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Analysis of power loss in forward converter transformer using a novel machine learning-based optimization framework


In wind energy systems, high voltage gain and high power-based forward converters are mainly used for switched-mode power supplies. However, due to the wide range of load usage in grid systems, the reliability and power loss in forward converter-based system performance became crucial. Many earlier researches are conducted to validate the performance of forward converters in renewable resources. But, effective improvement is not achieved for wind applications. Thus, in this paper, the novel grey wolf-based boosting intelligent frame (GWbBIF) control algorithm is proposed in forward converter switching controls. The gain of the controller and duty cycle of the converter is tuned by the proposed control approach. Consequently, the power loss from the wind transformer is optimized by the proposed grey wolf fitness function. The implementation of this research has been done on the MATLAB/Simulink platform. The simulation outcomes of the proposed system are compared with various conventional techniques in terms of total harmonic distortion (THD), power loss, stability, error, driving circuit, etc. While compared with the other methods, the proposed methods effectively show the optimal performance of forward converter in wind system by reduced power loss and improved reliability that is considered as the significant aspects while estimating the entire system.

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Correspondence to Pavankumar R. Patil.

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Patil, P.R., Tanavade, S. & Dinesh, M.N. Analysis of power loss in forward converter transformer using a novel machine learning-based optimization framework. Soft Comput 27, 3733–3749 (2023).

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  • Forward converter
  • Machine learning
  • Optimization
  • Power loss
  • Transformer
  • Wind system