Multiple Resonance Prediction through Lumped-Parameter Modeling of Transformers in High Frequency Applications

peer reviewedThe accurate prediction of coupled inductive and capacitive effects in electromagnetic coils is of crucial importance in many industrial applications, due to the increase of operating frequency or to the increase of voltage levels. In this paper, we propose a light-weight coupled electric circuit model that avoids solving a large 3D Maxwell full-wave problem, and is still able to predict not only the first resonance but also the next few resonances as accurately as experimental characterizations would do. The resistive, inductive and capacitive lumped circuit parameters of the magnetic device are identified by means of 2D finite element modelling, and then implemented in an electric circuit that realises the inductive-capacitive coupling. Moreover, the lumped parameter identification can be performed at different levels of representation of the electromagnetic coils, from turn-level to winding-level, in order to resolve additional resonances of the system, still keeping the computational complexity compatible with industrial design requirements. The efficiency of the method is confirmed by means of simulations and measurements performed on a high frequency transformer and on an air inductance

Hybrid power solution modelling based on artificial intelligence

peer reviewedPower electronics become increasingly resourceful as the use of renewable energies increases. Microgrids and active distribution networks include various controllable devices that interact and may create instabilities. This underlines the necessity of modeling complex systems to conduct system-level analyses. As a first step toward tools for modeling inverter-based electrical systems, this paper introduces a model of the HyPoSol system, put in perspective with measurements on the real system. The HyPoSol system consists of a photovoltaic (PV) inverter, a battery, and a three-port converter designed by CE+T Power. To develop a model of the PV inverter, we employed an enhanced polytopic model which uses neural networks as weighting functions. The PV inverter model is combined with a Tremblay’s battery model and a simplified model of the three-port converter. We conduct system-level analyses on the overall representation of the HyPoSol system and compare the results with measurements