-368

Tipo: Artículo

DOI: 10.4995/RIAI.2022.16656 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

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".

Referencias bibliográficas

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • GaN-Systems, 2018. GS66516T Top-side cooled 650 V E-mode GaN transistor. GaN Systems Inc. Rev 180422.
  • 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
  • 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
  • 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
  • Kaski, S., Sinkkonen, J., Klami, A., 2005. Discriminative clustering. Neurocomputing 69, 18-41. https://doi.org/10.1016/j.neucom.2005.02.012
  • 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
  • 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
  • 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
  • Mohan, N., Undeland, T.M., Robbins, W.P., 2003. Power electronics: converters, applications, and design. John wiley & sons.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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