A new experimental application of least-squares techniques for the estimation of the induction motor parameters

This paper deals with a new experimental approach to the parameter estimation of induction motors with least-squares techniques. In particular, it exploits the robustness of total least-squares (TLS) techniques in noisy environments by using a new neuron, the TLS EXIN, which is easily implemented on-line. After showing that ordinary least-squares (OLS) algorithms, classically employed in literature, are quite unreliable in presence of noisy measurements, which is not the case for TLS, the TLS EXIN neuron is applied numerically and experimentally for retrieving the parameters of the induction motor by means of a test-bench. Additionally, for the case of very noisy data, a refinement of the TLS estimation has been obtained by the application of a constrained optimisation algorithm which explicitly takes into account the relationships among the K-parameters. The strength of this approach and the enhancement obtained is fully demonstrated first numerically and then verified experimentally.

[1]  G. A. Capolino,et al.  Recursive Least Squares Rotor Time Constant Identification for Vector Controlled lnduction Machine , 1992 .

[2]  Cursino B. Jacobina,et al.  Estimating the parameters of induction machines at standstill , 1999, IEEE International Electric Machines and Drives Conference. IEMDC'99. Proceedings (Cat. No.99EX272).

[3]  A. De Carli,et al.  Parameter identification for induction motor simulation , 1976, Autom..

[4]  Rik Pintelon,et al.  A weighted total least squares estimator for multivariable systems with nearly maximum likelihood properties , 1998, IEEE Trans. Instrum. Meas..

[5]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[6]  P. Lataire,et al.  Estimation of a Global Synchronous Machine Model Using a Multiple-Input Multiple-Output Estimator , 2002, IEEE Power Engineering Review.

[7]  A. De Carli,et al.  Parameter identification for induction motor simulation , 1974, Autom..

[8]  F.M. Raimondi,et al.  Parameter identification of a mathematical model of induction motors via least squares techniques , 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

[9]  Rik Pintelon,et al.  Identification of synchronous machine parameters using a multiple input multiple output approach , 1999 .

[10]  Maurizio Cirrincione,et al.  Estimation of the electrical parameters of an induction motor with the TLS EXIN neuron , 2002, Proceedings of the Fourth IEEE International Caracas Conference on Devices, Circuits and Systems (Cat. No.02TH8611).

[11]  L. Trefethen,et al.  Numerical linear algebra , 1997 .

[12]  Sabine Van Huffel.  Analysis of the Total Least Squares Problem and its Use in Parameter Estimation , 1987 .

[13]  Maurizio Cirrincione,et al.  Experimental verification of a technique for the real-time identification of induction motors based on the recursive least-squares , 2002, 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623).

[14]  A.M.N. Lima,et al.  Real-time estimation of the electrical parameters of an induction machine using sinusoidal PWM voltage waveforms , 1997, IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting.

[15]  Ricardo D. Fierro,et al.  The Total Least Squares Problem: Computational Aspects and Analysis (S. Van Huffel and J. Vandewalle) , 1993, SIAM Rev..

[16]  Giansalvo Cirrincione,et al.  Linear system identification using the TLS EXIN neuron , 1999, Neurocomputing.

[17]  Christiaan Moons,et al.  Parameter identification of induction motor drives , 1995, Autom..

[18]  Sabine Van Huffel,et al.  The MCA EXIN neuron for the minor component analysis , 2002, IEEE Trans. Neural Networks.

[19]  Jennifer Stephan,et al.  Real-time estimation of the parameters and fluxes of induction motors , 1992, Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting.