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On-line losses minimization of induction motor vector control

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EN
Abstrakty
EN
Conventional field-orientated Induction motor drives operate at rated flux even at low load. To improve the efficiency of the existing motor it is important to regulate the flux of the motor in the desired operating range. In this paper a loss model controller (LMC) based on the real coded genetic algorithm is proposed, it has the straightforward goal of maximizing the efficiency for each given load torque. In order to give more accuracy to the motor model and the LMC a series model of the motor which consider the iron losses as a resistance connected in series with the mutual inductance is considered. Digital computer simulation demonstrates the effectiveness of the proposed algorithm and also simulation results have confirmed that this algorithm yields the optimal efficiency.
Rocznik
Strony
257--268
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
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autor
autor
autor
autor
Bibliografia
  • [1] Ramesh L.et al., Efficiency optimization of induction motor using a fuzzy logic based optimum flux search controller. IEEE International Conference on Power Electronics Drives and Energy Systems (PEDES06), 12-15 Dec., pp. 1-6 (2006).
  • [2] Branko Blanusa et al., An improved search based algorithm for efficiency optimization in the induction motor drives. XLII Konferencija, za ETRAN, Hercy-Novi (2003).
  • [3] Kirischen D.S., Novoty D.W., Lipo T.A., Optimal efficiency control of an induction motor drive. IEEE Trans. on Energy Conversion EC-2(1): 70-76, March (1987).
  • [4] Ghozzi S. et al., Energy optimization of induction motor drives. IEEE International Conference on Industrial Technology (ICIT), 8-10 Dec., 2: 602-610 (2004).
  • [5] Bose B.K., Power electronics and AC drives. Prentice Hall, USA (2002).
  • [6] Poirier E. et al., Loss minimization control of induction motor drives based on genetic algorithms. Electric Machines and Drives, 2001 IEEE International Conference, pp. 475-478 (2001).
  • [7] Jinchuan. Li et al., A new optimization method on vector control of induction motors. Electric Machines and Drives, 2005 IEEE International Conference, 15-18 May, pp. 1995-2001 (2005).
  • [8] Sheble G.B., Briting K., Refined genetic algorithm – economic dispatch example. IEEE Trans. on ower Systems, Feb., 10(1): 117-124 (1995).
  • [9] Golberg D.E., Genetic algorithms in search, optimization and machine learning reading. MA: Addisonn-Wisley, USA (1989).
  • [10] Murphy J.M.D., Honsinger U.B., Efficiency optimization of inverter-fed induction motor drives. Conf. Rec. IEEE Ind. Appl. Soc., pp. 544-552 (1982).
  • [11] Renders J.M., Genetic algorithms and neural network, Hermès, Paris (1995).
  • [12] She K.L. et al., Speed estimation of an induction motor drive using an optimized extended kalman filter. IEEE Transaction on Industrial Electronics, Feb., 49(1), pp. 124-133 (2002).
  • [13] Leigh J.R., Control theory. Second edition. The Institution of Electrical Engineers, London (2004).
  • [14] Zidani F. et al., Fuzzy efficient-optimization controller for induction motor. IEEE Power Engineering Review, October, pp. 43-44 (2000).
  • [15] Z. Rouabah et al., Optimal efficiency of a fuzzy controller in a field oriented control induction motor drive. 4th International Conference on Electrical Engineering CEE06, Batna, Algeria, 07-08 November, pp. 135-138 (2006).
  • [16] Dal. Y, Ohm, Dynamic model of induction motors for vector control. Drivetech, 17 Inc., Blacksburg, Virginia (2000).
  • [17] Mendes E., Razek A., A simple model foe core losses and magnetic saturation in induction machines adapted for DirectStatorFluxOrientationControl. IEE/PEVD’94Conference, pp 192-197 (1994).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS2-0063-0036
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