基于动态自由能磁滞模型的GMM-FBG电流传感器磁滞建模与参数辨识

doi:10.3969/j.issn.1000-1158.2022.04.13

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摘要为解决GMM-FBG电流传感器中存在的磁滞非线性和涡流损失问题,提出了一种耦合涡流损失模型的动态自由能磁滞模型,采用非线性遗传算法对该模型进行参数辨识和优化,提高了模型在工频下对磁滞曲线的预测精度。搭建了GMM-FBG电流传感器实验平台,利用所建的磁滞模型对传感系统进行建模补偿和实验验证。实验结果表明该模型能够较好地预测工频下传感器中的动态磁滞非线性,模型的预测误差在3.6%以内,电流测量灵敏度可达到0.069nm/A。

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	滕峰成
	杨雪璠
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	叶文昊

关键词 ：计量学, GMM-FBG电流传感器, 动态自由能磁滞模型, 非线性遗传算法, 参数辨识

Abstract：In order to solve the hysteresis nonlinearity and eddy current loss problems in GMM-FBG current sensor, a dynamic free energy hysteresis model of coupled eddy current loss model was proposed, and nonlinear genetic algorithm was used to identify and optimize the parameters of the model, which improved the prediction accuracy of the model to the hysteresis curve at power frequency. A GMM-FBG current sensor experimental platform was built, and the hysteresis model was used to compensate the sensor system and verify the sensor system. The experimental results showed that the model can well predict the dynamic hysteresis nonlinearity of the sensor under power frequency, the prediction error of the model is within 3.6%, and the sensitivity of current measurement can reach 0.069nm/A.

Key words： metrology GMM-FBG current sensor dynamic free energy hysteresis model nonlinear genetic algorithm parameter identification

收稿日期: 2020-06-29 发布日期: 2022-04-08

PACS:

TB97

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

引用本文:

滕峰成,杨雪璠,吕登岩,叶文昊. 基于动态自由能磁滞模型的GMM-FBG电流传感器磁滞建模与参数辨识[J]. 计量学报, 2022, 43(4): 513-520.
TENG Feng-cheng,YANG Xue-fan,Lü Deng-yan,YE Wen-hao. Hysteresis Modeling and Parameter Identification of GMM-FBG Current Sensor Based on Dynamic Free Energy Hysteresis Model. Acta Metrologica Sinica, 2022, 43(4): 513-520.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.04.13 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I4/513

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