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 \(\upmu \)H 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.
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Di Capua, G., Molinara, M., Fontanella, F., De Stefano, C., Oliva, N., Femia, N. (2023). Modeling of Ferrite Inductors Power Loss Based on Genetic Programming and Neural Networks. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_20
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