Modeling of Ferrite Inductors Power Loss Based on Genetic Programming and Neural Networks
Authors: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, C. De Stefano, F. Fontanella, L. Vanneschi, Giulia Di Capua, Mario Molinara, Francesco Fontanella, Claudio De Stefano, Nunzio Oliva, Nicola Femia
Abstract
This work compares two behavioral modeling approaches for predicting AC power loss in Ferrite-Core Power Inductors (FCPIs), normally used in Switch-Mode Power Supply (SMPS) applications. The first modeling approach relies on a genetic programming algorithm and a multi-objective optimization technique. The resulting AC power loss model uses the voltage and switching frequency imposed on the FCPI as input variables, whereas the DC inductor current is used as a parameter expressing the impact of saturation on the magnetic device. A second modeling approach involves a Multi-Layer Perceptron, with a single hidden layer. The resulting AC power loss model uses the voltage, switching frequency and DC inductor current as input variables. As a case study, a 10 μH FCPI has been selected and characterized by a large set of power loss experimental measurements, which have been adopted to obtain the training and test data. The experimental results confirmed the higher flexibility of the FCPI behavioral modeling based on genetic programming.
BibTeX
@article{communicationsincomputerandinformationscience2023modeling,
title={Modeling of Ferrite Inductors Power Loss Based on Genetic Programming and Neural Networks},
author={COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE and C. De Stefano and F. Fontanella and L. Vanneschi and Giulia Di Capua and Mario Molinara and Francesco Fontanella and Claudio De Stefano and Nunzio Oliva and Nicola Femia},
year={2023},
doi={10.1007/978-3-031-31183-3_20}
}