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Thesis
Home   /   Thesis   /   Modeling and prediction of electromagnetic emissions from power converters using deep learning

Modeling and prediction of electromagnetic emissions from power converters using deep learning

Electromagnetism - Electrical engineering Engineering sciences Numerical simulation Technological challenges

Abstract

In recent years, electromagnetic compatibility (EMC) in power converters based on wide bandgap (WBG) semiconductors has attracted growing interest, due to the high switching speeds and increased frequencies they enable. While these devices improve power density and system efficiency, they also generate more complex conducted and radiated emissions that are challenging to control. In this context, this thesis focuses on the prediction, modeling, and characterization of electromagnetic interference (EMI) (> 30 MHz), both conducted and radiated, in high-frequency power electronic systems. The work is based on a multi-subsystem partitioning method and an iterative co-simulation approach, combined with in situ characterization to capture non-ideal and nonlinear phenomena. In addition, deep learning techniques are employed to model EMI behavior using both measured and simulated data. Generative artificial intelligence (Generative AI) is also leveraged to automatically generate representative and diverse configurations commonly encountered in power electronics, thereby enabling efficient exploration of a wide range of EMI scenarios. This hybrid approach aims to enhance analysis accuracy while accelerating simulation and design phases.

Laboratory

Département de l’Electricité et de l’Hydrogène pour les Transports (LITEN)
Service Transverses pour Technologies de Conversion électrochimique et électrique
Laboratoire Electronique Enérgie et Puissance
Université Grenoble Alpes
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