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Analysis of Power Loss in Forward Converter Transformer Using a Novel Machine Learning Based Optimization Framework

Analysis of Power Loss in Forward Converter Transformer Using a Novel Machine Learning Based... High power and high voltage gains in Forward Converter significance are a foremost topic for wind power switch mode supplies. However, due to the recent penetration of forward converter usage in the power network and wide range of load, the reliability and power loss problem becomes a crucial one. Therefore, this paper projected a novel Grey Wolf based Boosting Intelligent Frame (GWbBIF) control algorithm for improving the reliability of power system since the incorporation of forward converter to the entire system level. Consequently, the power loss of transformer is optimized by the projected Grey Wolf fitness function. The implementation of this work has been done on MATLAB/Simulink. The simulation outcomes of the proposed system show that the forward converter reliability and power loss of transformer are considered as significant aspects while estimating the whole system function. The proposed outcomes are compared with the conventional methods for validating the importance of the projected method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

Analysis of Power Loss in Forward Converter Transformer Using a Novel Machine Learning Based Optimization Framework

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022
eISSN
2199-4706
DOI
10.1007/s40866-022-00145-y
Publisher site
See Article on Publisher Site

Abstract

High power and high voltage gains in Forward Converter significance are a foremost topic for wind power switch mode supplies. However, due to the recent penetration of forward converter usage in the power network and wide range of load, the reliability and power loss problem becomes a crucial one. Therefore, this paper projected a novel Grey Wolf based Boosting Intelligent Frame (GWbBIF) control algorithm for improving the reliability of power system since the incorporation of forward converter to the entire system level. Consequently, the power loss of transformer is optimized by the projected Grey Wolf fitness function. The implementation of this work has been done on MATLAB/Simulink. The simulation outcomes of the proposed system show that the forward converter reliability and power loss of transformer are considered as significant aspects while estimating the whole system function. The proposed outcomes are compared with the conventional methods for validating the importance of the projected method.

Journal

Technology and Economics of Smart Grids and Sustainable EnergySpringer Journals

Published: May 16, 2022

Keywords: Forward converter; Machine learning; Optimization; Power loss; Transformer; Wind system

References