-
超磁致伸缩材料(GMM)的磁滞模型随着激励幅值、偏磁情况、激励频率的变化模型参数也会发生变化,现有的磁滞模型无法预测三种外部条件同时变化所带来的影响。本文通过传统Jiles-Atherton(J-A)动态模型解释磁滞损耗机理,根据运行条件和材料特性建立关系式来反应外界条件变化。针对J-A模型建立与激励幅值相关的关系式,针对剩余损耗建立起剩余损耗系数与激励幅值和偏磁情况的关系式,同时利用分数阶对系统的涡流损耗重新进行定义,从而得到修正后的磁滞模型。文中利用遗传算法对不同运行条件下的试验数据进行模型参数辨识,根据模型参数以及运行条件得到相应的修正系数。通过模型的仿真情况,验证了修正后模型的精度,分析了涡流和剩余损耗的影响因素以及对模型预测的影响;通过对磁滞模型进行评估,对比了磁滞曲线与磁滞损耗的误差情况。结果表明,修正后的模型能够对不同的激励进行高精度预测,低频时忽略动态损耗会造成较大误差,且涡流和剩余损耗对磁滞模型精度具有较大影响,在对具体磁滞情况进行分析时利用磁滞曲线进行评估更为准确。
-
关键词:
- 超磁致伸缩材料(GMM) /
- Jiles-Atherton(J-A)动态模型 /
- 涡流损耗 /
- 剩余损耗
The hysteresis model of giant magnetostrictive materials(GMM) changes the model parameters as the excitation amplitude, bias condition and excitation frequency change, and the existing hysteresis model is unable to predict the effects of simultaneous changes in the three external conditions. In this paper, the hysteresis loss mechanism is explained by the traditional Jiles-Atherton(J-A) dynamic model, and the relation equation is established according to the operating conditions and material properties to respond to the change of external conditions. For the J-A model, the relationship equation related to the excitation amplitude is established, and the relationship equation between the residual loss coefficient and the excitation amplitude and the bias condition is established for the residual loss, while the eddy current loss of the system is redefined using the fractional order to obtain the modified hysteresis model. In the paper, the genetic algorithm is used to identify the model parameters of the test data under different operating conditions, and the corresponding correction coefficients are obtained according to the model parameters as well as the operating conditions. The accuracy of the modified model is verified by simulating the model and analyzing the influences of eddy currents and residual losses and their effects on the model predictions; the hysteresis model is evaluated to compare the hysteresis curves with the hysteresis losses in terms of errors. The results show that the modified model is capable of predicting various excitations with high accuracy, and that neglecting dynamic losses at low frequencies leads to large errors. If the model order of the eddy current loss is smaller than the actual order of the material, the predicted hysteresis curve will be contracted inward and the predicted eddy current loss will be small; on the contrary, the predicted hysteresis curve will be expanded outward and the predicted eddy current loss will be large, and with the increase of the excitation frequency, both cases will cause the prediction error to become larger and larger. When the bias magnetic field is zero, the residual loss coefficient is unchanged, when the bias magnetic field is unchanged, the excitation amplitude increases, the residual loss coefficient decreases, when the excitation amplitude is unchanged, the bias magnetic field increases, the residual loss coefficient also increases. When both changes at the same time, the two parameters must actually be analyzed on the residual loss coefficient. Using hysteresis curves to evaluate hysteresis is more accurate.-
Keywords:
- giant magnetostrictive materials(GMM) /
- Jiles-Atherton(J-A) dynamic model /
- eddy current loss /
- residual loss
-
[1] Yang Z J, Li J H, Zhou Z G, Gong J X, Bao X Q, Gao X X 2022 Metals 12 341
[2] Yamaura S, Nakajima T, Kamata Y, Sasaki T, Sekiguchi T 2020 J. Magn. Magn. Mater. 514 167260
[3] Yu C F, Wu G, Wang Y, Xiao Z H, Duan Y Y, Chen Z 2022 IEEE Access 10 43501
[4] Li Y S 2023 Shock Vib. 2023 7379276
[5] Liu Y G, Gao X H, Li Y L 2016 Sensor. Actuat. A-Phys. 250 7
[6] Sablik M J, Jiles D C 1988 J. Appl. Phys. 64 5402
[7] Unniachanparambil G M, Kulkarni S V 2019 IET Electr. Power App. 13 2090
[8] Wang Yang, Liu Zhizhen 2017 Proceedings of the CSEE 37 313(in Chinese) [王洋,刘志珍 2017 中国电机工程学报37 313]
[9] Liu Ren, Li Lin 2019 High Voltage Engineering 45 4062(in Chinese) [刘任,李琳 2019 高电压技术45 4062]
[10] Tantai L Y, Han X Q, Wang L, Yuan T J 2020 Power System Technology 44 122(in Chinese) [澹台乐琰,韩肖清,王磊,袁铁江 2020 电网技术44 122]
[11] Liu R, Gu C Y, Sun J D, Tang B 2024 Proceedings of the CSEE 1 1(in Chinese) [刘任,顾朝阳,孙江东,唐波 2024 中国电机工程学报1 1]
[12] Zhang B, Gupta B, Ducharne B, Sébald G, Uchimoto T 2018 IEEE T. Mang. 54 7301605
[13] Zhang B, Gupta B, Ducharne B, Sébald G, Uchimoto T 2018 IEEE T. Mang. 54 6100204
[14] Liu R, Li L 2021 IEEE T. Power Electr. 36 2009
[15] Hamimid M, Mimoune S M, Feliachi M 2012 Physica B. 407 2438
[16] Liu Y G, Gao X H, Chen C X 2016 Math. Probl. Eng. 2016 2609069
[17] Meng A H, Zhu J M, Kong M, He H L 2013 IEEE T. Mang. 49 552
[18] Chen B, Qin X B, Tang B, Liu R, Zhang J G, Wan N N 2022 Proceedings of the CSEE 42 4590(in Chinese) [陈彬,秦小彬,唐波,刘任,张建功,万妮娜 2022 中国电机工程学报42 4590]
[19] Li Y, Zhu L H, Zhu J G 2018 IEEE T. Mang. 54 1300105
[20] Baghel A P S, Kulkarni S V 2014 IEEE T. Mang. 50 7009004
[21] Jumarie G 2009 Appl. Math. Lett. 22 1659
[22] Wang Y, Liu Z Z 2016 IEEE T Appl. Supercon. 26 0608905
[23] Liu R, Li L 2019 IEEE T. Mang. 55 7501404
[24] Wei Y F, Yang X, Chen Y K, Zheng H B, Su L L 2022 IEEE T. Mang. 58 7300909
[25] Zhu Y C, Yang X L, Wereley N M 2016 Smart Mater. Struct. 25 085030
[26] Du R Y, Robertson P 2015 UKSim (Cambridge: Emmanuel Coll)p432
计量
- 文章访问数: 108
- PDF下载量: 6
- 被引次数: 0