基于PSO-Powell混合算法的软磁复合材料二维矢量磁滞特性模拟

Two-Dimensional Vector Hysteresis Simulation of Soft Magnetic Composite Materials Based on the Hybrid Algorithm of PSO-Powell

针对经典矢量Preisach模型仅能模拟各向同性磁滞特性的缺陷,提出改进的矢量磁滞模型。通过引入幅值与方向角的关联参数改善磁场强度的曲线形态,使模型能够考虑软磁材料的各向异性特征。利用矢量磁特性实验平台对软磁复合材料(SMC)在二维旋转磁化激励下的磁特性进行测量,根据测量的极限磁滞回线,分别构造标量和矢量Everett函数。提出基于粒子群(PSO)和Powell方向加速的组合优化策略,显著改善PSO的局部寻优能力,提高计算效率和精度,进而实现矢量Preisach磁滞模型的高效参数辨识。矢量磁滞特性的仿真结果与测量结果相吻合,验证了该文所提改进矢量磁滞模型及其参数辨识方法的有效性。

An improved vector hysteresis model was proposed because the classical vector Preisach model can only simulate the isotropic hysteresis characteristics of materials.The improved model was utilized to regulate the curve shape of magnetic field strength by introducing the correlation parameters of its amplitude and direction angle.Accordingly,the anisotropy characteristics of soft magnetic composite(SMC)materials can be considered.The experiment for measuring the two-dimensional rotational magnetic properties of the SMC material was carried out by the vector magnetic property platform.The measured limiting hysteresis loops were required to construct the scalar and vector Everett functions.Moreover,a hybrid optimization strategy based on particle swarm optimization(PSO)and Powell technique was proposed to identify the parameters of the vector Preisach hysteresis model efficiently,which significantly improved the local optimization ability of the PSO algorithm.The simulation results are consistent with the measurement results,which verifies the effectiveness of the improved vector model and the parameter identification method proposed in this paper.