Fixed-Point Harmonic-Balanced Method for DC-Biasing Hysteresis Analysis Using the Neural Network and Consuming Function

Abstract

The magnetic flux includes dc component and ac component in the square laminated core (SLC) under dc-biased magnetization. Hysteresis loops are distorted by dc component of magnetic field intensity in ferromagnetic core and exhibit asymmetrical and special nonlinearities. A neural network (NN) is trained on the basis of the experimental data to model hysteresis effects in the limb-yoke of the SLC. Hysteresis effects in the mitered-joint region are modeled by the consuming function combined with the dc-biasing magnetization curve. The global fixed-point magnetic reluctivity is properly determined in harmonic-balanced finite-element method (HBFEM) to ensure globally convergent computation. The magnetic field in the SLC under dc-biased magnetization is computed by the proposed method taking account of the dc-biasing hysteresis effects.Index Terms-Consuming function, dc-biasing hysteresis loops, fixed-point magnetic reluctivity, neural network.