基于蛙跳模糊算法的Jiles Atherton铁心磁滞模型参数确定

Jiles Atherton(JA)模型广泛应用于铁心磁滞建模领域,其参数确定精确与否直接影响铁心磁滞现象的表达,为此提出蛙跳模糊算法对JA模型参数进行辨识。为了防止蛙跳算法陷入局部最优解并加快收敛速度,根据特殊点(矫顽力、矫顽力点磁化率)处实测值与计算值的相对误差,采用模糊控制方法,得到动态反馈系数,修正蛙跳算法的步长调整公式。采用爱泼斯坦方圈搭建磁滞回线测量系统,测得铁心饱和磁滞回线与局部磁滞回线。采用蛙跳模糊算法、蛙跳算法、粒子群算法和遗传算法分别求解得到模型参数,并将计算的磁滞回线与实测磁滞回线进行比较,证明了所提出的蛙跳模糊算法不易陷入局部最优解且具有更快的收敛速度。根据蛙跳模糊算法得到的局部磁滞回线模型参数辨识结果,研究模型参数与磁通密度的关系并进行拟合,得到不同磁通密度下的局部磁滞回线参数拟合模型。

Determination of Jiles Atherton Core Hysteresis Model Parameters Based on Fuzzy-Shuffled Frog Leaping Algorithm

Jiles Atherton (JA) hysteresis model is widely used in core hysteresis modeling. Its parameters determination affects the expression of core hysteresis phenomenon. A fuzzy-shuffled frog leaping algorithm (F-SFLA) is proposed in this paper to identify the parameters of JA hysteresis model. In order to speed up the convergence and avoid falling into local optimal solution of shuffled frog leaping algorithm (SFLA), a dynamical feedback factorF, which is the output of the fuzzy logic, is added to the step length of SFLA. The input of the fuzzy logic is the relative error of the measured and the calculated value at the special points (the coercive field amplitude, the dM/dH on the coercive field amplitude). F-SFLA, SFLA, particle swarm optimization algorithm (PSOA) and genetic algorithm (GA) are applied to determine the parameters of major hysteresis loop and minor hysteresis loop. Compared with hysteresis loops measured by Epstein frame measuring system, the results show that F-SFLA has less computational time and is not easy to fall into local optimal solution. Additionally, the relationship between the parameters of minor loop and magnetic flux density is analyzed, and the parameters of minor loop varying with magnetic flux density are fitted.