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一种多谐波激励下取向硅钢片的磁滞及损耗特性预测方法 被引量:2

A Prediction Method for Hysteresis and Loss Characteristics of Oriented Silicon Steel Sheet Under Multi-harmonic Excitation
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摘要 基于损耗分离与场分离的统一性,利用Preisach模型实现静态磁滞损耗的计算,提出单一频率正弦激励下异常损耗参数辨识的改进方法,有效地提高了正弦激励下动态磁滞模型的计算精度。利用软磁磁性能测量系统,基于取向硅钢片在多个不同单频正弦条件下的损耗测量结果进行参数提取,构造多谐波激励下异常损耗对应场量的表达式,从而提出任意复杂多谐波激励下取向硅钢片的动态磁滞模型。在多种不同工况下,将动态磁滞回线及总铁耗的预测值与测量值进行对比,二者较为吻合,说明了文中所提模型的有效性和通用性。 Based on the unification of loss separation and field separation, the Preisach model was utilized to calculate the static hysteresis losses. An improved method for the parameter identification of abnormal losses was proposed to improve the accuracy of dynamic hysteresis model under single frequency sinusoidal excitation. The parameter extraction was realized based on the measured losses of the oriented silicon steel sheet under different single frequency sinusoidal conditions by the magnetic property measurement system. The field quantity expression corresponding to the abnormal losses under multi-harmonic excitation was constructed, thus the dynamic hysteresis model of oriented silicon steel sheet under arbitrary complex multi-harmonic excitations was proposed.The predicted dynamic hysteresis loops and losses under various working conditions are compared with the measured ones, which verifies the accuracy and generality of the improved model proposed in this paper.
作者 赵小军 徐华伟 黄康 周磊 王瑞 苑东伟 ZHAO Xiaojun;XU Huawei;HUANG Kang;ZHOU Lei;WANG Rui;YUAN Dongwei(Department of Electrical Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;Tangshan Power Supply Company,State Grid Jibei Electric Power Co.,Ltd.,Tangshan 063000,Hebei Province,China;Maintenance Branch Tangshan Branch,State Grid Ji Bei Electric Power Co.,Ltd.,Tangshan 063000,Hebei Province,China)
出处 《中国电机工程学报》 EI CSCD 北大核心 2022年第4期1625-1632,共8页 Proceedings of the CSEE
基金 国家重点研发计划项目(2017YFB0902703) 北京市自然科学基金项目(3212036) 中央高校基本科研业务费(2019MS078)。
关键词 软磁材料 谐波激励 PREISACH模型 磁滞特性 损耗预测 soft magnetic materials harmonic excitation Preisach model hysteresis loss prediction
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