Conference Agenda

Circuit type simulation packages are routinely used to analyze a large variety of topologies and parameter combinations (duty cycle, frequency, input/output voltages, . . . ) in power electronics systems. The inductors present in the simulated systems usually contain magnetic cores, which are very often made of ferrite materials. As ferrite materials are soft magnetic materials exhibiting a

significant hysteresis behaviour, scientists and engineers need accurate hysteresis models to predict magnetic quantities and losses in those cores. This paper shows how to implement the Energy-Based hysteresis model into the LTspice software, in order to simulate hysteresis phenomena in the magnetic cores of inductance components.

This paper proposes a new iron loss separation algorithm and an artificial neural network (ANN) model with deep learning for iron loss estimation in an electrical steel sheet (ESS). In the method, anomalous magnetic field and loss are extracted from measured hysteresis loops and iron loss by excluding static hysteresis and classical eddy current fields and losses. With the extracted data, an ANN model is developed with the help of deep learning algorithm to predict anomalous magnetic field and loss for arbitrary B-waveforms. The effectiveness of the ANN model is validated through comparisons with experimental measurements and Bertotti’s anomalous formula over non-oriented, highly Grain-oriented and domain-refined highly Grain-oriented ESSs.

This digest introduces a method to account for domain walls in a statistical magneto-mechanical model. After minimizing the domain energy, the volume fraction of a domain is calculated with a Boltzmann distribution by considering the contribution of magnetic walls. Finally, the magnetization and the magnetostriction are computed by weighing the six domain orientations with their respective volume fractions.

It is significant to study the magnetostrictive model since the vibration noise of power transformer mainly comes from the magnetostrictive effect of silicon steel sheet. In this paper, the loss of silicon steel sheet is divided into hysteresis loss Why, eddy current loss Wed and anomalous loss Wan by Statistical Theory of Losses. The Why is calculated by static J-A hysteresis model, Wed and Wan are calculated by analytical equation. Then the expression of dynamic magnetic field intensity is derived by the field separation approach, and the magnetostriction model is obtained by combining the dynamic magnetic field intensity and the quadratic domain rotation model. Finally, the efficiency of model is verified by experiments.

Soft Magnetic Composites (SMC) are an alternative to laminated steels for the design of smaller and lighter electromagnetic devices. Such electromagnetic devices might be subjected to significant mechanical stresses that can alter their electromagnetic properties. This paper presents a homogenization model which provides estimates for both the nonlinear magnetic response and the Eddy Current (EC) Losses of SMC subjected to a stress state.