DOI QR코드

DOI QR Code

Improved Estimation Method for the Capacitor Voltage in Modular Multilevel Converters Using Distributed Neural Network Observer

  • Mehdi Syed Musadiq (School of Electronic and Electrical Engineering, Hongik University) ;
  • Dong-Myung Lee (School of Electronic and Electrical Engineering, Hongik University)
  • Received : 2023.11.01
  • Accepted : 2023.12.11
  • Published : 2023.12.31

Abstract

The Modular Multilevel Converter (MMC) has emerged as a key component in HVDC systems due to its ability to efficiently transmit large amounts of power over long distances. In such systems, accurate estimation of the MMC capacitor voltage is of utmost importance for ensuring optimal system performance, stability, and reliability. Traditional methods for voltage estimation may face limitations in accuracy and robustness, prompting the need for innovative approaches. In this paper, we propose a novel distributed neural network observer specifically designed for MMC capacitor voltage estimation. Our observer harnesses the power of a multi-layer neural network architecture, which enables the observer to learn and adapt to the complex dynamics of the MMC system. By utilizing a distributed approach, we deploy multiple observers, each with its own set of neural network layers, to collectively estimate the capacitor voltage. This distributed configuration enhances the accuracy and robustness of the voltage estimation process. A crucial aspect of our observer's performance lies in the meticulous initialization of random weights within the neural network. This initialization process ensures that the observer starts with a solid foundation for efficient learning and accurate voltage estimation. The observer iteratively updates its weights based on the observed voltage and current values, continuously improving its estimation accuracy over time. The validity of proposed algorithm is verified by the result of estimated voltage at each observer in capacitor of MMC.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning(NRF-2021R1A2C100578212).

References

  1. Y. Li and B. Wu, "A novel DC voltage detection technique in the CHB inverter-based STATCOM," IEEE Trans. Power Deliv., vol.23, no.3, pp.1613-1619, 2008. DOI: 10.1109/TPWRD.2008.919251.
  2. Z. M. Bibalan, M. R. A. Pahlavani, and S. M. M. Gazafroodi, "Adaptive back stepping observer design for direct current link capacitor voltages of multi port high power converter," 30th Power Syst. Conf. PSC, pp.351-357, 2017. DOI: 10.1109/IPSC. 015.7827772.
  3. V. Najmi, H. Nademi, and R. Burgos, "An adaptive backstepping observer for modular multilevel converter," IEEE Energy Convers. Congr. Expo., pp.2115-2120, 2014. DOI: 10.1109/ECCE.2014.6953682.
  4. G. S. Da Silva, R. P. Vieira, and C. Rech, "Modified sliding-mode observer of capacitor voltages in Modular Multilevel Converter," IEEE 13th Brazilian Power Electr. Conf., pp.1-6, 2015. DOI: 10.1109/COBEP.2015.7420217.
  5. H. Nademi, A. Das, and L. E. Norum, "Modular multilevel converter with an adaptive observer of capacitor voltages," IEEE Trans. Power Electron., vol.30, no.1, pp.235-248, 2015. DOI: 10.1109/TPEL.2014.2301879.
  6. S. D'Arco and J.A. Suul, "Estimation of submodule capacitor voltages in modular multilevel converters," Eur. Conf. Power Electron. pp.1-10, 2013. DOI: 10.1109/EPE.2013.6631931.
  7. G. Farivar, V. G. Agelidis, and B. Hredzak, "A generalized capacitors voltage estimation scheme for multilevel converters," Eur. Conf. Power Electron. pp.1-5, 2014. DOI: 10.1109/EPE.2014.6910926.
  8. S. Haghnazari and M.R. Zolghadri, "A novel voltage measurement technique for modular multilevel converter capacitors," IECON, pp.238-243, 2015. DOI: 10.1109/IECON.2015.7392105.
  9. O. S. M. Abushafa, S. M. Gadoue, M. S. A. Dahidah, D. J. Atkinson, and P. Missailidis, "Capacitor voltage estimation scheme with reduced number of sensors for modular multilevel converters," IEEE J. Emerg. Sel. Top. Power Electron., vol.6, no.4, pp.2086-2097, 2018. DOI: 10.1109/JESTPE.2018.2797245.
  10. R. Picas, J. Zaragoza, J. Pou, S. Ceballos, and J. Balcells, "New measuring technique for reducing the number of voltage sensors in Modular Multilevel Converters," IEEE Trans. Power Electron., vol.31, no.1, pp.177-187, 2016. DOI: 10.1109/TPEL.2015.2412658.
  11. O. S. H. M. Abushafa, M.S.A. Dahidah, S. M. Gadoue, and D. J. Atkinson, "Submodule voltage estimation scheme in Modular Multilevel Converters with reduced voltage sensors based on Kalman Filter approach," IEEE Trans. Ind. Electron., vol 65, no.9, pp.7025-7035, 2018. DOI: 10.1109/TIE.2018.2795519.
  12. A. Khodaparast, E. Azimi, A. Azimi, M. E. Adabi, J. Adabi, and E. Pouresmaeil, "A new modular multilevel inverter based on step-up switched-capacitor modules," Energies, vol.12, no.3, 2019. DOI: 10.3390/en12030524.
  13. E. Solas, G. Abad, J. A. Barrena, A. Carcar, and S. Aurtenetxea, "Modulation of Modular Multilevel Converter for HVDC application," Proc. EPE-PEMC, pp.84-89, 2010. DOI: 10.1109/EPEPEMC.2010.5606876.
  14. M. Guan and Z. Xu, "Modeling and control of a modular multilevel converter-based HVDC system under unbalanced grid conditions," IEEE Trans. Power Electron., vol.27, no.12, pp.4858-4867, 2012. DOI: 10.1109/TPEL.2012.2192752.
  15. E. Solas, G. Abad, J. A. Barrena, A. Carcar, and S. Aurtenetxea, "Modelling, simulation and control of Modular Multilevel Converter," Proc. EPE-PEMC, pp.90-96, 2010. DOI: 10.1109/EPEPEMC.2010.5606881.
  16. Y. Zhu, Q. Guo, C. Li, D. Chang, D. Chen, and Y. Zhu, "Research on power modulation strategy for MMC-HVDC and LCC-HVDC in parallel HVDC system," IEEE Conf. Energy Internet Energy Syst. Integr. Ubiquitous Energy Netw. Connect. Everything, pp.1456-1461, 2019. DOI: 10.1109/EI247390.2019.9061853.
  17. P. Poblete, G. Pizarro, G. Droguett, F. Nunez, P.D. Judge, and J. Pereda, "Distributed neural network observer for submodule capacitor voltage Estimation in Modular Multilevel Converters," IEEE Trans. Power Electron., vol.37, no.9, pp. 1030610318, 2022. DOI: 10.1109/TPEL.2022. 3163395.