Sensorless control of permanent magnet synchronous propulsion motor for ships with parameter identification

被引:0
作者
Chen Z.-F. [1 ,2 ]
Liu Y.-C. [1 ]
Lu H.-Y. [1 ]
机构
[1] Marine Engineering College, Dalian Maritime University, Dalian
[2] Zhejiang International Martime College, Zhoushan
来源
Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2020年 / 24卷 / 03期
关键词
Marine permanent magnet synchronous motor; MRAS; Parameter identification; Sensorless;
D O I
10.15938/j.emc.2020.03.007
中图分类号
学科分类号
摘要
To achieve accurate control, it needs to modify motor parameters of the sensorless control of the permanent magnet synchronous propulsion motor of ship. In this paper, the model reference adaptive method (MRAS) is used for the online identification of the parameters of the permanent magnet synchronous motor. The RungeKutta method was used to establish a full-rank adjustable model, and the adaptive law was derived according to Popov's super-stability theorem. The identification of resistance and inductance parameters was completed by step identification, the identified parameters were obtained by high-frequency harmonic filtering through a low-pass filter to obtain precise motor parameters, and the actual parameters of the filtered motor were used for algorithm feedback to dynamically update the motor model. Both the software simulation and the physical platform verified that the MRAS parameter online identification algorithm can accurately and effectively identify the actual stator resistance and inductance of the motor. The sensorless control scheme for marine permanent magnet propulsion motors with parameter identification is feasible and effective. © 2020, Harbin University of Science and Technology Publication. All right reserved.
引用
收藏
页码:53 / 61
页数:8
相关论文
共 18 条
[1]  
Li H., Liu X., Xie X., Influence ofparameters on control system and improved identification method of pitch motor in wind turbine generator system, Electric Machines and Control, 23, 7, (2019)
[2]  
Wu Y., Huang X., Gao X., A cult-ural artificial fish-swarm optimization algorithm andapplication in the parameters identification of rotor system, Electric Machines and Control, 16, 5, (2012)
[3]  
Liu Y., Liu S., Guo H., Research on online parameter identification adaptive control method for UUV propulsion motor, Electiric Machines and Control, 20, 4, (2016)
[4]  
Wang Z., Ye Y., Sensorless selfstarting process of permanent magnet synchronous motor based on back EMF, Electric Machines and Control, 15, 10, (2011)
[5]  
Wang L., Yang Z., Improvement of PMSM model in SIMULINK and its application in parameter identification, Electric Machines and Control, 16, 7, (2012)
[6]  
Wang G., Yang R., Initial rotor position estimation method for interior permanent magnet synchronous motor, Electric Machines and Control, 10, 6, (2010)
[7]  
Hu Q., Sun C., Speed sensorless control of permanent magnet synchronous motor in full speed range, Electric Machines and Control, 20, 9, (2016)
[8]  
Piippo A., Hinkkanen M., Luomi J., Analysis of an adaptive observer for sensorless control of interior permanent magnet synchronous motors, IEEE Transactions on Industrial Electronics, 55, 2, (2008)
[9]  
Lin G., Zhang J., Liu C., Parameter identification of PMSM using improved comprehensive learning particle swarm optimization, Electric Machines and Control, 19, 1, (2015)
[10]  
Chen Z., Zhong Y., Li J., Adaptive online parameter identification of embedded PMSM, Electric Machines and Control, 14, 4, (2010)