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Tytuł artykułu

Rotor Resistance Estimator based on Virtual Current Sensor Algorithm for Induction Motor Drives

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article, model reference adaptive system (MRAS)-based estimator of a rotor resistance of an induction motor (IM) is presented. In contrast to the solutions known from the literature, the reference model of this estimator uses the measured values of the phase current and the adaptive part is a virtual current sensor. The article presents an accurate description of the algorithm taking into account the discrete equations for possible practical implementation in the microprocessor system. In the first step, the impact of motor parameters to stator current estimation quality in the adaptive model was checked. Subsequently, simulation tests of the proposed rotor resistance estimator were carried out for the field-oriented control of the induction motor drive system with a model of an induction motor with fixed parameters and an induction motor with a changing main inductance according to a magnetisation curve. The analysis of the estimator’s work showed its high efficiency and insensitivity to changes in the IM main inductance.
Wydawca
Rocznik
Strony
143--156
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
  • Department of Electrical Machines, Wroclaw University of Science and Technology, Drives and Measurements, Wybrzeze Wyspianskiego 27, 50-370, Wrocław, Poland
Bibliografia
  • Adamczyk, M. and Orlowska-Kowalska, T. (2019), Virtual Current Sensor in the Fault-Tolerant Field-Oriented Control Structure of an Induction Motor Drive. Sensors, 19(22), p. 4979.
  • Barut, M., Demir, R., Zerdali, E. and Inan, R. (2012). Real-Time Implementation of Bi Input Extended Kalman Filter Based Estimator for Speed Sensorless Induction Motor. IEEE Transactions on Industrial Electronics, 59(11), pp. 4197–4206.
  • Du, T. and Brdys, M. A. (1993), Implementation of extended Luenberger observers for joint state and parameter estimation of PWM induction motor drive, In: 1993 Fifth European Conference on Power Electronics and Applications, Brighton, UK, 13–16 September 1993.
  • Dybkowski, M. (2018), Universal Speed and Flux Estimator for Induction Motor. Power Electronics and Drives, 3(38), pp. 157–169.
  • Dybkowski, M. and Orlowska-Kowalska, T. (2013), Speed Sensorless Induction Motor Drive System with MRAS type Speed and Flux Estimator and Additional Parameter Identification, In: 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, Caen, France, 3–5 July, pp. 33–38.
  • Horváth, K. and Kuslits, M. (2018), Dynamic Performance of Estimator-based Speed Sensorless Control of Induction Machines Using Extended and Unscented Kalman Filters. Power Electronics and Drives, 3(38), pp. 129–144.
  • Kazmierkowski, M. P., Krishnan, R. and Blaabjerg, F. (2002), Control in Power Electronics—Selected Problems. Academic Press: Cambridge, MA, USA.
  • Levi, E., Sokola, M. and Vukosavic, S. N. (2000), A Method for Magnetizing Curve Identification in Rotor Flux Oriented Induction Machines. IEEE Transactions on Energy Conversion, 15(2), pp. 157–162.
  • Loron, L. and Laliberte, G. (1993), Application of the extended Kalman filter to parameters estimation of induction motors, In: 1993 Fifth European Conference on Power Electronics and Applications, Brighton, UK, 13–16 September 1993.
  • Mapelli, F. L., Bezzolato, A. and Tarsitano, D. (2012) A rotor resistance MRAS estimator for induction motor traction drive for electrical vehicles, In: 2012 XXth International Conference on Electrical Machines, Marseille, 2–5 September 2012, pp. 823–829.
  • Orlowska-Kowalska, T. (1989), Application of Extended Luenberger Observer for Flux and Rotor Time-Constant Estimation in Induction Motor Drives. IEE Proceedings D - Control Theory and Applications, 136(6), pp. 324–330.
  • Orlowska-Kowalska, T. (2003), Sensoless Induction Motor Drives. Wroclaw University of Technology Press: Wroclaw, Poland.
  • Orlowska-Kowalska, T. and Dybkowski, M. (2016), Industrial Drive Systems. Current State and Development Trends. Power Electronics and Drives, 1(1), pp. 5–25.
  • Toliyat, H. A., Levi, E. and Raina, M. (2003), A review of RFO induction motor parameter estimation techniques. IEEE Transactions on Energy Conversion, 18(2), pp. 271–283.
  • Zai, LC., DeMarco, C. L. and Lipo, T. A. (1992), An Extended Kalman Filter Approach to Rotor Time Constant Measurement in PWM Induction Motor Drives. IEEE Transactions on Industry Applications, 28(1), pp. 96–104.
  • Zerdali, E. and Barut, M. (2018), Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors. Power Electronics and Drives, 3(1), pp. 115–127.
  • Zorgani, Y. A., Jouili, M., Koubaa, Y. and Boussak, M. (2019), A Very-Low Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by Using MRAS Scheme, Power Electronics and Drives. 4(1), pp. 125–140.
  • Zorgani, Y. A., Koubaa, Y. and Boussak, M. (2010), Simultaneous estimation of speed and rotor resistance in sensorless ISFOC induction motor drive based on MRAS scheme, In: The XIX International Conference on Electrical Machines - ICEM 2010, Rome, Italy, 6–8 September 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5ef15b01-0bb9-417e-9bb4-611806d6478f
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