Investigation and Modeling of DC Bias Impact on Core Losses at High Frequency

• Published in: IEEE transactions on power electronics Vol. 38; no. 6; pp. 1 - 16
• Main Authors: Sanusi, Bima Nugraha, Zambach, Mathias, Frandsen, Cathrine, Beleggia, Marco, Jorgensen, Anders, Ouyang, Ziwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bias; Circuits; Coercivity; Core loss; core losses; core losses measurement; core losses modeling; DC bias; Electrical resistance measurement; Ferrites; Flux density; High frequencies; high frequency; Hysteresis loops; Loss measurement; Magnetic cores; Magnetic flux; Magnetic hysteresis; Prediction models; Windings
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2023.3249106

This paper aims to study the core losses behavior at high frequency (MHz range) with superimposed DC bias in ferrite. Inductive compensation method is employed in the measurement circuit to reduce sensitivity to phase errors. An addition to the circuit is proposed in this work to make easier DC bias generation. Furthermore, a detailed measurement accuracy consideration is also discussed. The tested MnZn ferrite has a nominal relative permeability of 1500 and 800, which was tested at frequency from 500 kHz to 3 MHz. The measurement results are explained thoroughly with three controlling parameters: excitation frequency, peak AC flux density, and DC bias. There are several important findings. First, a higher DC bias creates higher losses at the same frequency and AC flux density. Secondly, at the same DC bias and AC flux density, higher frequency generates a lower relative losses increase. This second point has not been seen in previous literature and is elaborated more in Section III.B. Thirdly, the measurement result shows how DC bias increases the hysteresis loop area and coercivity. The first and third findings confirm the the existing literature findings. As a final step, an improved Steinmetz Premagnetization Graph and Artificial Neural Network are used to create core loss prediction model. The measurement data and the built model can be accessed online for use by other magnetics designer.