Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

• Autorzy: Sedhuraman K. , Himavathi S. , Muthuramalingam A.
• Języki publikacji: PL

Abstrakty

EN

This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

Słowa kluczowe

EN

sensorless indirect vector controlled IM drives; speed estimator; reactive power; MRAS; neural network; back propagation algorithm

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2013
• Tom: Vol. 62, nr 1
• Strony: 25--41
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Sedhuraman K.

autor

Himavathi S.

autor

Muthuramalingam A.

• Department of Electrical and Electronics Engineering Pondicherry Engineering College Puducherry-605014, India,

Bibliografia

• [1] Maiti S., Chakraborty C., Hori Y., Ta M.C., Model reference adaptive controller-based rotor resistanceand speed estimation techniques for vector controlled induction motor drive utilizing reactivepower. IEEE Trans. on Ind. Electron. 55(2): 594-601 (2008).
• [2] Jevremovic V.R., Vasic V., Marcetic D.P., Jeftenic B., Speed-sensorless control of induction motorbased on reactive power with rotor time constant identification. IET Electr. Power Appl. 4(6): 462-473 (2010).
• [3] Orlowska-Kowalska T., Dybkowski M., Stator-Current-Based MRAS Estimator for a Wide RangeSpeed-Sensorless Induction-Motor Drive. IEEE Trans. on Ind. Electron. 57(4): 1296-1308 (2010).
• [4] Peng F.Z., Fukao T., Robust speed identification for speed sensorless vector control of inductionmachines. IEEE Trans. on Industry Applications IA-30(5): 1234-1230 (1994).
• [5] Bose B.K., Modern Power Electronics and AC Drives. Prentice Hall of India (2005).
• [6] Holts J., Methods for Speed Sensorless Control of AC Drives. IEEE PCC, Yokohama, pp. 415-420 (1993).
• [7] Vas P., Sensorless Vector and Direct Torque Control. Oxford Science Publication (1998).
• [8] Schauder C., Adaptive Speed Identification for Vector Control of Induction Motors without RotationalTransducers. IEEE Trans. on Industry Applications 28(5): 1054-1061 (1992).
• [9] Ohtani T., Vector Control of Induction Motor without Shaft Encoder. IEEE Trans. on Industry Applications 28(1): 157-164 (1992).
• [10] Yang G., Chin T., Adaptive-Speed Identification Scheme for A Vector-Controlled Speed SensorlessInverter-Induction Motor Drive. IEEE Trans. on Industry Applications 29(4): 820-825 (1993).
• [11] Kubota H., Speed Sensorless Field-Oriented Control of Induction Motor with Rotor ResistanceAdaptation. IEEE Trans. On Industry Applications 30(5): 414-418 (1994).
• [12] Cirrincione M., Pucci M., An MRAS-Based Sensorless High-Performance Induction Motor DriveWith a Predictive Adaptive Model. IEEE Trans. On Industrial Electronics 52: 532-551 (2005).
• [13] Jansen P.L., Lorenz R.D., Transducer less field orientation concepts employing saturation-inducedsaliencies in induction machines. IEEE Trans. on Industry Applications 32: 1380-1393 (1996).
• [14] Karanayil M., Rahman M.F., Grantham C., Online Stator and Rotor Resistance Estimation SchemeUsing Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive. IEEE Trans. on Industrial Electronics 54(1): (2007).
• [15] Lazhar Ben-Brahim, Susumu Tadakuma, Alper Akdag, Speed Control of Induction Motor WithoutRotational Transducers. IEEE Trans. On Industry Applications 35(4): 844-850 (1999).
• [16] Chen T., Sheu T., Model Reference Neural Network Controller for Induction Motor Speed Control. IEEE Trans. on Energy Conversion 17(2), (2002).
• [17] Mondal S.K., Pinto J.O.P., Bose B.K., A neural network based space vector PWM controller fora three voltage fed inviter induction motor drive. IEEE Trans. On Industry Applications 30(3): 660-669 (2002).
• [18] Hurst K., Habetler T., Griva G., Profumo F., Speed sensorless field-oriented control of inductionmachines using current harmonic spectral estimation. IEEE Conference Record of Industry Applications Society Annual Meeting 1: 601-607 (1994).
• [19] Ferrah A., Hogben-Laing P., Bradley K., Asher G., Woolfson M., The effect of rotor design on sensorlessspeed estimation using rotor slot harmonics by adaptive digital filtering using the maximumlikelihood approach. Conference Proceedings of the IEEE IAS Annual Meeting, pp. 128-135, IEEE (1997).
• [20] Schroedl M., Sensorless control of ac machines at low speed and standstill based on the “inform”method. Conference Record of the31st Annual Meeting of the IAS, 1: 270-277, IEEE (1996).
• [21] Rashed M., Stronach A.F., A stable back-EMF MRAS-based sensorless low speed induction motordrive insensitive to stator resistance variation. Proc. Inst. Elect. Eng. Electr. Power Appl. 151(6): 685-693 (2004).
• [22] Narendra K., Part’msarathy K., Identification and control of dynamical system using neural network. IEEE Trans. On Neural Networks 1(1): 4-27 (1990).
• [23] Ham F.M., Kostanic I., Principles of Neurocomputing for Science & Engineering. Tata McGraw- Hill, Pvt. Ltd., India (2008).
• [24] Hagan M.T., Demuth H.B., Beale M., Neural Network Design. Cengage Learning, Pvt. Ltd., India (2008).