Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter

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Title
Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter
Author(s)
Date
2024
Citation
Luis-Alfonso Fernandez-Serantes, José-Luis Casteleiro-Roca, Hubert Berger, Dragan Simić, José-Luis Calvo-Rolle, Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter, Logic Journal of the IGPL, 2024; jzae036, https://doi.org/10.1093/jigpal/jzae036
Abstract
[Abstract] A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification.
Keywords
Hard-switching
Soft-switching
Synchronized rectifier
Step-down converter
Power electronics
Classification
Dimensional reduction
 
Description
Funding for open access charge: Universidade da Coruña/CISUG.
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Rights
Creative Commons Attribution License CC BY 4.0 http://creativecommons.org/licenses/by/4.0/
ISSN
1368-9894