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Displacement Self-sensing of Bearingless Switched Reluctance Motors Based on LS-SVM

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PL
Zastosowanie metody LS-SVM w określaniu przemieszczenia w bezłożyskowym silniku o przełączanej reluktancji BSRM
Języki publikacji
EN
Abstrakty
EN
To achieve the rotor radial displacement self-sensing for a bearingless switched reluctance motor (BSRM), a new displacement estimation method using least squares support vector machine (LS-SVM) was proposed. Firstly, the working principle and mathematic of a 3-phase 12/8 pole BSRM was introduced in brief. Then taking advantage of LS-SVM with better solution for small-sample learning problem and strong generalization ability, two LS-SVMs were trained off-line to obtain two efficient nonlinear mapping structures to express the dynamic behavior of BSRM. The LSSVM training data set is comprised of representative experimental data with current {i | i = (isa1, isa2, ima)} and rotor position θ as inputs and the corresponding displacements {D | D=(α , β )}as outputs. As well as giving a detailed explanation of the new method, simulation and experimental results were presented. It shows that the proposed LS-SVM-based displacement self-sensing method has high precision and operation efficiency.
PL
W artykule przedstawiono uczący się estymator przesunięcia dla bezłożyskowego silnika o przełączanej reluktancji (BSRM), wykorzystujący metodę LS-SVM (ang. Least Square Support Vector Machines). Opisano zasadę działania i model matematyczny silnika BSRM 3- fazowego 12/8 biegunowego. W celu uzyskania efektywnej struktury mapowania nieliniowego do określenia stanów dynamicznych, zastosowano dwa algorytmy, które zostały nauczone offline. Estymator poddano weryfikacji symulacyjnej i eksperymentalnej.
Rocznik
Strony
310--313
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
autor
autor
autor
  • School of Electrical and Information Egineering, Jiangsu University, Zhenjiang 212013 China, ujszzy@gmail.com
Bibliografia
  • [1] L. Chen, W. Hofmann. Speed regulation technique of bearingless 8/6 switched reluctance motor with simpler single winding structure, IEEE Trans. Ind. Electron., 59(2012):2592-2600.
  • [2] F. C. Lin, S. M. Yang. Self-Bearing Control of a switched reluctance motor using sinusoidal currents, IEEE Trans. Power Electron., 22(2007): 2518 -2526.
  • [3] C. R. Morrison, M. W. Siebert, E. J. Ho. Electromagnetic forces in a hybrid magnetic-bearing switched-reluctance motor, IEEE Trans. Magn.,44(2008): 4626-4638.
  • [4] M. Takemoto, A. Chiba, H. Akagi. Radial force and torque of a bearingless switched reluctance motor operating in a region of magnetic saturation, IEEE Trans. Ind. App., 40(2004): 103-112.
  • [5] X. Cao, Z. Deng, G. Yang, Y. Yang. Mathematical model of bearingless switched reluctance motors based on maxwell stress tensor method, Proceedings of the CSEE, 29(2009): 78-83.
  • [6] Y. Sun, J Wu, Q. Xiang, The mathematic model of bearingless switched reluctance motor based on the finiteelement analysis, Proceedings of the CSEE, 27(2007): 33-40.
  • [7] M. Takemoto, A. Chiba, T. Fukao. A method of determining the advanced angle of square-wave currents in bearingless switched reluctance motor, IEEE Trans. Ind. Appl., 37 (2001):1702-1709.
  • [8] X. Cao, Z. Deng, G. Yang, X. Wang, Independent control of average torque and radial force in bearingless switchedreluctance motors with hybrid excitations, IEEE Trans. Power Electron., 24(2009): 1376-1385.
  • [9] Y. Yang, Z. Deng, G. Yang, X. Cao. A control strategy for bearingless switched-reluctance motors, IEEE Trans. Power Electron., 25(2010):2807-2819.
  • [10] X. Cao, Z. Deng. A full-period generating mode for bearingless switched reluctance generators, IEEE Trans. Appl. Supercond., 20(2010):1072-1076.
  • [11] T. Kuwajima, T. Nobe, K. Ebara, A. Chiba, and T. Fukao, An estimation of the rotor displacements of bearingless motors based on a high frequency equivalent circuits, Proc. IEEE Power Electro. Drive Syst., 2(2001):725-731.
  • [12] T. Tera, Y. Yamauchi, A. Chiba. Performances of bearingless and sensorless induction motor drive based on mutual inductances and rotor displacements estimation, IEEE Trans. Power Electron, 53(2006):187-194.
  • [13] T. Mizuno, K. Araki, H. Bleuler. Stability analysis of selfsensing magnetic bearing controllers, IEEE Trans. Contr. Syst. Technol., 4(1996): 572-579.
  • [14] Y. Sun, Y. Zhou, X. Ji. Decoupling control of bearingless switched reluctance motor with neural network inverse system method, Proceeding of the CSEE, 31(2011): 117-123.
  • [15] C. A. Hudson, N. S. Lobo, R. Krishnan. Sensorless control of single switch-based switched reluctance motor drive using neural network, IEEE Trans. Ind. Electron,55(2008):321 - 329.
  • [16] E. Mese and D. A. Torrey, An approach for sensorless position estimation for switched reluctance motors using artifical neural networks, IEEE Trans. Ind. Electron, 17(2002): 66-75.
  • [17] V. Vapnik. New York: Springer-Verlag, (1999).
  • [18] J. A. K. Snykens, European Journal of Control, 7(2001).
  • [19] C. Xia, Z. He, Y. Zhou, X. Xie. Rotor position estimation for switched reluctance motors based on support vector machine Trans. China electro-technical Society, 22(2007):12-17.
  • [20] Z. Zhu, Y. Sun. Rotor displacement estimation for MB sensorless control, Przegląd Elektrotechniczny, 88(2012):141-145.
  • [21] Z. Zhu, Y. Sun, Y. Huang. Inverse dynamics modeling and control for bearingless switched reluctance motor, Electric Mach. and Contr., 15(2011):74-79.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS1-0050-0092
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