Dynamic Model Identification of Induction Motors using Intelligent Search Techniques with taking Core Loss into Account

Traditionally, dynamic parameters of induction motors can be roughly estimated through conventional tests (no load test, block rotor test and retardation test) and core loss is neglected in the dynamic behaviours analysis. Due to the complication of dynamic behaviours of induction motors, inaccuracy of transient characteristics may obtain when using these dynamic parameters. In order to improving accuracy of dynamic behaviour analysis, however, the inclusion of core loss in the machine model needs to be re-addressed and an intelligent approach to estimated dynamic parameters needs to be adopted. In this paper, three of intelligent search techniques, which are i) Tabu Search (TS), ii) Adaptive Tabu Search (ATS) and iii) Genetic Algorithm (GA), are employed to demonstrate the effectiveness of intelligent identification compared with the conventional model with and without core loss parameter(RC). The simulation results from dynamic parameters including RC obtained by the GA in comparison with the experimental results are convinced the effectiveness for this aim.

[1]  R. Krishnan,et al.  Electric Motor Drives: Modeling, Analysis, and Control , 2001 .

[2]  Stephen J. Chapman,et al.  Electric Machinery Fundamentals , 1991 .

[3]  I. Margineanu,et al.  Method And Testing Equipment For Parameter Identification Of Induction Motors , 1998, Proceedings of the 6th International Conference on Optimization of Electrical and Electronic Equipments.

[4]  Richard M. Stephan,et al.  Vector Control Methods for Induction Machines: An Overview , 1995 .

[5]  Emil Levi,et al.  Iron loss in rotor-flux-oriented induction machines: identification, assessment of detuning, and compensation , 1996 .

[6]  Francesco Alonge,et al.  Parameter identification of induction motor model using genetic algorithms , 1998 .

[7]  Takayuki Mizuno,et al.  Decoupling Control Method of Induction Motor Taking Stator Core Loss into Consideration , 1989 .

[8]  P. Vadstrup,et al.  Parameter identification of induction motors using differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  R. D. Ball,et al.  Alternating-current machines. A review of progress , 1962 .

[10]  Jong-Wook Kim,et al.  Parameter identification of induction motors using dynamic encoding algorithm for searches (DEAS) , 2005, IEEE Transactions on Energy Conversion.

[11]  Scott D. Sudhoff,et al.  Analysis of Electric Machinery and Drive Systems , 1995 .

[12]  S. A. Nasar,et al.  Unified treatment of core losses and saturation in the orthogonal-axis model of electric machines , 1987 .

[13]  Kwanghee Nam,et al.  A vector control scheme for EV induction motors with a series iron loss model , 1998, IEEE Trans. Ind. Electron..

[14]  Dong-Seok Hyun,et al.  Stator-flux-oriented control of induction motor considering iron loss , 2001, IEEE Trans. Ind. Electron..

[15]  Paolo Ferraris,et al.  Induction motor iron losses measurement with a static converter supply using a slotless rotor test bench , 1994 .

[16]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary and genetic algorithms: theory and applications , 1997 .

[17]  Seung-Ki Sul,et al.  Implementation of field oriented induction machine considering iron losses , 1996, Proceedings of Applied Power Electronics Conference. APEC '96.

[18]  Deacha Puangdownreong,et al.  System identification via Adaptive Tabu Search , 2002, 2002 IEEE International Conference on Industrial Technology, 2002. IEEE ICIT '02..