Parameter Identification of J-A Model Based on Improved Algorithm (GSS-PSO)

When the Jiles-Atherton hysteresis model is applied to analyze the B-H curve of current transformer, it is necessary to accurately identify the five key arguments in the Jiles-Atherton model. Since the existing identification methods have the problems of long calculation time and poor optimization ability, an improved PSO algorithm is proposed to recognize the key parameters of Jiles-Atherton model. With the genetic selection strategy is recommended into the PSO algorithm, the global search ability of the algorithm is improved by increasing the diversity of the PSO, so as to improve the accuracy of the authentication of the key parameters of Jiles-Atherton model. In this paper, the calculation speed and accuracy of the proposed improved algorithm (gss-pso) are compared with other intelligent algorithms in identifying the key parameters of Jiles-Atherton model. The sequelae show that the error of the B-H curve calculated by the improved algorithm is the least, and the identification efficiency is higher, which proves the accuracy and efficacy of the algorithm in parameter authentication of Jiles-Atherton hysteresis model.