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Dynamic performance of estimator-based speed sensorless control of induction machines using extended and unscented Kalman filters

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EN
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EN
This paper presents an estimator-based speed sensorless field-oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterises both the electrical and the mechanical behaviours of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages by using an estimator algorithm. An important aspect is that one of the state variables is the load torque and, hence, it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. The dynamic performance of these variants is tested and compared using experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present.
Wydawca
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Strony
129--144
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
  • Széchenyi István University H-9026 Győr, Egyetem tér 1., Hungary
autor
Bibliografia
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  • Amezquita-Brooks, L., Liceaga-Castro, E., Liceaga-Castro, J. and Ugalde-Loo, C. E. (2015). Flux-Torque Cross-Coupling Analysis of FOC Schemes: Novel Perturbation Rejection Characteristics. ISA Transactions, 58, pp. 446-461.
  • Auger, F., Hilairet, M., Guerrero, J. M., Monmasson, E., Orłowska-Kowalska, T. and Katsura, S. (2013). Industrial Applications of the Kalman Filter: A Review. IEEE Transactions on Industrial Electronics, 60(12), pp. 5458-5471.
  • Biswas, S. K., Qiao, L. and Dempster, A. G. (2017). A Novel A Priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency. IEEE Transactions on Automatic Control, 64(4), pp. 1852-1864.
  • Casadei, D., Profumo, F., Serra, G. and Tani, A. (2002). FOC and DTC: Two Viable Schemes for Induction Motors Torque Control. IEEE Transactions on Power Electronics, 17(5), pp. 779-787.
  • De Pelegrin, J., Torrico, C. R. C. and Carati, E. G. (2016). A Model-Based Suboptimal Control to Improve Induction Motor Efficiency. Journal of Control, Automation and Electrical Systems, 27(1), pp. 69-81.
  • Eisenberg, B. and Sullivan, R. (2008). Why is the Sum of Independent Normal Random Variables Normal? Mathematics Magazine, 81(5), pp. 362-366.
  • Fodor, D. and Tóth, R. (2004). Speed sensorless linear parameter variant H control of the induction motor. In: 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), Vol. 4. Nassau (Bahamas): IEEE, pp. 4435-4440.
  • Grafarend, E. W. (2006). Linear and Nonlinear Models: Fixed Effects, Random Effects, and Mixed Models. Berlin/New York: Walter de Gruyter.
  • Holtz, J. (2006). Sensorless Control of Induction Machines – With or Without Signal Injection. IEEE Transactions on Industrial Electronics, 53(1), pp. 7-30.
  • Horváth, K. and Kuslits, M. (2017). Speed sensorless field oriented control of induction machines using unscented Kalman filter. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP). Brasov: IEEE, pp. 523-528.
  • Jafarzadeh, S., Lascu, C. and Fadali, M. S. (2013). Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives. IEEE Transactions on Industry Applications, 49(1), pp. 92-99.
  • Jafarzadeh, S., Lascu, C. and Fadali, M. S. (2012). State Estimation of Induction Motor Drives Using the Unscented Kalman filter. IEEE Transactions on Industrial Electronics, 59(11), pp. 4207-4216.
  • Julier, S. J. and Uhlmann, J. K. (1997). A New Extension of the Kalman Filter to Nonlinear Systems. In: Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulations and Controls, Orlando.
  • Julier, S. J. and Uhlmann, J. K. (2004). Unscented Filtering and Nonlinear Estimation. Proceedings of the IEEE, 92(3), pp. 401-422.
  • Julier, S. J., Uhlmann, J. K. and Durrant-Whyte, H. F. (2000). A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators. IEEE Transactions on Automatic Control, 45(3), pp. 477-482.
  • Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), pp. 35-45.
  • Kim, Y.-R., Sul, S.-K. and Park, M.-H. (1994). Speed Sensorless Vector Control of Induction Motor Using Extended Kalman Filter. IEEE Transactions on Industry Applications, 30(5), pp. 1125-1133.
  • Kumar, S., Prakash, J. and Kanagasabapathy, P. (2011). A Critical Evaluation and Experimental Verification of Extended Kalman Filter, Unscented Kalman Filter and Neural State Filter for State Estimation of Three Phase Induction Motor. Applied Soft Computing, 11(3), pp. 3199-3208.
  • Lalley, S. (2001). Stochastic Calculus and Finance I, Lecture 5: Brownian Motion. Lecture Notes, The University of Chicago.
  • Leite, A. V., Araujo, R. E. and Freitas, D. (2004). Full and reduced order extended Kalman filter for speed estimation in induction motor drives: A comparative study. In: 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551), Vol. 3. Aachen (Germany): IEEE, pp. 2293-2299.
  • Lešić, V., Vašak, M., Stojičić, G., Perić, N., Joksimović, G. and Wolbank, T. M. (2012). State and parameter estimation for field-oriented control of induction machine based on unscented Kalman filter. In: International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion. Sorrento: IEEE, pp. 409-414.
  • Liu, K.-Z., Yokoo, M., Kondo, K. and Zanma, T. (2015). New Adaptive Vector Control Methods for Induction Motors with Simpler Structure and Better Performance. Control Theory and Technology, 13(2), pp. 173-183.
  • Loeve, M. (2012). Probability Theory I. Graduate Texts in Mathematics. New York: Springer-Verlag.
  • Orłowska-Kowalska, T. and Dybkowski, M. (2016). Industrial Drive Systems. Current State and Development Trends. Power Electronics and Drives, 1(1), pp. 5-25.
  • Rigatos, G. and Siano, P. (2012). Sensorless nonlinear control of induction motors using unscented Kalman filtering. In: IECON 2012 – 38th Annual Conference on IEEE Industrial Electronics Society. Montreal: IEEE, pp. 4654-4659.
  • Šlapák, V., Kyslan, K. and Ďurovský, F. (2016a). Position Controller for PMSM Based on Finite Control Set Model Predictive Control. Elektronika ir Elektrotechnika, 22(6), pp. 17-21.
  • Šlapák, V., Kyslan, K., Lacko, M., Fedák, V. and Ďurovský, F. (2016b). Finite Control Set Model Predictive Speed Control of a DC Motor. Mathematical Problems in Engineering, pp. 1-10.
  • Vantsevich, V. V. and Blundell, M. V. (2015). Advanced Autonomous Vehicle Design for Severe Environments. Amsterdam: IOS Press.
  • Yildiz, R., Barut, M. and Zerdali, E. (2016). Speed-sensorless induction motor drive with unscented Kalman filter including the estimations of load torque and rotor resistance. In: IECON 2016 – 42nd Annual Conference of the IEEE Industrial Electronics Society. Florence: IEEE, pp. 2946-2950.
  • Yildiz, R., Barut, M., Zerdali, E., Inan, R. and Demir, R. (2017). Load torque and stator resistance estimations with unscented Kalman filter for speed-sensorless control of induction motors. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP). Brasov: IEEE, pp. 456-461.
  • Zerdali, E. and Barut, M. (2017). The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors. IEEE Transactions on Industrial Electronics, 64(6), pp. 4340-4351.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ac9b136b-17ce-4bfa-9184-5a2186638077
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