Speed estimation error of sensorless induction motor drives using soft computing technique
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Speed estimation error of sensorless induction motor drives using soft computing technique

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    Speed estimation error of sensorless induction motor drives using soft computing technique Speed estimation error of sensorless induction motor drives using soft computing technique Document Transcript

    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME13SPEED ESTIMATION ERROR OF SENSORLESS INDUCTIONMOTOR DRIVES USING SOFT COMPUTING TECHNIQUEHussain Shaik (1)S.Chaitanya (2)Asst. Prof, EEE Dept, VJIT, Hyderabad.Dr. Sardar Ali (3)Professor & Head of EEE Dept,RITS, ChevellaABSTRACTHigh-performance applications of sensor less systems require high accuracy of speedestimation over a wide speed range extending from very low and zero-speed operations to high speedsbeyond the rating. Parallel speed and stator resistance identification schemes of sensor less inductionmotor drives have been introduced to overcome the problem of resistance variation Stator terminalvoltages and currents are measured and filtered using analog circuitry Activation of the statorresistance adaptation mechanism quickly compensates the initial error in the estimated statorresistance value and therefore eliminates the initial speed estimation error. As a consequence, theactual and estimated speeds become in very good agreement. Low and zero-speed sensor lessoperation have also been investigated by the proposed SMO combined with the online statorresistance adaptation scheme.I. INTRODUCTIONSeveral methods have been recently proposed for speed estimation of sensor less inductionmotor drives. They can be classified into two major categories. The first one includes the techniquesthat estimate the rotor speed based on non-ideal phenomena such as rotor slot harmonics and highfrequency signal injection methods. Such methods require spectrum analysis, which, besides beingtime-consuming procedures, allows a narrow band of speed control. The second category of speedestimation methods relies on the model of the induction motor.Adaptive full-order flux observers (AFFOs) for estimating the speed and stator resistance aredeveloped using Lyapunov’s stability criterion. While these schemes are not computationallyintensive, an AFFO with a nonzero gain matrix may become unstable. An MRAS for estimating thespeed and stator resistance is developed using Popov’s stability criterion. Recently, two extendedKalman filter (EKF) algorithms for estimating stator and rotor resistances are utilized in a braidedINTERNATIONAL JOURNAL OF ADVANCED RESEARCH INENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online)Volume 4, Issue 3, April 2013, pp. 13-19© IAEME: www.iaeme.com/ijaret.aspJournal Impact Factor (2013): 5.8376 (Calculated by GISI)www.jifactor.comIJARET© I A E M E
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME14manner, thus achieving an accurate estimation of a high number of parameters and states than wouldhave been possible with a single EKF algorithm. The second category of online stator resistanceidentification schemes depends on artificial intelligence techniques in the process of stator resistanceadaptation. Artificial neural networks for estimating stator and rotor resistances are used for thispurpose. High-performance applications of sensor less systems require high accuracy of speedestimation over a wide speed range extending from very low and zero-speed operations to high speedsbeyond the rating. Operation of field-oriented induction motors below the base speed is usuallyachieved with constant flux.II. MODELING OF CASE STUDYA. Speed and stator resistance estimation procedure1. Construction of SMOThe induction motor can be represented by its dynamic model expressed in the stationaryreference frame in terms of stator current and rotor flux as follows:(1)Where A11, A12, A21, A22, and b1 are given in the Appendix. With reference to the introducedmathematical model and considering the stator currents as the system outputs, the sliding-modecurrent observer can be constructed as(2, 3)Where K is the switching gain. The error equation, which takes into account parameter variation, canbe expressed, by subtracting (1) from (2), as follows:(4)The sliding surface S is constructed as(5)(6)
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME15If rotor speed and stator resistance are considered as variable parameters, assuming no otherparameter variations, the matrix ∆A is expressed as followsThe sliding mode occurs when the following sliding condition is satisfied:(7)2. Characteristics of SMO on Sliding SurfaceWhen the estimation error trajectory reaches the sliding surface, i.e., S = 0, then, from (5), itis obvious that the observed currents will converge to the actual ones, i.e., I ss = iss. According to theequivalent control concept, assuming that the observed currents ˆI ss match the actual currents iss inthe steady state, then(8)from which the error equation becomes(9)A closed-loop observer constructs the estimation algorithm of stator currents, as in (2), whereas theestimation of rotor fluxes is carried out by an open loop represented by (3) without the flux error.Therefore, the real and estimated rotor fluxes are assumed the same ˆλ sr = λsr Thus, the errorequation becomes as follows:(10)3. Stability of the Identification SystemPopov’s hyper stability theory is well known as stability criterion for nonlinear feedbacksystems. This theory is applied here to examine the stability of the proposed identification system.This requires that the error system and the feedback system are derived so that the theory could beapplied. In the SMO, using a speed identification error ∆ω = ˆωr − ωr, a stator resistance identificationerror ∆RS = ˆRS − RS, and an error signal L = −K sgn (ˆI Ss − iss).III. ONLINE IDENTIFICATION OF MAGNETISING INDUCTANCEMagnetizing inductance of induction motors may vary significantly when the rotor flux referencevaries in the field-weakening region. Accurate speed estimation in this region requires the precisevalue of magnetizing inductance. Therefore, the structure of the speed observer should be modified insuch a way that the variation of main flux saturation is recognized within the speed estimationalgorithm. This requires the online identification algorithm of the magnetizing inductance. Themagnetizing inductance is given on the basis of the known magnetizing curve of the machine with
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME16(23)(24)(25,26)Since the magnetizing flux is now known, it is possible to estimate the magnetizing inductance usingthe known nonlinear inverse magnetizing curveFigure 2. Block diagram of experimental system(27, 28)The block diagram of online identification procedures of the magnetizing inductance is shown in Fig3. The magnetizing curve of the machine was identified offline, as described in [21], and isrepresented with a suitable analytical function in per unit, of the form(29)Coefficients a and b of (29) were determined as a = 0.9 and b = 7. The rated magnetizing current is0.85 A (rms), and the rated magnetizing flux is 0.84524 Wb (rms). The rated magnetizing inductancevalue is 0.9944 H.Using (29) and the given data, the required ˆLm is easily obtained.Figure 3 Block diagram of speed, stator resistance, and magnetizing inductance identificationschemes
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME17IV. SIMULATION DIAGRAMFigure 4 Speed estimation error for +20% Rs error in the observer at 150 rad/sV. RESULTSFigure 5 Speed estimation error for +20% Rs error in the observer at 150 rad/sFigure 6 Speed estimation error for +20% Rs error in the observer at 3 rad/s
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME18Figure 7 Speed estimation error for +20% Rs error in the observer at 150 rad/s (Fuzzy Logic)Figure 8 Speed estimation error for +20% Rs error in the observer at 3 rad/s(Fuzzy Logic)VI. CONCLUSIONIn this paper, parallel speed and stator resistance identification schemes of sensor lessinduction motor drives have been introduced to overcome the problem of resistance variation.Estimation algorithms have been obtained based on a sliding mode current observer combined withPopov’s hyper stability theory. It has been found that activation of the stator resistance adaptationmechanism quickly compensates the initial error in the estimated stator resistance value and thereforeeliminates the initial speed estimation error. As a consequence, the actual and estimated speedsbecome in very goodagreement.The speed estimation error at different reference speeds is estimated and observed that theerror decreased with decrease in the reference speed with PI control technique.This speed estimation error is further decreased with a different control technique i.e fuzzylogic with the same reference speeds.So, we conclude that the speed estimation error can b decreased with a better controltechnique i.e fuzzy logic rather the conventional PI control in this case.REFERENCES[1] J. Holtz, “Sensorless control of induction motor drives,” Proc. IEEE, vol. 90, no. 8, pp. 1359–1394, Aug. 2002.[2] M. S. Zaky, M. M. Khater, H. Yasin, S. S. Shokralla, and A. El-Sabbe, “Speed-sensor less controlof induction motor drives,” Eng. Res. J., vol. 30, no. 4, pp. 433–444, Oct. 2007.
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME19[3] Q. Gao, G. Asher, and M. Sumner, “Sensorless position and speed control of induction motorsusing high-frequency injection and without offline precommissioning,” IEEE Trans. Ind. Electron.,vol. 54, no. 5, pp. 2474– 2481, Oct. 2007.[4] H. Tajima, G. Guidi, and H. Umida, “Consideration about problems and solutions of speedestimation method and parameter tuning for speed sensor less vector control of induction motordrives,” IEEE Trans. Ind. Appl., vol. 38, no. 5, pp. 1282–1289, Sep./Oct. 2002.[5] J. Holtz and J. Quan, “Sensorless vector control of induction motors at very low speed using anonlinear inverter model and parameter identification,” IEEE Trans. Ind. Appl., vol. 38, no. 4, pp.1087–1095, Jul./Aug. 2002.[6] G. Guidi and H. Umida, “A novel stator resistance estimation method for speed-sensor lessinduction motor drives,” IEEE Trans. Ind. Appl., vol. 36, no. 6, pp. 1619–1627, Nov./Dec. 2000.[7] A. B. Proca, A. Keyhani, and J. M. Miller, “Sensorless sliding-mode control of induction motorsusing operating condition dependent models,” IEEE Trans. Energy Convers., vol. 18, no. 2, pp. 205–212, Jun. 2003.[8] A. B. Proca and A. Keyhani, “Sliding-mode flux observer with online rotor parameter estimationfor induction motors,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp. 716–723, Apr. 2007.[9] D. P.Marcetic and S. N. Vukosavic, “Speed-sensorless AC drives with the rotor time constantparameter update,” IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2618–2625, May 2007.[10] S. Maiti, C. Chakra borty, Y. Hori, and M. C. Ta, “Model reference adaptive controller-basedrotor resistance and speed estimation techniques for vector controlled induction motor drive utilizingreactive power,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 594–601, Feb. 2008.[11] G. Edelbaher, K. Jezernik, and E. Urlep, “Low-speed sensorless control of induction machine,”IEEE Trans. Ind. Electron., vol. 53, no. 1, pp. 120– 129, Feb. 2006.[12] H. M. Kojabadia and L. Changb, “Comparative study of pole placement methods in adaptive fluxobservers,” Control Eng. Pract., vol. 13, no. 6, pp. 749–757, Jun. 2005.[13] Pooja Agrawal and Ritesh Diwan, “Sensorless Control of Surface-Mount Permanent-MagnetSynchronous Motors”, International Journal of Electrical Engineering & Technology (IJEET),Volume 4, Issue 2, 2013, pp. 112 - 119, ISSN Print : 0976-6545, ISSN Online: 0976-6553.[14] Hemant chouhan, Ritesh Kumawat and Dr. H. K. Verma, “Comparative Analysis of Scalarand Vector Control Induction Machine Drive through Modeling and Simulation”,International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2, 2012,pp. 39 - 50, ISSN Print : 0976-6545, ISSN Online: 0976-6553.