An empirical investigation on the static Jiles-Atherton model identification by using different set of measurements

Since the first presentation of the static Jiles-Atherton (JA) model, several authors discussed about its identification process and many of them have emphasized that the JA model performs poorly on waveforms different from the one used for its identification. As a consequence, this problem is usually approached by modifying the original model, or by introducing new parameters, or making the parameters dependent on the field intensity, using innovative optimization algorithms. The aim of this paper is to empirically demonstrate that a relationship between the found parameters and the identification process exists. Indeed, we prove that more than one numerical solution can reproduce the same saturated hysteresis loop under a quite low threshold error, and a wrong choice of data utilized for finding the parameters drastically affects the final results. Different shapes of hysteresis loops have been used as identification patterns and the generalization capabilities of the model has been inspected on different distorted excitation waveforms used for validation. The obtained results confirm that the simulation capabilities of the static JA model improve if a suitable choice of data is made during the identification process.