基于改进粒子群算法的电流互感器J-A模型参数辨识

在应用Jiles-Atherton(J-A)磁滞模型对电流互感器的磁滞回线进行分析时,需对J-A磁滞模型中5个关键参数进行精确识辨。针对目前辨识方法存在的计算时间长和寻优能力差等问题,提出了一种改进的粒子群算法对J-A磁滞模型中的关键参数进行辨识。该算法将遗传选择策略引入到粒子群算法中,通过增加粒子群的多样性来提高了算法全局搜索能力,从而提高J-A磁滞模型关键参数辨识的准确度。文中对比分析了所提改进算法(GSS-PSO)与其他智能算法对J-A磁滞模型的关键参数辨识速度与准确度。结果表明,改进的算法得到的磁滞回线与实测磁滞回线的误差最小,且识别效率较高,证明了该算法在J-A磁滞模型参数辨识中的准确性和有效性。

The Identification Method of J-A Hysteresis Model ParameterBased on Improved Algorithm(gss-pso)

It is necessary to accurately identify the five key parameters in the Jiles-Atherton(J-A)hysteresis model when it is applied to analyze the hysteresis loop of current transformer.An improved particle swarm optimization algorithm(PSO)is proposed to identify the key parameters in the J-A hysteresis model to solve the problems of computation time-consuming and poor optimization ability existing in the current identification methods.The genetic selection strategy is introduced into the PSO algorithm to improve the global search ability of the algorithm by increasing the diversity of the PSO,so as to improve the accuracy of key parameter identification of the J-A hysteresis model.In this paper,the identification speed and accuracy of the proposed improved algorithm(GSS-PSO)are compared with other intelligent algorithms in identifying the key parameters of J-A hysteresis model.The results show that the error between the hysteresis loops obtained by the improved algorithm and the measured hysteresis loops is the minimum,and the identification efficiency is high,which proves the accuracy and effectiveness of the proposed improved algorithm in the parameter identification of J-A hysteresis model.