Direct torque control for induction machines using neural networks

• Autorzy: Messaif I. , Berkouk E-M. , Saadia N.
• Języki publikacji: EN

Abstrakty

EN

In this work, a novel switching vector selector in Direct Torque Control of an induction machine using Artificial Neural Network is studied. In the first part, we describe design of a speed sensor-less Direct Torque Control (DTC) strategy of an induction motor supplied by a two-level voltage source. For this, a conventional look up table is applied which improves the performances. Due to the high computation load, this technique is not convenient for an one-line and real-time control. Thus, a simplified method of choosing the output vector for two-level voltage source inverter-fed induction machine is proposed in the second part, and a novel switching vector selector using Artificial Neural Network (ANN) is trained under the tutor of the method mentioned above. The ANN receives attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of plant or process. In fact, when the stator flux and electromagnetic torque are different from theirs respective references, the output vector can be expediently acquired. Simulation results showed that the ANN structure can replace successfully the conventional look up table of the DTC.

Słowa kluczowe

EN

direct torque control; switching table; inverter; induction machine; neural network structure and training; Levenberg-Marquardt algorithm

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2007
• Tom: Vol. 17, no. 1
• Strony: 93--104
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Messaif I.

autor

Berkouk E-M.

autor

Saadia N.

• Universite des Sciences et de la Technomogie Houari Boumediene, Algiers, Algeria,

Bibliografia

• [1] J. FAIZ ET AL.: Direct torque control of induction motor for electric propulsion systems. Int. Elect. Power Syst Res., 51 (1999), 95-101.
• [2] I. MESSAIF, E-M BERKOUK and N. SAADIA: Controle Direct du Couple d'une Machine Asynchrone Alimentêe par un Onduleur a 3-Niveaux de Tension. Journees Tunisiennes de l'Electrotechnique et de l'Automatique, JTEA'06, Tunis, Tunisia, (2006).
• [3] L. A. CABRERA, M. E. ELBULUK and I. HUSAIN: Tuning the stator resistance of induction motors using artificial neural network. IEEE Trans. on PE, 12(5), (1997), 779-787.
• [4] K. HUNT, D. SBARBARO, R. ZBIKOWSKI and P. GOWTHROP: Neural networks for control systems - A survey. Alltornatica, 28(6), (1992), 1083-1112.
• [5] D. CASADEI, G. GRANDI, G. SERRA and A. TANI: Switching strategies in direct torque control of induction machines. ICEM 94, 2 (1994), 204-209.
• [6] L A. CABRERA, M. E. ELBULUK and D. S. ZINGER: Learning techniques to train neural networks as a state selector for inverter-fed induction machines using direct torque control. IEEE Trans. on PE, 12(5), (1997), 788-799.
• [7] K. HORNIK: Multilayer networks are universal approximations. Neural networks, 2 (1989), 359-366.
• [8] K. LEVENBERG: A method for the solution of certain problems in least squares. Quarterly of Applied Mathematics, 5 (1944), 164-168.
• [9] D. MARQUARDT: An algorithm for least squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics, 11 (1963), 431-441.
• [10] J. NOCEDAL and S. J. WRIGHT: Numerical optimization. Springer-Verlag, 1999.
• [11] D. MISHRA, A. YADAV, S. RAY and P. K. KALRA: Levenberg-Marquardt learning algorithm for integrate-and-fire neuron model. Neural Information Processing-Letters and Reviews, 9(2), (2005).