The switching of the power semiconductors in static converters is the main source of electromagnetic interferences (EMI). To meet with electromagnetic compatibility standards, it is necessary to reduce the level of the conducted emissions. This reduction can be achieved by different techniques including the EMI filters whose design is mainly based on the use of ring core inductors. This element is a key point for designing efficient EMI filters, requiring then accurate inductor high-frequency (HF) models. Therefore, this paper deals with the development of an HF behavioral model of inductors, based on electrical equivalent circuit, for an implementation in circuit simulation software. The aim is to provide a robust and adjustable model under small-signal operating conditions for frequencies up to 100 MHz. The proposed model considers the frequency-dependent properties of the magnetic core material and also includes the parameterization of the electrical equivalent circuit elements with the number of winding turns and dimension of the magnetic core. Simulations results using the obtained inductor model are validated by impedance measurements with two types of magnetic materials: ferrite and nanocrystalline.

The passive magnetic ferrite components used in EMI filters have a complex magnetic permeability (CMP) which varies with frequency. The CMP in frequency range from 150 kHz to 30MHz is not given by manufactures. Moreover, measurement techniques require some specific size and shape of sample to measure CMP of ferrites. Common application of these passives components are EMI filters, where toroid ferrite cores need to be characterized to determine the insertion loss function. In this paper a CMP measure procedure is proposed. The leakage inductance and parasitic capacitance of an inductor are determined. Then, a rational function approximation (RTA) is applied to represent the HF equivalent impedance of CMP.

Purpose – Although the original Jiles-Atherton (J-A) hysteresis model is able to represent a wide range of major hysteresis loops, in particular those of soft magnetic materials, it can produces non-physical minor loops with its classical equations. The purpose of this paper is to show a modification in the J-A hysteresis model in order to improve the minor and inner loops representation. The proposed technique allows the J-A model representing non-centred minor loops with accuracy as well as improving the symmetric inner loops representation. Design/methodology/approach – Only the irreversible magnetization component is slightly modified keeping unchanged the other model equations and the model simplicity. The high-variation rate of the irreversible magnetization, which causes the non-physical behaviour of minor loops, is limited by introducing a new physical parameter linked to the losses. Contrarily to other modifications of the original model found in the literature, the previously knowledge of the magnetic field waveform is not needed in this case. Findings – The modified hysteresis model is validated by comparison with experimental results. A good agreement is observed between calculations and measurements. The modified model retains the low-computational effort and numerical simplicity of the original one. Originality/value – This paper shows that a classical scalar hysteresis model can be suitably used to take into account the minor loops behaviour and be included in a finite element code. The methodology is useful for the design and analysis of electromagnetic devices under distorted flux patterns.

To take account of the uncertainties introduced on the soft magnetic materials properties (magnetic behavior law, iron losses) during the manufacturing process, the present work deals with the stochastic modeling of the magnetic behavior law B-H and iron losses of claw pole stator generator. Twenty-eight samples of slinky stator (SS) coming from the same production chain have been investigated. The used approaches are similar to those used in mechanics. The accuracy of existing anhysteretic models has been tested first using cross validation techniques. The well known iron loss separation model has been implemented to take into account the variability of the losses. Then, the multivariate Gaussian distribution is chosen to model the variability and dependencies between identified parameters, for both behavior law and iron loss models. The developed stochastic models allow predicting a 98% confidence interval for the considered samples.