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access icon free Stator current model reference adaptive systems speed estimator for regenerating-mode low-speed operation of sensorless induction motor drives

The performance of a stator current-based model reference adaptive systems (MRAS) speed estimator for sensorless induction motor drives is investigated in this study. The measured stator currents are used as a reference model for the MRAS observer to avoid the use of a pure integrator. A two-layer, online-trained neural network stator current observer is used as the adaptive model for the MRAS estimator which requires the rotor flux information. This can be obtained from the voltage or current models, but instability and dc drift can downgrade the overall observer performance. To overcome these problems of rotor flux estimation, an off-line trained multilayer feed-forward neural network is proposed here as a rotor flux observer. Hence, two networks are employed: the first is online trained for stator current estimation and the second is off-line trained for rotor flux estimation. Sensorless operation for the proposed MRAS scheme using current model and neural network rotor flux observers are investigated based on a set of experimental tests in the low-speed region. Using a neural network rotor flux observer to replace the current model is shown to solve the stability problem in the low-speed regenerating mode of operation.

References

    1. 1)
      • 17. Zaky, M.S.: ‘Stability analysis of speed and stator resistance estimators for sensorless induction motor drives’, IEEE Trans. Ind. Electron., 2012, 59, (2), pp. 858870 (doi: 10.1109/TIE.2011.2161658).
    2. 2)
      • 33. Ohyama, K., Asher, G.M., Sumner, M.: ‘Comparative analysis of experimental performance and stability of sensorless induction motor drives’, IEEE Trans. Ind. Electron., 2006, 53, (1), pp. 178186 (doi: 10.1109/TIE.2005.862298).
    3. 3)
      • 7. Comanescu, M., Xu, L.: ‘Sliding mode MRAS speed estimators for sensorless vector control of induction machine’, IEEE Trans. Ind. Electron., 2006, 53, (1), pp. 146153 (doi: 10.1109/TIE.2005.862303).
    4. 4)
      • 24. Cirrincione, M., Pucci, M., Cirrincione, G., Capolino, G.A.: ‘Sensorless control of induction machines by a new neural algorithm: the TLS EXIN neuron’, IEEE Trans. Ind. Electron., 2007, 54, (1), pp. 127149 (doi: 10.1109/TIE.2006.888774).
    5. 5)
      • 5. Kubota, H., Matsuse, K., Nakano, T.: ‘DSP-based speed adaptive flux observer of induction motor’, IEEE Trans. Ind. Appl., 1993, 29, (2), pp. 344348 (doi: 10.1109/28.216542).
    6. 6)
      • 15. Maiti, S., Verma, V., Chakraborty, C., Hori, Y.: ‘An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement’, IEEE Trans. Ind. Inf., 2012, 8, (4), pp. 757766 (doi: 10.1109/TII.2012.2210229).
    7. 7)
      • 25. Cirrincione, M., Pucci, M., Cirrincione, G., Capolino, G.: ‘A new adaptive integration methodology for estimating flux in induction machine drives’, IEEE Trans. Power Electron., 2004, 19, (1), pp. 2534 (doi: 10.1109/TPEL.2003.820565).
    8. 8)
      • 21. Campbell, J., Sumner, M.: ‘Practical sensorless induction motor drive employing an artificial neural network for online parameter adaptation’, IEE Proc. Electr. Power Appl., 2002, 149, (4), pp. 255260 (doi: 10.1049/ip-epa:20020289).
    9. 9)
      • 20. Ben-Brahim, L., Tadakuma, S., Akdag, A.: ‘Speed control of induction motor without rotational transducers’, IEEE Trans. Ind. Appl., 1999, 35, (4), pp. 844850 (doi: 10.1109/28.777193).
    10. 10)
      • 14. Orlowska-Kowalska, T., Dybkowski, M.: ‘Stator-current-based MRAS estimator for a wide range speed-sensorless induction-motor drive’, IEEE Trans. Ind. Electron., 2010, 57, (4), pp. 12961308 (doi: 10.1109/TIE.2009.2031134).
    11. 11)
      • 30. Sobczuk, D., Grabowski, P.: ‘DSP implementation of neural network speed estimator for inverter fed induction motor’. Proc. 24th Annual Conf. IEEE Industrial Electronics Society, 1998 (IECON '98), 31 August–4 September 1998, vol. 2, pp. 981985.
    12. 12)
      • 28. Sangwongwanich, S., Suwankawin, S., Koonlaboon, S.: ‘A unified speed estimation design framework for sensorless AC motor drives based on positive-real property’. Power Conversion Conf. – Nagoya, 2007 (PCC '07), 2–5 April 2007, pp. 11111118.
    13. 13)
      • 8. Vieira, R., Gastaldini, C., Azzolin, R., Gründling, H.: ‘Discrete-time sliding mode speed observer for sensorless control of induction motor drives’, IET Electr. Power Appl., 2012, 6, (9), pp. 681688 (doi: 10.1049/iet-epa.2011.0269).
    14. 14)
      • 29. Gadoue, S.M., Giaouris, D., Finch, J.W.: ‘A neural network based stator current MRAS observer for speed sensorless induction motor drives’. Proc. IEEE Int. Symp. Industrial Electronics, Cambridge, UK, 2008, pp. 650655.
    15. 15)
      • 4. Ravi Teja, A.V., Chakraborty, C., Maiti, S., Hori, Y.: ‘A new model reference adaptive controller for four quadrant vector controlled induction motor drives’, IEEE Trans. Ind. Electron., 2012, 59, (10), pp. 37573767 (doi: 10.1109/TIE.2011.2164769).
    16. 16)
      • 19. Bose, B.K.: ‘Neural network applications in power electronics and motor drives – an introduction and perspective’, IEEE Trans. Ind. Electron., 2007, 54, (1), pp. 1433 (doi: 10.1109/TIE.2006.888683).
    17. 17)
      • 6. Ghanes, M., Zheng, G.: ‘On sensorless induction motor drives: slidingmode observer and output feedback controller’, IEEE Trans. Ind. Electron., 2009, 56, (9), pp. 34043413 (doi: 10.1109/TIE.2009.2026387).
    18. 18)
      • 9. Barut, M., Demir, R., Zerdali, E., Inan, R.: ‘Real-time implementation of bi input-extended Kalman filter-based estimator for speed-sensorless control of induction motors’, IEEE Trans. Ind. Electron., 2012, 59, (11), pp. 41974206 (doi: 10.1109/TIE.2011.2178209).
    19. 19)
      • 10. Jafarzadeh, S., Lascu, C., Fadali, M.: ‘State estimation of induction motor drives using the unscented Kalman filter’, IEEE Trans. Ind. Electron., 2012, 59, (11), pp. 42074216 (doi: 10.1109/TIE.2011.2174533).
    20. 20)
      • 13. Peng, F., Fukao, T.: ‘Robust speed identification for speed-sensorless vector control of induction motors’, IEEE Trans. Ind. Appl., 1994, 30, (5), pp. 12341240 (doi: 10.1109/28.315234).
    21. 21)
      • 11. Schauder, C.: ‘Adaptive speed identification for vector control of induction motors without rotational transducers’, IEEE Trans. Ind. Appl., 1992, 28, (5), pp. 10541061 (doi: 10.1109/28.158829).
    22. 22)
      • 31. Gadoue, S.M., Giaouris, D., Finch, J.W.: ‘An experimental assessment of a stator current MRAS based on neural networks for sensorless control of induction machines’. Proc. 2011 Symp. Sensorless Control for Electrical Drives, Birmingham, UK, 2011, pp. 102106.
    23. 23)
      • 3. Vas, P.: ‘Sensorless vector and direct torque control’ (Oxford University Press, New York, 1998).
    24. 24)
      • 27. Suwankawin, S., Sangwongwanich, S.: ‘A speed-sensorless IM drive with decoupling control and stability analysis of speed estimation’, IEEE Trans. Ind. Electron., 2002, 49, (2), pp. 444455 (doi: 10.1109/41.993278).
    25. 25)
      • 2. Holtz, J., Quan, J.: ‘Drift and parameter compensated flux estimator for persistent zero stator frequency operation of sensorless controlled induction motors’, IEEE Trans. Ind. Appl., 2003, 39, (4), pp. 10521060 (doi: 10.1109/TIA.2003.813726).
    26. 26)
      • 26. Gadoue, S.M., Giaouris, D., Finch, J.W.: ‘Sensorless control of induction motor drives at very low and zero speed using neural network flux observers’, IEEE Trans. Ind. Electron., 2009, 56, (8), pp. 30293039 (doi: 10.1109/TIE.2009.2024665).
    27. 27)
      • 22. Karanayil, B., Rahman, M.F., Grantham, C.: ‘Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drives’, IEEE Trans. Ind. Electron., 2007, 54, (1), pp. 167176 (doi: 10.1109/TIE.2006.888778).
    28. 28)
      • 12. Rashed, M., Stronach, A.F.: ‘A stable back-EMF MRAS-based sensorless low speed induction motor drive insensitive to stator resistance variation’, IEE Proc. Electr. Power Appl., 2004, 151, (6), pp. 685693 (doi: 10.1049/ip-epa:20040609).
    29. 29)
      • 18. Etien, E., Chaigne, C., Bensiali, N.: ‘On the stability of full adaptive observer for induction motor in regenerating mode’, IEEE Trans. Ind. Electron., 2010, 57, (5), pp. 15991608 (doi: 10.1109/TIE.2009.2032200).
    30. 30)
      • 1. Finch, J.W., Giaouris, D.: ‘Controlled AC electrical drives’, IEEE Trans. Ind. Electron., 2008, 55, (2), pp. 481491 (doi: 10.1109/TIE.2007.911209).
    31. 31)
      • 32. Ohyama, K., Asher, G.M., Sumner, M.: ‘Comparative experimental assessment for high-performance sensorless induction motor drives’. Proc. IEEE Int. Symp. Industrial Electronics (ISIE '99), 1999.
    32. 32)
      • 16. Guzinski, J., Abu-Rub, H.: ‘Speed sensorless induction motor drive with predictive current controller’, IEEE Trans. Ind. Electron., 2013, 60, (2), pp. 699709 (doi: 10.1109/TIE.2012.2205359).
    33. 33)
      • 23. Cirrincione, M., Pucci, M.: ‘An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model’, IEEE Trans. Ind. Electron., 2005, 52, (2), pp. 532551 (doi: 10.1109/TIE.2005.844247).
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