A Simple Leakage Inductance Identification Technique for Three-Phase Induction Machines Under Variable Flux Condition

Accurate knowledge about the leakage inductance at different flux levels is required for the correct identification of the magnetizing curve of the induction machine. Its exact value is also necessary for the estimation of various other parameters required for high performance control. This paper proposes a simple technique to identify the leakage inductance of the machine at different flux levels by operating it near the torque limit. The leakage inductance is stored in the processor memory as a function of the rotor magnetizing current through the “piecewise mixed model” of approximation for online application. Different experiments performed on a practical machine validate the proposed concept.

[1]  Mohamed S. Zaky,et al.  Wide-Speed-Range Estimation With Online Parameter Identification Schemes of Sensorless Induction Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[2]  C. Schauder,et al.  Adaptive speed identification for vector control of induction motors without rotational transducers , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[3]  A. do Prado,et al.  On-Line Identification of Induction Motors using Discrete Models for Sinusoidal Signals , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[4]  Debashis Chatterjee A Novel Magnetizing-Curve Identification and Computer Storage Technique for Induction Machines Suitable for Online Application , 2011, IEEE Transactions on Industrial Electronics.

[5]  W. Marsden I and J , 2012 .

[6]  Andrea Cavagnino,et al.  Computational Algorithms for Induction-Motor Equivalent Circuit Parameter Determination—Part I: Resistances and Leakage Reactances , 2011, IEEE Transactions on Industrial Electronics.

[7]  H.B. Ertan,et al.  Detection of Some Parameters of Induction Motors a Proposal and Its Verification , 2007, 2007 7th International Conference on Power Electronics and Drive Systems.

[8]  B. E. Kushare,et al.  Neural-Network-Based Parameter Estimations of Induction Motors , 2013 .

[9]  Slobodan N. Vukosavic,et al.  A method for magnetizing curve identification in rotor flux oriented induction machines , 2000 .

[10]  T. G. Habetler,et al.  An Evaluation of Model-Based Stator Resistance Estimation for Induction Motor Stator Winding Temperature Monitoring , 2002, IEEE Power Engineering Review.

[11]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[13]  E. Levi,et al.  A Review of RFO Induction Motor Parameter Estimation Techniques , 2002, IEEE Power Engineering Review.

[14]  Jin Young Choi,et al.  Flux observer with online tuning of stator and rotor resistances for induction motors , 2002, IEEE Trans. Ind. Electron..

[15]  M. Sumner,et al.  Autocommissioning for voltage-referenced voltage-fed vector-controlled induction motor drives , 1993 .

[16]  José Luiz Silvino,et al.  An improved estimation of the induction machine leakage inductances , 1999, IEEE Trans. Ind. Electron..

[17]  Horst Grotstollen,et al.  Off-line identification of the electrical parameters of an industrial servo drive system , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[18]  Hamid A. Toliyat,et al.  Neural-Network-Based Parameter Estimations of Induction Motors , 2008, IEEE Transactions on Industrial Electronics.

[19]  K. Gopakumar,et al.  A Rotor Flux Estimation During Zero and Active Vector Periods Using Current Error Space Vector From a Hysteresis Controller for a Sensorless Vector Control of IM Drive , 2011, IEEE Transactions on Industrial Electronics.