Step-down Fibonacci switched capacitor converters- a topological design method

Gupta, Arvind Kumar; Raizada, Shirish

doi:10.1109/indicon49873.2020.9342146

2020 IEEE 17th India Council International Conference (INDICON) 2020

DOI: 10.1109/indicon49873.2020.9342146

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Step-down Fibonacci switched capacitor converters- a topological design method

Arvind Kumar Gupta¹,

Shirish Raizada

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2022

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Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks

et al. 2022

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Switched-capacitor converters (SCCs) have been developed for decades and applied successfully in many applications thanks to their merits of high power density, high efficiency, and low cost. Existing SCC evaluation methods do not provide an efficient and effective way to estimate their performance. This paper proposes a new evaluation performance method for SCCs that transforms the topology optimization issue into a more typical non-linear regression problem. The SCC topologies are described by the adjacent matrix. A CNN model is then selected to solve the regression problem. Experimental results confirm that this CNN model greatly shortens the calculation time while performing better than other machine learning algorithms.

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Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks

et al. 2022

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Step-down Fibonacci switched capacitor converters- a topological design method

Cited by 1 publication

References 13 publications

Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks

Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks

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