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A Novel Approach Integrated Dc To Dc Fast Charging Module

Satheesh G. Kumar, Mohanbabu G., Padmanabhan D., Praveen K., Sabapathi S.

Abstract


This paper presents a wide variation of input dc voltage. It is a “step-up” inverter, meaning that only one power stage works at high  frequency in  order  to achieve  minimum switching  loss.  When  input  dc  voltage  is  lower  than  the magnitude of the ac voltage, it is a voltage-source inverter, and on it is current-source inverter in the other mode. The leakage current caused by the capacitance can be reduced to zero theoretically due to the characteristics of the converter configuration. The output ac voltage of the proposed inverter can be higher than input dc voltage. Only five active switches are used in the presented inverter, and those switching devices can be synchronous driven by various sinusoidal pulse width modulation methods. Finally, the simulation and experimental results are obtained in a prototype.


Keywords


Integrated DC, voltage, Network Model, Charging Module, PV module

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References


Khan, M. A. Z. Raja, M. Shoaib, P. Kumam, H.

Alrabaiah, Z. Shah, and S. Islam, ‘‘Design of neural network with Levenberg-Marquardt and Bayesian regularization backpropagation for solving pantograph delay differential equations,’’ IEEE Access, vol. 8, pp.137918–137933, 2020

A. Ul-Haq, H. F. Sindi, S. Gul, and M. Jalal, ‘‘Modeling and fault categorization in thin-film and crystalline PV arrays through multilayer neural network algorithm,’’ IEEE Access, vol. 8, pp. 102235–102255, 2020

T. Dragičević, P. Wheeler, and F. Blaabjerg, ‘‘Artificial intelligence aided automated design for reliability of power electronic systems,’’ IEEE Trans. Power Electron., vol. 34, no. 8, pp. 7161–7171, Aug. 2019

Ashokkumar, S. R., Anupallavi, S., MohanBabu, G., Premkumar, M., & Jeevanantham, V. (2022). Emotion identification by dynamic entropy and ensemble learning from electroencephalogram signals. International Journal of Imaging Systems and Technology, 32(1), 402-413.

M. Sinecen, ‘‘Comparison of genomic best linear unbiased prediction and Bayesian regularization neural networks for genomic selection,’’ IEEE Access, vol. 7, pp.

–79210, 2019

Premkumar, M., Ashokkumar, S. R., Jeevanantham, V., Pallavi, S. A., Mohanbabu, G., & Raaj, R. S. (2022). Design of cost-effective real time tremor alerting system for patients of neurodegenerative diseases. Materials Today: Proceedings, 57, 1989-1994.

W. Lee, K. Kim, J. Park, J. Kim, and Y. Kim,

‘‘Forecasting solar power using long-short term memory and convolutional neural networks,’’ IEEE Access, vol. 6, pp. 73068–73080, 2018

MohanBabu, G., Anupallavi, S., & Ashokkumar, S. R. (2021). An optimized deep learning network model for eeg based seizure classification using synchronization and functional connectivity measures. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7139-7151.

Premkumar, M., Kathiravan, M., & Thirukkumaran, R. (2012). A novel mac algorithm for energy aware wireless sensor networks. International Journal of Computer Applications, 58(5).

Yris, JC, Calleja, JH, Sanchez, AC & Gonzalez, LH 2020, “Study of a Family of Buck-Boost Converter with Tapped Inductor for Grid Connected Photovoltaic Systems‟, Electronics, Robotics and Automotive Mechanics Conference, pp. 581-585.

Premkumar, M., Sundararajan, T. V. P., & Mohanbabu, G. (2022). Dynamic Defense Mechanism for DoS Attacks in Wireless Environments Using Hybrid Intrusion Detection System and Statistical Approaches. Tehnički vjesnik, 29(3), 965-970.

J. He, L. Du, C. Wang, “Supply voltage and grid current harmonics compensation using multiport interfacing converter integrated into Two-AC- Bus grid,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3057–3070, 2019.


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