摘要
感应电机由于其可靠性好、结构简单等优点成为工业伺服控制的主要传动装置。但是感应电机同时又是一个多变量、非线性、强耦合的系统,矢量控制较好地解决了感应电机磁链和转矩的解耦,取得了优良的控制性能。但随着运行工况的变化,电机的参数尤其是电气参数的变化严重地影响了矢量控制系统的控制效果,转子电阻的变化破坏了磁链和转矩之间的解耦关系,致使电机的控制变得更加困难。因此在线辨识感应电机的转子电阻对提高感应电机矢量控制系统性能具有重要意义。
论文综述并分析了多年来感应电机转子参数辨识的各种控制策略,详细阐述了感应电机动态数学模型的性质,通过坐标变换,给出了在不同坐标系下感应电机的数学模型的表达形式并基于转子磁场定向的旋转坐标系,介绍了矢量控制系统的基本原理,基于Matlab建立了该系统地仿真模型,仿真研究了转子电阻变化对系统性能的影响。在此基础上,开展采用模型参考自适应理论(MRAS)的感应电机转子电阻智能辨识方案的研究。文中分析了传统MRAS存在的问题,采用无功功率模型,结合模糊控制技术,设计了转子电阻模糊模型参考自适应辨识策略;将神经网络和MRAS技术相结合,提出了基于神经网络的MRAS转子电阻智能辨识器。论文对所提辨识器进行了仿真研究,证明了其有效性,并基于FPGA技术,采用SG仿真工具,对转子电阻辨识器的硬件实现进行了初步探讨,为辨识器的工业应用奠定了基础。
Induction Motor is one of the most widely used actuator for industrial applications due to its advantages such as reliability,simplicity, low cost and volume manufacturing. However induction motors is a multi-variable, non-linear, highly coupled system. Vector control can solve the induction motor torque and flux of decoupling, and achieved excellent control performance. However, with changes in the operating conditions, the changes of induction motor parameters especially electrical parameters effect the controll effort of vector control system seriously , the changes of rotor resistance undermined the decoupling between flux and torque, which made Motor control more difficult. Therefore online identification of induction motor rotor resistance is of great significance to improve performance of induction motor vector control system.
In this paper various control strategies of indution motor rotor parameter identification in the recently years is synthesised and analysised, detailed nature of induction motor dynamic mathematical model, through the coordinate transformation , given out different induction motor mathematical models in different coordinate system. Based on the rotor magnetic field orientation and rotation coordinates, vector control system is introduced and established vector contoll simulition model based on Matlab and do research on effects of the rotor resistance change of system performance.Based on this research, research on intelligent identification of induction motor rotor resistence theories is done based on Model Reference Adaptive theory (MRAS) .This paper analyses the problems of traditional MRAS,with reactive power model, with fuzzy control technology, design rotor resistance identification strategy based on fuzzy model reference adaptive system and rotor resistance MRAS intelligent identifier based on the neural network. In the paper do simulation of the identification strategies in the paper and prove the effectiveness of the strategies, and based on FPGA technology, use SG simulation tool , the identification of the rotor resistance hardware implementation has been discussed and laid the foundation for industrial applications of induction motor rotor resistance.
引文
1.陈伯时.电力拖动自动控制系统[M].北京:机械工业出版社, 2004.8—16
2.尔桂花,窦曰轩.运动控制[M].北京:清华大学出版社,2002第一版.108-115
3.罗慧,刘军峰,万淑云.感应电机参数的离线辨识[J].电气传动,2006,36(8):16—21
4.王明渝,冼成瑜,惠娅倩.感应电动机矢量控制参数离线辨识技术[J]. 2006,21(8):90—96
5.Maurizio,Cirrincione. A New Experimental Application of Least-Squares Techniques for the Estimation of the Induction Motor Parameters[J].IEEE transactions on industry applications.2002,2:1171—1180
6.J. Stephan,M.Bodson,and J.Chiasson.Real time estimation of the parameters and fluxes of induction motors[J]. IEEE Transactions on Industry Applications,1994,30(3):746—759
7. Rachid Beguenane,Mohand A.Ouhrouche,Andrzej M.Trzynadlowski.A new scheme for sensorless induction motor control drives operating in low speed region[J]. Mathematics and Computers in Simulation,2006,71(2):109—120
8. Li Han,Wen Xuhui, Chen Guilan,New General MRAS Adaptive Scheme to Estimate Stator and Rotor Resistance of Induction Motors[C].Industry Applications Conference, 2006,4(8): 1775—1780
9. L. Loron and G. LaliW.Application of the extened kalman filter to parameters estimation of induction motors.The European Power Electronics Association,1993:85—90
10. T. Katmka,S.Toda.Online estimation of induction motor parameters by extened kalman filter. The European Power Electronics Association.1993,4:325—329
11.夏超英.交直流传动系统的自适应控制[M].北京,机械工业出版社,1998:273—285.
12.金海,黄进.基于模型参考方法的感应电机磁链的自适应观测及参数辨识[J].电工技术学报,2006,21(1):65—69
13. Ertan,H.B.Identification of parameters of an induction motor from field solution.2003,1:1—6
14. F.J.Lin,D.H.Wang and P.K.Huang, FPGA-based fuzzy sliding-mode control for a linear induction motor drive, IEE Proc.Electr. Power Appl.,2005,152( 5):1137—1148
15. Daigo Akamatsu,Akira Chiba.An improved rotor resistance identification for bearingless induction motors.2005,15(18):1981—1987
16. Geng Yang,Tung-Hai Chin.MRAS-based speed sensorless control scheme with stator resistance identification function for vector-controlled induction motor drives[J]. Electrical Engineering in Japan,2007,113(1):98—108
17.Li Zhen,Longya Xu.A mutual MRAS identification scheme for position sensorlee field orientation control of induction machines. Industry ApplicationsConference.1995,1:159—165
18.胡楷,潘孟春,李圣怡,单庆晓.基于MRAS的异步电机转子时间常数实时辨识[J].电气传动自动化,2005,27(4):45—48
19. Huang Bin,Qu Wenlong,Lu Haifeng.A Novel On-line Rotor Resistance Estimation Method for Vector Controlled Induction Motor Drive[A].IPEMC [C],2004,2:655—660
20. S.Min,M.E.Elbuluk,and D.S.Zinger.PI and fuzzy estimators for tuning the stator resistancein direct torque control of induction machines[J]. IEEE Transactions on Power Electronics.1998,13(2): 279—287
21. Jordi Catalài López, Luis Romeral. Novel Fuzzy Adaptive SensorlessInduction Motor Drive.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2006,53(4):1170-1178
22. H. Razik and A. Rezzoug, Identification of electrical parameters of an induction motor, a comparison between R.L.S and genetic algorithm, ICEM2000, ESPOO, Finland, 2000:1433—1437
23.郭慧浩,范瑜,邵利.自适应遗传算法在直线感应电机参数优化中的应用[J].防爆电机.2005,3(40):16—18
24. Richard R. Bishop and Gill G.Richards , Identifying Induction Machine Parameters Using A Genetic Optimization Algorithm , IEEE Proceedings .1990,2:476—479
25.Chunki Kwon, Scott D. Sudhoff, An on-line rotor resistance estimator for induction machine drives[C]. International Conference IEEE,2005,15(18):391—397
26. Ertan,H.B.Identification of parameters of an induction motor from field solution.2003,1:1—6
27. Sergio Villazana, Antonino Caralli ,A Novel Method to Estimate the Rotor Resistance of the Induction Motor Using Support Vector Machines[C]. IEEE Industrial Electronics, IECON 32nd Annual Conference,2006:952—957
28. P.Y.Chung,M.Dolen,and R.D. Lorenz, Parameter identification of induction machines by continuous genetic algorithms, 2000,10:341—346
29. F.Alonge, F.Dippolito, and F.M.Raimondi.Parameter identification of induction motors:Least squares vs. genetic algorithms, IWMC, Grenoble, France, 1998:1112—1122
30. L.Charaabi(1), E.Monmasson .FPGA-based implementation of DTSFC and DTRFC algorirhms.2005:245—250
30. F.J.Lin,D.H.Wang and P.K.Huang, FPGA-based fuzzy sliding-mode control for a linear induction motor drive, IEE Proc.Electr. Power Appl.,2005,152( 5):1137—1148
31. N.Benaidja,N.Khenfer.Identification of Asynchronous Machine Parameters by Evolutionary Techniques[J].Electric Power Components and Systems,2006,34(12):1359—1376
32. R.C.Eberhart and J.Kennedy. A New Optimizer Using Particles Swarm Theory. Proc. Sixth International Sympsium on M icroMachine and Human Science, Nagoya, Japan,1995
33. J. Kennedy and R. C. Eberhart. Particle Swarm Optimization. IEEE International Conference on N eural Network.1995,4:1942—1948
34.李永东.交流电机数字控制系统[M].北京,机械工业出版社,2002:177—303
35.王成元,夏家宽.电机现代控制技术[M].北京,机械工业出版社,2006:59—63
36. Geng Yang, Tung-Hai Chin. MRAS-based speed sensorless control scheme with stator resistance identification function for vector-controlled induction motor drives[J]. Electrical Engineering in Japan,2007,113(1):98—108
37. Geng Yang,Tung-Hai Chin.MRAS-based speed sensorless control scheme with stator resistance identification function for vector-controlled induction motor drives[J]. Electrical Engineering in Japan,2007,113(1):98—108
38.Karanayil,B.Rahman,M.F.Grantham.On-line rotor resistance identification for induction motor drive with artificial neural networks supported by a simple PI stator resistance estimator[C].2003,1:433—438
39. Akamatsu Daigo,Orita Shuichi. Rotor Resistance Identification and Stability Control Method in Bearingless Induction Motor Drives[J]. Transactions of the Institute of Electrical Engineers of Japan. D,2005,125(10):895—904
40. Geng Yang, Tung-Hai Chin. MRAS-based speed sensorless control scheme with stator resistance identification function for vector-controlled induction motor drives[J]. Electrical Engineering in Japan,2007,113(1):98—108
41. M.Cirstea,A.Dinu, A New Neural Networks Approach to Induction Motor Speed Control , 2001,2:784—787
42. Karanayil,B.Rahman,M.F.On-line Stator and Rotor Resisance Estimation Scheme Using Artificial Neural Networks for Vector controlled Speed Sensorless Induction Motor Drive.Industrial Electronics,2007,54(1):167—176
43.杨会东,李岚.自适应神经网络在异步电动机转速辨识中的应用[J].微特电机,2007,
44. K.B.Lee.Disturbance observer that uses radial basis function networks for the low speed control of a servo motor.IEE Proc.-Control Theory Applications.2005,152(2):118—124
45. Xilinx Inc.. Xilinx System Generator for DSP Tutorals[M]. USA:Xilinx Inc., 2003.
46. Xilinx Inc.. Xilinx System Generator for DSP v3.1 Reference Guide[M] . USA:Xilinx Inc., 2003.
47.James Hwang, Jonathan Ballagh. Building Custom FIR Filters Using System Generator[A]. FPL[C] 2002:1101-1104.