基于收缩因子改进PSO算法的J-A磁滞模型参数辨识*

doi:10.11985/2024.01.020

摘要/Abstract

摘要：

变压器铁心磁滞特性的准确预测及其模型参数可靠辨识，一直是国内外学者们研究的难点问题。针对现有主流磁化(Jile-Atherton，J-A)模型存在的辨识参量多、计算时间长、容易陷入局部最优解等问题，提出一种基于收缩因子改进粒子群优化(Particle swarm optimization，PSO)算法的J-A磁滞模型参数辨识方法。研究建立以磁感应强度为输入变量的J-A静态磁滞逆模型，提出考虑包含涡流损耗、异常损耗因素下的动态磁滞模型；针对传统PSO算法计算精度低、不易于快速寻优的问题，提出基于收缩因子改进的PSO优化算法，可实现J-A磁滞模型关键参量的快速辨识。所提算法克服了传统粒子飞行速度的限制，兼具全局寻优和局部寻优的特点，易于实现J-A磁致模型的快速参数辨识。通过仿真算例分析，验证了所提改进PSO算法在不同磁密峰值工况下的应用可靠性，且迭代收敛速度和精度均优于传统PSO算法。

关键词: Jile-Atherton模型, 参数辨识, PSO算法, 磁滞回线

Abstract:

The accurate prediction of transformer core hysteresis characteristics and the reliable identification of its model parameters have been a difficult problem for scholars at home and abroad. In view of the existing mainstream magnetization Jile-Atherton(J-A) models, there are many identification parameters, long calculation time, and easy to fall into the local optimal solution. A J-A hysteresis model parameter identification method based on constriction factor improved particle swarm optimization(PSO) algorithm is proposed. The J-A static hysteresis inverse model with magnetic induction intensity as the input variable was established, and a dynamic hysteresis model considering the inclusion of eddy current loss and anomalous loss factors is proposed. An improved PSO optimization algorithm based on the constriction factor is proposed, which can realize the fast identification of key parameters of the J-A hysteresis model, in order to address the problems of low computational accuracy and not easy to find the optimization quickly. The limitation of traditional particle is overcome by the algorithm, which has the characteristics of both global and local optimization, and is easy to realize the fast parameter identification of J-A magnetism model. The reliability of the proposed improved PSO algorithm for different magnetic density peak operating conditions is verified through simulation case analysis, and the iterative convergence speed and accuracy are better than those of the conventional PSO algorithm.

Key words: Jile-Atherton model, parameter identification, particle swarm optimization algorithm, hysteresis loop

中图分类号:

TM43

林希, 张浩民, 刘振祥. 基于收缩因子改进PSO算法的J-A磁滞模型参数辨识^*[J]. 电气工程学报, 2024, 19(1): 187-195.

LIN Xi, ZHANG Haomin, LIU Zhenxiang. Parameter Identification of J-A Dynamic Hysteresis Model Based on an Improved Constriction Factor PSO Algorithm[J]. Journal of Electrical Engineering, 2024, 19(1): 187-195.

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参数	实测值	PSO	IwPSO	CFPSO
M_s/(A/m)	1 397 000	1 394 192	1 402 317	1 397 061
α/(×10^-5)	5.00	4.81	5.49	4.93
a/(A/m)	30	29	31.62	29.95
k/(A/m)	30	37.29	30.11	27.85
c	0.5	0.63	0.49	0.46

迭代数	误差
迭代数	经典PSO	IwPSO	CFPSO
5	12.33	11.51	15.47
10	8.95	7.79	7.63
25	4.06	7.25	1.43
50	1.85	2.56	0.63
100	1.54	0.62	0.31

参数	实测值(文献[14])	CFPSO
M_s/(A/m)	1 696 100	1 657 974
α/(×10^-6)	7.547 9	7.500 1
a/(A/m)	3.005 2	2.955 1
k/(A/m)	13.071 8	10.763 5
c	0.317 4	0.347 5
k_e	0.012 4	0.015 4
k_a	0.283 2	0.284 3