СОСТОЯТЕЛЬНОЕ ОЦЕНИВАНИЕ ПАРАМЕТРОВ  АСИНХРОННОГО ДВИГАТЕЛЯ С ОШИБКОЙ ПО СКОРОСТИ

Дмитрий Владимирович Иванов; Илья Львович Сандлер; Михаил Александрович Tерехин

doi:10.17308/sait/1995-5499/2025/4/61-74

Authors

Dmitriy V. Ivanov Volga State Transport Universitу https://orcid.org/0000-0002-5021-5259 (unauthenticated)
Ilya L. Sandler Volga State Transport University https://orcid.org/0000-0003-4967-3321 (unauthenticated)
Mikhail A. Terekhin Volga State Transport University; Samara State Medical University https://orcid.org/0009-0004-1127-0978 (unauthenticated)

DOI:

https://doi.org/10.17308/sait/1995-5499/2025/4/61-74

Keywords:

asynchronous motor, errors in variables, least squares method, K-parameters, consistent estimation, speed error, electric drive

Abstract

Estimation of the electromagnetic parameters of an asynchronous motor is often carried out on the basis of K-parameters. The model of an asynchronous motor based on K-pa rameters is linear in coefficients, which makes it possible to simplify identification algorithms. Typically, the model with K-parameters is used provided that the rotation speed of the electric motor shaft is constant. This condition assumes the degeneracy of the covariance matrix of the signals, which creates additional difficulties in identification. The article uses an improved model of an asynchronous motor based on K-parameters, which allows identification at variable speed. T he rotation speed of an asynchronous electric motor shaft in real identification systems is al ways measured with errors. Errors can be associated both with errors in speed determination sensors and with errors that arise when estimating speed without sensors. Discretization, as well as estimation of derivative values, also introduces additional errors. The presence of errors in velocity measurements leads to biased estimates when applying ordinary least squares (LS) to estimate K-parameters. The article proposes a new method for electromagnetic estimation of the parameters of an asynchronous motor with a squirrel-cage rotor with an error in speed. It has been proven that under non-restrictive assumptions on the signal and noise, the estimates will be highly consistent. The simulation results showed that the proposed identification method based on generalized total least squares (GTLS) allows one to obtain more accurate parameter estimates than the least squares (LS) used in such methods. The results of this article can be ap plied in the development of predictive diagnostic systems and in electric drive control systems.

Author Biographies

Dmitriy V. Ivanov, Volga State Transport Universitу

Candidate of Physical and Mathematical Sciences, Associate Professor of the Department of Information Systems Security; Associate Professor of the Department of Digital Technologies.
Ilya L. Sandler, Volga State Transport University

senior lecturer at the Department of Digital Technologies; senior lecturer at the Department of Automation and Control in Technical Systems.
Mikhail A. Terekhin, Volga State Transport University; Samara State Medical University

2nd year master’s student of department of Digital technologies; assistant at the Applied Medical Engineering School

References

Каширских В. Г. Динамическая идентификация асинхронных электродвигателей / В. Г. Каширских. – Кемерово : ГУ КузГТУ, 2005. – 140 с.

Cirrincione M. Power Converters and AC Electrical Drives with Linear Neural Networks / M. Cirrincione, M. Pucci, G. Vitale – Boca Raton: CRC Press, Taylor & Francis Group, 2012. – 661 p.

Toliyat H. A. A review of RFO induction motor parameter estimation techniques / H. A. Toliyat, E. Levi, М. Raina // IEEE Trans. Energy Convers – 2003. – No 18. – P. 271–283.

A review of parameter estimators and controllers for induction motors based on artificial neural networks / Gutierrez-Villalobos G. M. [et al.] // Neurocomputing. – 2013. – Vol. 118. – P. 87–100. https://doi.org/10.1016/j.necom.2013.02.018.

Боловин Е. В. Критический экспертный анализ методов идентификации параметров асинхронных двигателей / Е. В. Боловин // Научный вестник Новосибирского государственного технического университета. – 2015. – № 1(58). – С. 7–27.

A Review on Parameters Identification Methods for Asynchronous Motor / X. Zhan [et al.] //International Journal of Advanced Computer Science and Applications. – 2015. –Vol. 6, No 1. – P. 104–109. http://dx.doi.org/10.14569/IJACSA.2015.060115

Parameter Identification of Inverter-Fed Induction Motors: A Review/ J. Tang [et al.]. – 702018. – T. 11, 2194. https://doi.org/10.3390/en11092194

Moons C., De Moor B. Parameter identification of induction motor drives / C. Moons, B. De Moor // Automatica. – 1995. – Vol. 31, No 8. – P. 1137–1147.

The estimation of the induction motor parameters by the GeTLS EXIN neuron / M. Cirrincione [et al.] // 2010 IEEE Energy Conversion Congress and Exposition. – P. 1680–1685

Cirrincione G., Cirrincione M. Neural Based Orthogonal Data Fitting: The EXIN Neural Networks / G. Cirrincione, M. Cirrincione // Series: Adaptive and Learning Systems for Signal Processing, Communications and Control. New York: Wiley & Sons, 2010. – 255 c.

Иванов Д. В. Идентификация тяговых асинхронных электродвигателей при наличии ошибок измерений / Д. В. Иванов, О. А. Кацюба // Вестник СамГУПС. – 2015. – № 3(29). – С. 154–158.

Ivanov D. V. Generalized total least squares for identification of electromagnetic parameters of an induction motor / D. V. Ivanov, I. L. Sandler, N. V. Chertykovtseva // Journal of Physics: Conference Series. – 2021. – Vol. 2032, No 1. – P. 012093. – DOI 10.1088/17426596/2032/1/012093.

Ivanov D. V. (2023) Identification of Fractional Models of an Induction Motor with Errors in Variables / D. V. Ivanov // Fractal Fract. – No 7, 485. https://doi.org/10.3390/fractalfract7060485

Ivanov D. V. Identification of parameters of induction motor with error of speed sensor / D. V. Ivanov [et al.] // Journal of Physics: Conference Series. – 2022. – Vol. 2176, No 1. – P. 012027. – DOI 10.1088/1742-6596/2176/1/012027.

Stephan J. Real-time estimation of the parameters and fluxes of induction motors / J. Stephan, M. Bodson, J. Chiasson // IEEE Transactions on Industry Applications. – 1994. – Vol. 30, No 3. – P. 746–759. – DOI 10.1109/28.293725.

Zhdanov A. I., Katsyuba O. A. Consistency of least-square estimates of parameters of linear difference equations with autocorrelation noise / A. I. Zhdanov, O. A. Katsyuba // Cybern Syst Anal – 1983. – Vol. 19. – P. 716–725. DOI 10.1007/BF01068771

Гантмахер Ф. Р. Теория матриц. – М. : Наука, 1966. –575 с.

Stoica P. Bias correction in least–squares identification / P. Stoica, T. Soderstrom // Int. J. Control – 1983. – Vol. 35, No 3. – P. 449–457.

Unton F. Recursive Estimator of the Solutions of Linear Equation Sequence / F. Unton // IEEE Trans. Aut. Control. – 1984. –V.AC – 29. N 2. – C. 177–179.

Söderström T., Stoica P. Instrumental Variable Methods for System Identification. – Berlin : Springer, 1983. – 245 p.

Söderström T. A generalized instrumental variable method for errors-in-variables / T. Söderström // Automatica. – 2011. – Vol. 47, No 8. – P. 1656–1666. – DOI: 10.1016/j.automatica.2011.05.010.

Ivanov D. V. Identification of Fractional Linear Dynamical Systems with Autocorrelated Errors in Variables by Generalized Instrumental Variables / D. V. Ivanov, I. L. Sandler, E. V. Kozlov // IFAC-PapersOnLine. – 2018. – Vol. 51, No 32. – P. 580–584. – DOI 10.1016/j.ifacol.2018.11.485.