Improving the Swelling Phenomenon in the Dynamic Jiles-Atherton Hysteresis Model Using Magnetic Viscosity

Abstract

Iron losses are a major source of inefficiency in electrical devices, such as transformers and rotating machines. Accurately estimating iron losses is essential for optimizing the performance, energy efficiency, and cost-effectiveness of these devices. The swelling phenomenon is a significant factor that affects the accuracy of iron loss estimation. This phenomenon appears in the saturation region of the hysteresis loop under high-frequency regimes and can significantly affect the magnetic properties of the device. This paper presents a novel methodology for addressing the swelling phenomenon and improving the accuracy of iron loss estimation. The methodology is based on the Jiles-Atherton model, which is a well-established model for describing the hysteresis phenomenon in ferromagnetic materials. The methodology is improved by incorporating a novel formulation of the excess field based on magnetic viscosity. The Bertotti approach is used to account for the dynamic effects of the swelling phenomenon. The fmincon algorithm is used to identify the parameters of the Jiles-Atherton model in both quasi-static and dynamic regimes. This algorithm is a MATLAB®-based constrained optimization method that is used to find the set of parameters that minimizes the error between the simulated and measured hysteresis loops. The obtained results show that the proposed methodology is able to accurately estimate iron losses under both quasi-static and dynamic regimes.