Parameter estimation of extended Jiles–Atherton hysteresis model based on ISFLA

Abstract

This study introduces an extended Jiles–Atherton (JA) hysteresis model, which considers dynamic loss and anisotropy. The two considerations facilitate an accurate non-linearity representation of electromagnetic devices, thereby resulting in a precise agreement between the simulated and measured hysteresis loops. To achieve this goal, JA hysteresis model parameters must be estimated by an optimisation algorithm. Therefore, an improved shuffled frog-leaping algorithm (ISFLA) is proposed in this study. Differential evolution (DE) mutation operator, adaptive step size factor, and inertia weight factor are considered. Then, the implementation of the ISFLA is discussed using MATLAB. The proposed ISFLA is verified by the measured hysteresis loops of grain-oriented silicon toroidal core. The comparison between genetic algorithm, simulated annealing, DE, and ISFLA is discussed. Results show that the proposed ISFLA demonstrates better global optimum ability, lower computational burden, and faster convergence rate than the other three methods.