全局优化算法在Preisach磁滞模型参数辨识问题中的应用与性能对比

doi:10.19595/j.cnki.1000-6753.tces.201294

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (2677 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要 Preisach模型参数的快速辨识对实现考虑磁滞特性的电工装备有限元计算具有重要意义。该文结合显式Everett函数Preisach磁滞模型,提出一种基于改进速度可控粒子群算法的Preisach模型参数辨识方法,并对比分析全局优化算法在该问题应用的效率问题。首先,为了解决传统离散型Preisach模型因存储庞大Everett矩阵造成的计算效率低下问题,构建Everett函数的参数化显式表达式;其次,提出一种基于改进速度可控粒子群算法的Preisach模型参数辨识方法,并基于测量的硅钢片准静态磁滞回线,实现模型的参数辨识;最后,对比分析模拟退火算法、遗传算法与该文所提算法在模型迭代次数与计算时间、磁滞回线模拟准确度、参数辨识成功率三个方面的应用效率。结果表明,该文所提出的改进速度可控粒子群算法在Preisach模型辨识上同时兼具辨识精度高、收敛速度快、成功率高的特点。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈龙
	易琼洋
	贲彤
	张泽宇
	汪友华

关键词 ：磁滞模型, 全局优化, 电工磁材料, 磁特性模拟

Abstract：The rapid identification of the Preisach model parameters is of great significance to realize the finite element calculation of electrical equipment considering the hysteresis characteristics. Combined the explicit Everett function with the Preisach model, this paper proposes a parameter identification method based on an improved velocity-controlled particle swarm optimization algorithm, and compares the efficiency of the global optimization algorithm. Firstly, a parameterized explicit expression of the Everett function is constructed to solve the problem that low computational efficiency of the traditional discrete Preisach model as the storage of Everett matrix is huge. Secondly, a parameter identification method of the Preisach model based on the improved velocity-controlled particle swarm optimization algorithm is proposed. Based on the measured quasi-static hysteresis loops of silicon steel sheet, the parameters of the Preisach model are identified. Finally, the simulated annealing algorithm, genetic algorithm, and the algorithm proposed in this paper are compared and analyzed in terms of the iterations number and calculation time, the accuracy of hysteresis loop simulation, and the success rate of Parameter identification. The results show that the improved velocity-controlled particle swarm optimization algorithm proposed in this paper has high identification accuracy, fast convergence speed, and high success rate in identifying the Preisach model.

Key words： Hysteresis model global optimization electrical magnetic material magnetic properties simulation

收稿日期: 2020-09-23

PACS:

TM271

基金资助:国家自然科学基金项目（52007102）、省部共建电工装备可靠性与智能化国家重点实验室（河北工业大学）开放课题（EERIKF2019009）、湖北省自然科学基金项目（2020CFB212）、宜昌市应用基础研究项目（A19-302-03）和湖北省输电线路工程技术研究中心（三峡大学）开放基金课题（2019KXL10）资助

通讯作者: 贲彤, 女,1991年生,博士,讲师,硕士生导师,研究方向为磁性材料磁特性、电磁场数值分析。E-mail: bentong@ctgu.edu.cn

作者简介: 陈龙,男,1989年生,博士,讲师,硕士生导师,研究方向为磁性材料磁特性模拟、全局优化设计。E-mail:chenlong@ctgu.edu.cn

引用本文:

陈龙, 易琼洋, 贲彤, 张泽宇, 汪友华. 全局优化算法在Preisach磁滞模型参数辨识问题中的应用与性能对比[J]. 电工技术学报, 2021, 36(12): 2585-2593. Chen Long, Yi Qiongyang, Ben Tong, Zhang Zeyu, Wang Youhua. Application and Performance Comparison of Global Optimization Algorithms in the Parameter Identification Problems of the Preisach Hysteresis Model. Transactions of China Electrotechnical Society, 2021, 36(12): 2585-2593.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.201294 https://dgjsxb.ces-transaction.com/CN/Y2021/V36/I12/2585

[1] Bertotti G.Generalized Preisach model for thedescrip- tion of hysteresis and eddy current effects inmetallic ferromagnetic materials[J]. Journal of Applied Physics, 1991, 69(8): 4608-4610.
[2] 赵志刚, 马习纹, 姬俊安, 等. 铁心动态Energetic建模与验证[J]. 电工技术学报, 2020, 35(20): 4241-4250.
Zhao Zhigang, Ma Xiwen, Ji Jun'an, et al.Dynamic Energetic modeling and verification of core under harmonic excitation[J] Transactions of China Elec- trotechnical Society, 2020, 35(20): 4241-4250.
[3] 李超, 徐启峰. 随温度变化的Preisach磁滞模型建
模方法[J]. 电工技术学报, 2013, 28(12): 90-94.
4 Li Chao, Xu Qifeng.Modeling of temperature dependent hysteresis based on Preisach theory[J]. Transactions of China Electrotechnical Society, 2013, 28(12): 90-94.
[4] 刘任, 李琳, 王亚琦, 等. 基于随机性与确定性混合优化算法的Jiles-Atherton磁滞模型参数提取[J]. 电工技术学报, 2019, 34(11): 2260-2268.
Liu Ren, Li Lin, Wang Yaqi, et al.Parameter extraction for Jiles-Atherton hysteresis model based on the hybrid technique of stochastic and deter- ministic optimization algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2260-2268.
[5] 贲彤, 陈龙, 闫荣格, 等. 考虑磁化及磁致伸缩特性各向异性的感应电机铁心电磁应力分析[J]. 电工技术学报, 2019, 34(1): 66-74.
Ben Tong, Chen Long, Yan Rongge, et al.Stress analysis of induction motor core considering aniso- tropic magnetic and magnetostrictive properties[J]. Transactions of China Electrotechnical Society, 2019, 34(1): 66-74.
[6] Mayergoyz I.Mathematical model of hysteresis and their applications[M]. New York: Academic Press, 2003.
[7] Finocchio G, Carpentieri M, Cardelli E.Analytical solution of Everett integral using Lorentzian Preisach function approximation[J]. Journal of Magnetism and Magnetic Materials, 2006, 300(2): 451-470.
[8] Ferenc V, Edward D T.Minor loops in magnetization dependent Preisach models[J]. IEEE Transactions on Magnetics, 1992, 28(2): 1245-1248.
[9] Hussain S, Lowther D A.An efficient implementation of the classical Preisach model[C]//21st Conference on the Computation of Electromagnetic Fields, Daejeon, 2017:1-6.
[10] Zhao Xiaojun, Guan Dawei, Zhang Honggang, et al.Simulation of Preisach hysteresis model based on the first-order reversal curves[J]. International Journal of Applied Electromagnetics & Mechanics, 2017, 55(1-4): 1-9.
[11] 赵小军, 刘小娜, 肖帆, 等. 基于Preisach模型的取向硅钢片直流偏磁磁滞及损耗特性模拟[J]. 电工技术学报, 2020, 35(9): 1849-1857.
Zhao Xiaojun, Liu Xiaona, Xiao Fan, et al.Hysteretic and loss modeling of silicon steel sheet under the DC biased magnetization based on the Preisach model[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 1849-1857.
[12] 段娜娜, 徐伟杰, 李永建, 等. 基于极限磁滞回线法的软磁复合材料磁特性模拟[J]. 电工技术学报, 2018, 33(20): 4739-4745.
Duan Nana, Xu Weijie, Li Yongjian, et al.Electro- magnetic property modeling of the soft magnetic composite material based on the limiting loop method[J]. Transactions of China Electrotechnical Society, 2018, 33(20): 4739-4745.
[13] 段娜娜, 徐伟杰, 李永建, 等. 一种考虑温度和压力影响的磁滞模型及其实验验证[J]. 电工技术学报, 2019, 34(13): 2686-2692.
Duan Nana, Xu Weijie, Li Yongjian, et al.A temperature and stress dependent hysteresis model with experiment validation[J]. Transactions of China Electrotechnical Society, 2019, 34(13): 2686-2692.
[14] Duan Nana, Xu Weijie, Wang Shuhong, et al.Hysteresis modeling of high-temperature super- conductor using simplified Preisach model[J]. IEEE Transactions on Magnetics, 2015, 51(3): 1-4.
[15] 张长庚, 杨庆新, 李永建. 电工软磁材料旋转磁滞损耗测量及建模[J]. 电工技术学报, 2017, 32(11): 208-216.
Zhang Changgeng, Yang Qingxin, Li Yongjian.Measurement and modeling of rotational hysteresis loss of electric soft magnetic material[J]. Transactions of China Electrotechnical Society, 2017, 32(11): 208-216.
[16] Zsolt Szabó, János Füzi.Implementation and identi- fication of Preisach type hysteresis models with everett function in closed form[J]. Journal of Mag- netism & Magnetic Materials, 2016, 406: 251-258.
[17] Zsolt Szabó, Tugyi I, Gy Kádár, et al.Identification procedures for scalar Preisach model[J]. Physica B: Physics of Condensed Matter, 2004, 343(1/4): 142-147.
[18] Zsolt Szabó.Preisach functions leading to closed form permeability[J]. Physica B: Condensed Matter, 2006, 372(1-2): 61-67.
[19] Stoleriu L, Andrei P, Stancu A. First order reversal curves identification procedures for vector models of hysteresis[J]. Journal of Applied Physics, 2008, 103(7): 07D923-07D926.
[20] Sjostrom M.Frequency analysis of classical Preisach model[J]. IEEE Transactions on Magnetics, 1999, 35(4): 2097-2103.
[21] Hergli K, Marouani H, Zidi M.Identification of Preisach hysteresis model parameters using genetic algorithms[J]. Journal of King Saud University Science, 2019, 31(4): 746-752.