基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证

doi:10.3969/j.issn.1000-1158.2021.09.12

摘要
图/表
参考文献(16)
相关文章 (15)

全文: PDF (2768 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了解决现有的GMM-FBG电流传感器的磁滞非线性问题,基于经典的J-A磁滞模型提出了一种改进的适用于低频(<120Hz)条件下的J-A模型。采用粒子群(PSO)算法对改进后的J-A模型进行了分段参数辨识与优化,提高了模型的预测精度。搭建了相应的GMM-FBG交流电流传感系统实验平台,运用所提出的改进的J-A模型对GMM-FBG电流传感器进行了磁滞建模和实验验证。实验及仿真结果证实该模型具有良好的预测性,模型的预测误差在2.5%以内,传感系统的电流测量灵敏度达到0.067nm/A。

	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
	滕峰成
	王珊珊
	杨雪璠
	吕登岩

关键词 ：计量学, GMM-FBG电流传感器, J-A磁滞模型, PSO算法, 参数辨识

Abstract：In order to solve the hysteresis nonlinearity of GMM-FBG current sensor, an improved J-A model for low frequency (<120Hz) based on the classical J-A hysteresis model was proposed. The improved J-A model was identified and optimized by using particle swarm optimization (PSO) algorithm segmentedly, which improved the prediction accuracy of the model. The corresponding experimental platform of GMM-FBG AC current sensing system was built. The improved J-A model was used to conduct hysteresis modeling and experiment verification of GMM-FBG current sensor. The experimental and simulation results confirm that the model has good predictability. The prediction error of the model is less than 2.5%, and the current measurement sensitivity of the sensing system reaches 0.067nm/A.

Key words： metrology GMM-FBG current sensor J-A hysteresis model PSO algorithm parameter identification

收稿日期: 2019-06-20 发布日期: 2021-09-24

PACS:

TB971

作者简介: 滕峰成(1966-),男,河北沧州人,燕山大学副教授,从事光电传感技术研究工作。Email: tengfengcheng@163.com

引用本文:

滕峰成,王珊珊,杨雪璠,吕登岩. 基于PSO算法的GMM改进J-A磁滞模型的参数辨识与验证[J]. 计量学报, 2021, 42(9): 1193-1199.
TENG Feng-cheng,WANG Shan-shan,YANG Xue-fan,L Deng-yan. Parameter Identification and Verification of Improved J-A Hysteresis Model of GMM Based on PSO Algorithm. Acta Metrologica Sinica, 2021, 42(9): 1193-1199.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2021.09.12 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2021/V42/I9/1193

［1］辛耀中, 石俊杰, 周京阳, 等. 智能电网调度控制系统现状与技术展望［J］. 电力系统自动化, 2015, 39(1): 2-8.
Xin Y Z, Shi J J, Zhou J Y, et al. Technology Development Trends of Smart Grid Dispatching and Control Systems ［J］. Automation of Electric Power Systems, 2015, 39(1): 2-8.
［2］蔡川, 张俊超, 熊利民, 等. 光谱失配对太阳电池短路电流测量准确性影响分析［J］. 计量学报, 2018, 39(4): 490-492.
Cai C, Zhang J C, Xiong L M, et al. Analysis of spectral mismatch error influences on measurement of reference solar cell. Acta Metrologica Sinica,2018,39(4):490-492.
［3］葛津铭, 陈彤, 林丞, 等. 基于超磁致伸缩材料的电流互感器涡流损耗模型与分析［J］. 磁性材料及器件, 2018, 49(6): 5-9.
Ge J M, Chen T, Lin C, et al. Analysis on eddy current loss of current transformer based on giant magnetostrictive material ［J］. Journal of Magnetic Materials and Devices, 2018, 49(6): 5-9.
［4］Jiang S X. Research on giant magnetostrictive material controller mode［C］//International Conference on Electronics,Communications and Control. Ningbo, China, 2011.
［5］张玉燕, 任天昕, 温银堂, 等. 复合材料胶层光纤布格光栅应变监测优化配置研究［J］. 计量学报, 2018, 39(2): 168-172.
Zhang Y Y, Ren T X, Wen Y T, et al. Strain Measurement and Optimal Placement of Fiber Bragg Sensors in Adhesive Layer of Composites. Acta Metrologica Sinica, 2018, 39(2): 168-172.
［6］冒鹏飞, 王传礼, 喻曹丰, 等. 磁滞伸缩驱动器磁滞特性的Persiach模型建模［J］. 科学技术与工程, 2017, 17(9): 149-152.
Mao P F, Wang C L, Yu C F, et al. Establish the Preisach Model of Hysteresis in Giant Magnetostrictive Material ［J］. Science Technology and Engineering, 2017, 17(9): 149-152.
［7］Zhang B, Gupta B, Ducharne B, et al. Dynamic Magnetic Scalar Hysteresis Lump Model Based on Jiles-Atherton Quasi-Static Hysteresis Model Extended With Dynamic Fractional Derivative Contribution［J］. IEEE Transactions on Magnetics, 2018, 54(11):7301605.
［8］董张卓, 伍弘, 尚腾, 等. 铁磁元件J-A磁滞模型参数辨识［J］. 电气应用, 2017, 36(9): 22-28.
Dong Z Z, Wu H, Shang T, et al. Parameter Identification of J-A Hysteresis Model for Ferromagnetic Components ［J］. Electrotechnical Application, 2017, 36(9): 22-28.
［9］雷阳, 段建东, 张小庆, 等. 电流互感器J-A模型参数辨识及大通流动模试验［J］. 中国电机工程学报, 2016, 36(S1): 240-245.
Lei Y, Duan J D, Zhang X Q, et al. Identification of Current Transformer J-A Model Parameters With Large Current Dynamic Simulation Experiments ［J］. Proceedings of the CSEE, 2016, 36(S1): 240-245.
［10］赵越, 李琳, 刘任. 基于人工鱼群与Levenberg-Marquardt混合算法的Jiles-Atherton磁滞模型参数提取［J］. 华北电力大学学报(自然科学版), 2018, 45(6): 21-27.
Zhao Y, Li L, Liu R. Artificial Fish Swarm and Levenberg-Marquardt Hybrid Algorithm-based Parameter Extraction of Jiles-Atherton Hysteresis Model ［J］. Journal of North China Electric Power University (Natural Science Edition), 2018, 45(6): 21-27.
［11］何汉林. GMM动态迟滞特性建模及其参数辨识［D］. 杭州: 杭州电子科技大学, 2012.
［12］孙乐. 超磁致伸缩材料的本构理论研究［D］. 兰州: 兰州大学, 2007.
［13］滕峰成, 郝宇, 林晓乐. 基于PSO算法的MFF模型的参数辨识与优化［J］. 计量学报, 2017, 38(2):209-214.
Teng F C, Hao Y, Lin X L. Parameter Identification and Optimization of the MFF Model Based on the Particle Swarm Optimization Algorithm ［J］. Acta Metrologica Sinica, 2017, 38(2): 209-214.
［14］刘浩然,崔静闯,卢泽丹,等. 一种改进的雁群扩展粒子群算法研究［J］. 计量学报, 2019, 40(3): 498-504.
Liu H R,Cui J C,Lu Z D, et al. Study on an Improved Extended PSO Algorithm Based on Geese Flight［J］. Acta Metrologica Sinica, 2019, 40(3): 498-504.
［15］刘彬,刘泽仁,赵志彪,等. 基于速度交流的多种群多目标粒子群算法研究［J］.计量学报, 2020, 41(8): 1002-1011.
Liu B,Liu Z R,Zhao Z B, et al. Research on Multi-population Multi-objective Particle Swarm Optimization Algorithm Based on Velocity Communication［J］. Acta Metrologica Sinica, 2020, 41(8): 1002-1011.
［16］Chen T, Ye M H, Liu S L, et al. Experimental study on cross-sensitivity of temperature and vibration of embedded fiber Bragg grating sensors［J］. Optoelectronics letters, 2018, 14(2): 92-97.