基于参数k计算改进Jiles-Atherton模型参数识别研究

Jiles-Atherton Model Parameters Identification and Research Based on Improvement Calculation of Parameter

Jiles-Atherton模型是研究磁滞回线的重要理论,但在使用过程中存在理论参数难以确定的问题。针对目前公式法需要较精确的初始值以及严格的迭代顺序,而优化算法在计算时计算时间过长且精度较差的问题,根据J-A理论中关于钉扎能的假设以及参数k的定义,改进了k的计算方法,在J-A模型5个参数间建立函数关系,为优化元启发式算法提供了理论依据。用该方法与粒子群算法(PSO)耦合,可直接计算k并进行合理性判别,对不合理的J-A参数值进行拦截从而提高运算精度和效率。对文献报道数据进行拟合计算,结果表明:该耦合粒子群(CPSO)通过对不合理的值拦截,不但大幅度提高计算效率,而且同时忽略了不合理数值计算对适应度值扭曲效应,大幅度提高计算准确度。进而用CPSO算法对硅钢材料的实测磁滞回线进行了拟合,实验结果验证了该计算方法的精确性、有效性。

Jiles-Atherton model is an important theory for studying the hysteresis loop,but there is a problem that the theoretical parameters are difficult to determine in the process of using.Aiming at the problem that the current formula method requires relatively accurate initial values and strict iteration order,and the optimization algorithm takes too long to calculate and has poor accuracy,based on the assumption of the pinning energy of the J-A theory and the definition of the parameterk,a new calculation method for the parameterk was proposed.The function relationship between the five parameters of the J-A model was established,which provided a theoretical basis for optimizing the meta-heuristic algorithm.This parameterk calculation method was coupled with the particle swarm optimization(PSO)to directly calculatek and make rationality judgment,and intercept unreasonable J-A parameter value so as to improve the calculation accuracy and efficiency.The data reported in literature were fitted and calculated.The results show that the coupled particle swarm(CPSO)can not only improve the efficiency of large scale calculation by intercepting unreasonable values,but also ignore the distortion effect of unreasonable numerical calculation on fitness values,and greatly improve the accuracy of calculation.Furthermore,the CPSO algorithm is used to fit the measured hysteresis loop of silicon steel.Experimental results verify the accuracy and effectiveness of the proposed method.