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Non-Linear Inductance Modeling of Switched Reluctance Machine Using Multivariate Non-Linear Regression Technique and Adaptive Neuro Fuzzy Inference System

D. Susitra, S. Paramasivam

Abstract


This paper presents two different methods of real time applicable modeling techniques for the Non-linear inductance model of a Switched reluctance Machine (SRM). These methods are based on Multivariate nonlinear regression technique (MVNLRT) and Adaptive Neuro fuzzy inference system (ANFIS). These techniques are applied for the nonlinear inductance calculation by using the magnetization characteristics of SRM. MVNLRT is an excellent solution for nonlinear modeling and its real time implementations. Similarly ANFIS also has a strong nonlinear approximation ability which could be used for nonlinear modeling and its real time implementations. In this paper, the best features of MVNLRT and ANFIS are utilized to develop the computationally efficient inductance model for SRM. Mathematical models for the phase Inductance L(I,θ) using MVNLRT and ANFIS have been successfully arrived, tested and presented for various values of phase currents(Iph) and rotor positions(θ) of a non linear SRM. It is observed that MVNLRT and ANFIS are highly suitable for Inductance L(I,θ) modeling of SRM which is found to be in good agreement with the training data used for modeling.


Keywords


Non-Linear Inductance Model, Multivariate Non-Linear Regression Technique (MVNLRT), Adaptive Neuro-Fuzzy Inference System (ANFIS), Switched Reluctance Machine (SRM).

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References


R.Krishnan.Switched reluctance motor drives. Modeling, Simulation, analysis, design and Applications Boca Raton FL:CRC.2001.

Osamu Ichinokura, Tsukasa Kikuchi, Kanji Hackamore, Tadakki Watanabe and Hai-Jiao Guo, Dynamic simulation model of switched reluctance generator, IEEE trans. Magnetics, Vol. 39, No. 5, pp.3253-3255, September 2003

Loop, B. Essah, D. N. Sudhoff, S., A basis function approach to the nonlinear average value modelling of switched reluctance machines IEEE Transaction on energy conversion, 2006, VOL 21; no 1, pages 60-68.

Radimov.N, Ben-Hail.N and Rabinovici.R,A Simple Model of Switched-Reluctance Machine Based Only on Aligned and Unaligned Position Data, IEEE IEEE Transactions on Magnetics, 2004, vol. 40,issue 3, pp. 1562-1572.

Loop.B.P, Sudhoff.S.D, Switched reluctance machine model using inverse inductance charecterization, IEEE Transaction on industry applications, June 2003,Vol.39, issue.3, pp.743-751.

Hongwei Gao, Farzad Rajaei salmasi and Ehsani Mehrdad, Inductance model-based sensorless control of the switched reluctance motor drive at low speed, IEEE transactions on power electronics, 2004, vol. 19, no 6, pp. 1568-1573.

Edrington, C.S.Fahimi and B.Krishnamurthy.M, An autocalibrating inductance model for Switched reluctance motor drives, IEEE Transaction on Industrial Electronics,2007,vol.54, pp.2165-2173.

Abelardo Martinez, E duardo Laloya, Javier Vicuna, Francisco perez, Tomas Pollan, Bonifacio Martin, Beatriz Sanchez and Juan Llado, Simulation model of an AC autonomous Switched reluctance generator, EUROCON 2007 - The International Conference on Computer as a Tool (EUROCON 2007), vol.2

Wen ding, Deliang Liang, Fourier series and ANFIS based modelling and prediction for switched reluctance motor, International conference on electrical machines and systems,ICEMS 2008.

Hujjan Zhou,Wen Ding and Zhenmin Yu, A nonlinear model for the Switched reluctance motor, Proc. of the 8th International conference on Electrical machines and systems, ICEMS sep 2005, vol.1, pp.568-571.

Hoang Le-Huy and Patrice Brunelle , A versatile Nonlinear Switched reluctance Motor model in simulink using realistic and analytical magnetization characteristics.

Vladan Vujicic and Slobodan.N, A simple nonlinear model of the switched reluctance motor, IEEE Transaction on energy conversion,Dec 2000,vol.15,No.4.

Vladan Vujicic and Slobodan.N, A simple nonlinear model of the switched reluctance motor, IEEE Transaction on energy conversion, Dec 2000, vol.15, No.4.

Shoujan Song and Weiguo Liu, A novel method for nonlinear modeling and dynamic simulation of a four -phase switched reluctance generator system based on MATLAB SIMULINK, in Proc. 2007 Second IEEE conference on Industrial Electronics and Applications, pp. 1509-1514

Ustun.O, Measurement and Real-Time Modeling of Inductance and Flux Linkage in Switched Reluctance Motors, IEEE Transactions on Magnetics, vol. 45, issue 12, pp. 5376-5382.

Wenzhe Lu, Ali Keyhani and Abbas Fardoun, Neural network based modelling and parameter identification of switched reluctance motors, IEEE Transaction on energy conversion, june 2003, vol.18, no.2

Zhengyu Lin, Doanld S. Reay, Barry.W.Williams and Xiangning He,Online modelling for switched reluctance motors, IEEE Transactions on Industrial electronics,dec 2007,vol.54,no.6.

Xiao Wen-Ping and Ye Jia-wei, Improved PSO-BPNN algorithm for SRG modelling, International Conference on Industrial Mechatronics and Automation, 2009. ICIMA 2009. Volume, Issue, 15-16 May 2009Page(s):245 – 248

Vejjan.R, Gobbi.R and Sahoo.N.C, Polynomial neural network based modeling of Switched reluctance motors, Power and energy society general meeting-Conversion and delivery of electrical energy in 21st century, July 2008, pp.1-4.

Elmas.C, Sagiroglu.S, Colak.I and Bal.G, Modeling of non-linear switched reluctance drive based on artificial neural networks, 5th international conference on Power electronics and variable speed drives, Oct 1994, pp.7-12.

Yan cai and Chao Gao, Non-linear modelling of switched reluctance drive based on BP neural networks, Proc. of the 3rd international conference on natural computation, 2007, vol.01, pp.232-236.

Oguz Ustun, A nonlinear full model for switched reluctance motor with artificial neural networks, Energy conversion and management, sep 2009,vol.50, pp.2413-2421.

Hexu Sun, yanqing Mi,Yan Dang, Yi Zheng and Yanzong Dong, The nonlinear modelling of switched reluctance motor with improved RBF network, second international conference on Intelligent networks and Intelligent systems, 2009.

Wen Ding Deliang Liang, Modeling of a 6/4 Switched Reluctance Motor using adaptive neural fuzzy inference system, IEEE Transactions on Magnetics, July 2008,vol:44, issue:7, pp-1796-1804.

Ferhat Daldaban, Nurettin Ustkoyuncu and Kerim Guney, Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system , July 2005,Science Direct

Lachman.T, Mohamad.T.R., Fong.C.H, Nonlinear model of Switched reluctance motor using artificial intelligence techniques, IEEE proc. of electric power applications, Jan 2004, vol.151, issue.1, pp.53-60.

S.Paramasivam, S.vijayan, M.Vasudevan,R.Arumugam and R.Krishnan, Real time verification of AI based rotor position estimation techniques for a 6/4 pole switched reluctance motor drive, IEEE Trans.Magn.,Vol.43,no.7, pp.3209-3222, Jul.2007.

J.S.R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Systems, Man, Cybernetics, 23(5/6):665-685, 1993

J.S.R. Jang and C.-T. Sun, Neuro-Fuzzy Modeling and Control, Proc. of the IEEE, 83(3):378-406 The Fuzzy Logic Toolbox for use with MATLAB, J.S.R. Jang and N. Gulley, Natick, MA: The MathWorks Inc.,1995

Switched reluctance motor Design and Simulation software by Peter Omand Rasmussen


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