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Sensorless DTC of induction motor using improved neural network switching state selector controller

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper deals with development of sensorless Direct Torque Control (DTC) system based on neural network. This network is built to solve the task of proper switching states selection based on information about electromagnetic torque and stator flux (position and magnitude) of induction motor. In fact, this technique which uses conventional switching table is not convenient for one-line and real time control for its high computation time. In order to avoid this problem a solution based on neural network is proposed. Well trained Artificial Neural Network structure can replace successfully the switching table. However, in the Neutral-Point-Clamped topology, it has an inherent problem of Neutral Point Potential (NPP) variation. In this way, a Neural Network-Direct Torque Control technique has been applied and the estimated value of the Neutral Point Potential is used, which is calculated by motor currents. This control strategy offers the possibility of selecting appropriate switching state to achieve the control of Neutral Point Potential. Simulation results verify the validity of the proposed method.
Rocznik
Strony
435--456
Opis fizyczny
Bibliogr. 17 poz., rys., wzory
Twórcy
autor
autor
  • Laboratoire d'Instrumentation, Département d'Instrumentation et Automatique, Faculté d'Électronique et d'Informatique, Université des Sciences et de la Technologie Houari Boumediene, BP no 32 El-Alia 16111 Bab-Ezzouar, Alger, Algérie, imessaif@hotmail.com
Bibliografia
  • [1] V. PETER: Sensorless vector and direct torque control. Oxford university press, London, 1998.
  • [2] I. TAKAHASHI and T. NOGUSHI: A new quick-response and high-efficiency control strategy of induction motor. IEEE Trans. On. IA, 22(5), (1986), 820-827.
  • [3] F. BOUCHAFAA, A. TALHA, E.-M. BERKOUK and M.S. BOUCHERIT: Stabilization of DC link voltage using a clamping bridge in multilevel cascade. Int. Conf. On Sciences of Electronic Technologies of Information and Telecomunications, Tunis, March 27-31, (2005).
  • [4] P. PURKAIT and R.S. SRIRAMAKAVACHAM: A new generalized space vector modulation algorithm for neutral-point-clamped multilevel converters. Symp. On Progress in Electromagnetics Research, Cambridge, USA, (2006).
  • [5] J. HOLTZ and N. OIKONOMOU: Neutral point potential balancing algorithm at low modulation index for three-level inverter medium voltage drives. IEEE Trans. On Industry Applications, 43(3), (2007), 761-768.
  • [6] I. MESSA¨IF, E.-M. BERKOUK and N. SAADIA: Selection of voltage switching tables DTC of induction motor driven by three-level NPC VSI. The First Electrical Engineering Conf., Aleppo, Syria, 26-28 June, (2007).
  • [7] X. DEL TORO, S. CALLS, M.G. JAYNE, P.A. WITTING, A. ARIAS and J.L. POMERAL: Direct torque control of an induction motor using a three-level inverter and fuzzy logic. Int. Symp. on Industrial Electronics, 2, (2004), 923-927.
  • [8] I. MESSAIF, E.-M. BERKOUK and N. SAADIA: DTC strategy of an asynchronous motor fed by a photovoltaic multilevel voltage source inverter. The Second Int. Conf. on Nuclear and Renewable Energy Resources, Ankara, Turkey, 4-7 July (2010), 365-371.
  • [9] K. HUNT, D. SBARBARO, R. ZBIKOWSKI and P. GOWTHROP: Neural networks for control systems-A survey. Automatica, 28(6), (1992), 1083-1112.
  • [10] I. MESSAI F, E.-M. BERKOUK and N. SAADIA: Direct torque control for induction machines using neural networks. Archives of Control Sciences, 17(1), (2007), 5-16.
  • [11] A. L. MOHAMADEIN, R. HAMDY and M. GADOUE: A comparison between two direct torque control strategies for flux and torque ripple reduction for induction motors drives. Proc. of the Ninth Int. Middle East Power Systems Conf., (MEPCON’ 2003), Shebeen Al-Koum, Egypt, December 16-18, (2003).
  • [12] I. MESSA¨I F: Controle direct du couple d’une machine asynchrone alimentee par onduleurs multiniveaux par une approche classique et une approche neuronale. Equilibrage des tensions d’entr´ee des onduleurs. Ph.D thesis. Universit´e des Sciences et de la Technologie Houari Boumediene, Alger, 2009, (in French).
  • [13] N. CELANOVIC: Space vector modulation and control of multilevel converters. Ph.D. thesis. Faculty of the Virginia Polytechnic Dept., 2000.
  • [14] K. HORNIK: Multilayer networks are universal approximations. Neural networks, 2 (1989), 359-366.
  • [15] K. LEVENBERG: A method for the solution of certain problems in least squares. Quarterly of Applied Mathematics, 5 (1944), 164-168.
  • [16] D. MARQUARDT: An algorithm for least squares estimation of nonlinear parameters. SIAM J. on Applied Mathematics, 11 (1963), 431-441.
  • [17] 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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0073-0018
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