Calculation of Impedance Parameters and Starting Characteristics of Large Induction Motor Based on Dynamic Magnetic Network
Xia Yunyan1, Zhou Zhou1, Shao Yuanliang2, Lian Rubo1
1. National Engineering Research Center of Large Electric Machines and Heat Transfer Technique Harbin University of Science and Technology Harbin 150080 China; 2. Harbin Electric Machinery Company Limited Harbin 150040 China
Abstract:Large induction motors are widely used in traction and drive applications. The accurate calculation of impedance parameters is crucial for predicting motor performance, determining the driving load, and optimizing motor design. The large induction motor has a long starting time and large current, experiencing apparent time-varying core saturation and skin effect. Current approaches, such as the magnetic network method, often neglect the nonlinearity in the magnetic permeance of the motor during the starting process, leading to inaccuracies in considering the saturation deviation caused by variable starting currents. Accordingly, the dynamic magnetic network method is proposed to analyze the electromagnetic performance of large induction motors in the transient process. Considering the influence of the saturation effect and skin effect on the motor impedance, the calculation method for motor starting characteristics is proposed, thereby improving the accuracy and efficiency of calculating dynamic impedance parameters and starting characteristics. The proposed nonlinear dynamic magnetic network model considers the air gap's magnetic permeance variation caused by the rotor's speed changes. The effect of the starting current characteristic on the saturation degree inside the motor is discussed. According to the magnetic field line and the saturation degree, the calculation method for nonlinear permeability in the dynamic magnetic network model is determined. Based on the dynamic magnetic network model and the rotor skin effect model, the dynamic impedance parameters are calculated by considering leakage flux, rotor speed variation, and saturation phenomena. The dynamic mathematical model of large induction motors is derived from the rotor multi-circuit method. According to the electromagnetic coupling relationship in the transient process, a dynamic coupling solution is determined to obtain dynamic impedance parameters and starting characteristics, considering variations in current, speed, saturation, and skin depth. Calculation accuracy at a lower computational cost is improved. Calculation results show that when the motor starts to reach the stable running state, the resistance increase coefficient gradually decreases from 5.517 to 1.025, and the reactance decrease coefficient increases from 0.36 to 1. Using dynamic parameters for calculating the starting characteristics of large motors improves accuracy. Compared with the traditional calculation method, the relative errors of starting torque, starting current, and maximum torque are reduced by 2.54%, 0.68%, and 5.39%, respectively. Compared with the finite element method (FEM), the solution time is reduced by about 90%. The following conclusions can be drawn. The proposed method ensures accurate calculation by coupling dynamic impedance parameters and starting characteristics for large induction motors, considering the nonlinearities in the magnetic permeance during the starting process and the saturation deviation caused by the time-varying starting current. Compared to the traditional calculation method and FEM, it improves accuracy and computational efficiency, facilitating calculations for related motor types.
夏云彦, 周洲, 邵远亮, 连如博. 基于动态磁网络法大型感应电机阻抗参数及起动特性计算[J]. 电工技术学报, 2024, 39(14): 4341-4352.
Xia Yunyan, Zhou Zhou, Shao Yuanliang, Lian Rubo. Calculation of Impedance Parameters and Starting Characteristics of Large Induction Motor Based on Dynamic Magnetic Network. Transactions of China Electrotechnical Society, 2024, 39(14): 4341-4352.
[1] Li Changbin, Wang Xiuhe, Liu Feng, et al.Analysis of permanent magnet-assisted synchronous reluctance motor based on equivalent reluctance network model[J]. CES Transactions on Electrical Machines and Systems, 2022, 6(2): 135-144. [2] 赵玫, 于帅, 张华强. 聚磁式横向磁通永磁直线电机的变磁导等效磁网络[J]. 电机与控制学报, 2020, 24(4): 12-22. Zhao Mei, Yu Shuai, Zhang Huaqiang.Variable permeability equivalent magnetic circuit network of flux-concentrated transverse flux permanent magnet linear machine[J]. Electric Machines and Control, 2020, 24(4): 12-22. [3] 禹春敏, 邓智泉, 梅磊, 等. 基于精确磁路的新型混合型轴向-径向磁悬浮轴承研究[J]. 电工技术学报, 2021, 36(6): 1219-1228. Yu Chunmin, Deng Zhiquan, Mei Lei, et al.Research of new hybrid axial-radial magnetic bearing based on accurate magnetic circuit[J]. Transactions of China Electrotechnical Society, 2021, 36(6): 1219-1228. [4] Hou Jining, Geng Weiwei, Li Qiang, et al.3-D equivalent magnetic network modeling and FEA verification of a novel axial-flux hybrid-excitation in-wheel motor[J]. IEEE Transactions on Magnetics, 2021, 57(7): 1-12. [5] 刘云飞, 张炳义, 宗鸣, 等. 基于非线性混合模型的模块组合式永磁电机磁场解析[J]. 电工技术学报, 2022, 37(18): 4593-4603. Liu Yunfei, Zhang Bingyi, Zong Ming, et al.Analytical prediction of magnetic field in modular combined permanent magnet motor by a nonlinear hybrid model[J]. Transactions of China Electro-technical Society, 2022, 37(18): 4593-4603. [6] 李想, 徐伟, 叶才勇. 新型定子永磁型动铁心式横向磁通直线振荡电机[J]. 中国电机工程学报, 2017, 37(21): 6209-6217. Li Xiang, Xu Wei, Ye Caiyong.Novel stator-magnet moving-iron transversal-flux linear oscillatory machine[J]. Proceedings of the CSEE, 2017, 37(21): 6209-6217. [7] Xu Gaohong, Liu Guohai, Jiang Shan, et al.Analysis of a hybrid rotor permanent magnet motor based on equivalent magnetic network[J]. IEEE Transactions on Magnetics, 2018, 54(4): 1-9. [8] Li Zhaokai, Huang Xiaoyan, Wu Lijian, et al.Open-circuit field prediction of interior permanent-magnet motor using hybrid field model accounting for saturation[J]. IEEE Transactions on Magnetics, 2019, 55(7): 1-7. [9] Cao Donghui, Zhao Wenxiang, Ji Jinghua, et al.Parametric equivalent magnetic network modeling approach for multiobjective optimization of PM machine[J]. IEEE Transactions on Industrial Elec-tronics, 2021, 68(8): 6619-6629. [10] 徐伟, 张祎舒, 曹辰, 等. 定子不对称极混合励磁双凸极电机改进型非线性变磁网络模型构建方法研究[J]. 中国电机工程学报, 2023, 43(1): 304-318. Xu Wei, Zhang Yishu, Cao Chen, et al.Improved construction method of nonlinear varying equivalent magnetic network model for hybrid excitation asymmetric stator pole double salient machine[J]. Proceedings of the CSEE, 2023, 43(1): 304-318. [11] 佟文明, 姚颖聪, 李世奇, 等. 考虑磁桥不均匀饱和的内置式永磁同步电机等效磁网络模型[J]. 电工技术学报, 2022, 37(12): 2961-2970. Tong Wenming, Yao Yingcong, Li Shiqi, et al.Equivalent magnetic network model for interior permanent magnet machines considering non-uniform saturation of magnetic bridges[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 2961-2970. [12] 佟文明, 王萍, 吴胜男, 等. 基于三维等效磁网络模型的混合励磁同步电机电磁特性分析[J]. 电工技术学报, 2023, 38(3): 692-702. Tong Wenming, Wang Ping, Wu Shengnan, et al.Electromagnetic performance analysis of a hybrid 13 excitation synchronous machine based on 3D equivalent magnetic network[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 692-702. [13] 唐敬. 轨道交通牵引感应电机电气故障建模与在线故障诊断方法研究[D]. 北京: 北京交通大学, 2020: 17-52. [14] 郑印钊, 周理兵, 王晋. 考虑实心体涡流影响的笼型实心转子电机起动工况导条电流分布的解析计算[J]. 电工技术学报, 2021, 36(11): 2355-2364. Zheng Yinzhao, Zhou Libing, Wang Jin.Analytical calculation of rotor bar current distribution in starting condition of cage solid-rotor machine considering the effect of eddy current in solid-rotor[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2355-2364. [15] Wu Shuang, Shi Tingna, Guo Liyan, et al.Accurate analytical method for magnetic field calculation of interior PM motors[J]. IEEE Transactions on Energy Conversion, 2021, 36(1): 325-337. [16] 韩力, 王斌, 仲杰, 等. 多绕组交流旋转电机饱和电感的快速求解算法[J]. 中国电机工程学报, 2021, 41(7): 2547-2556. Han Li, Wang Bin, Zhong Jie, et al.Fast algorithm for calculating the saturated inductances of multi-winding AC rotating machine[J]. Proceedings of the CSEE, 2021, 41(7): 2547-2556. [17] 崔征山, 周扬忠, 周祎豪. 基于改进子域模型的双绕组无轴承磁通切换电机磁场解析计算[J]. 电机与控制学报, 2022, 26(3): 66-77. Cui Zhengshan, Zhou Yangzhong, Zhou Yihao.Analytical calculation of magnetic field of dual-winding bearingless flux-switching permanent magnet motors based on improved subdomain model[J]. Electric Machines and Control, 2022, 26(3): 66-77.