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

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

• 1.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 13 SPEED ESTIMATION ERROR OF SENSORLESS INDUCTION MOTOR DRIVES USING SOFT COMPUTING TECHNIQUE Hussain Shaik (1) S.Chaitanya (2) Asst. Prof, EEE Dept, VJIT, Hyderabad. Dr. Sardar Ali (3) Professor & Head of EEE Dept, RITS, Chevella ABSTRACT High-performance applications of sensor less systems require high accuracy of speed estimation over a wide speed range extending from very low and zero-speed operations to high speeds beyond the rating. Parallel speed and stator resistance identification schemes of sensor less induction motor drives have been introduced to overcome the problem of resistance variation Stator terminal voltages and currents are measured and filtered using analog circuitry Activation of the stator resistance adaptation mechanism quickly compensates the initial error in the estimated stator resistance value and therefore eliminates the initial speed estimation error. As a consequence, the actual and estimated speeds become in very good agreement. Low and zero-speed sensor less operation have also been investigated by the proposed SMO combined with the online stator resistance adaptation scheme. I. INTRODUCTION Several methods have been recently proposed for speed estimation of sensor less induction motor drives. They can be classified into two major categories. The first one includes the techniques that estimate the rotor speed based on non-ideal phenomena such as rotor slot harmonics and high frequency signal injection methods. Such methods require spectrum analysis, which, besides being time-consuming procedures, allows a narrow band of speed control. The second category of speed estimation methods relies on the model of the induction motor. Adaptive full-order flux observers (AFFOs) for estimating the speed and stator resistance are developed using Lyapunov’s stability criterion. While these schemes are not computationally intensive, an AFFO with a nonzero gain matrix may become unstable. An MRAS for estimating the speed and stator resistance is developed using Popov’s stability criterion. Recently, two extended Kalman filter (EKF) algorithms for estimating stator and rotor resistances are utilized in a braided INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING 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.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
• 2.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 14 manner, thus achieving an accurate estimation of a high number of parameters and states than would have been possible with a single EKF algorithm. The second category of online stator resistance identification schemes depends on artificial intelligence techniques in the process of stator resistance adaptation. Artificial neural networks for estimating stator and rotor resistances are used for this purpose. High-performance applications of sensor less systems require high accuracy of speed estimation over a wide speed range extending from very low and zero-speed operations to high speeds beyond the rating. Operation of field-oriented induction motors below the base speed is usually achieved with constant flux. II. MODELING OF CASE STUDY A. Speed and stator resistance estimation procedure 1. Construction of SMO The induction motor can be represented by its dynamic model expressed in the stationary reference 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 introduced mathematical model and considering the stator currents as the system outputs, the sliding-mode current observer can be constructed as (2, 3) Where K is the switching gain. The error equation, which takes into account parameter variation, can be expressed, by subtracting (1) from (2), as follows: (4) The sliding surface S is constructed as (5) (6)
• 3.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 15 If rotor speed and stator resistance are considered as variable parameters, assuming no other parameter variations, the matrix ∆A is expressed as follows The sliding mode occurs when the following sliding condition is satisfied: (7) 2. Characteristics of SMO on Sliding Surface When the estimation error trajectory reaches the sliding surface, i.e., S = 0, then, from (5), it is obvious that the observed currents will converge to the actual ones, i.e., I ss = iss. According to the equivalent control concept, assuming that the observed currents ˆI ss match the actual currents iss in the 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 the estimation 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 error equation becomes as follows: (10) 3. Stability of the Identification System Popov’s hyper stability theory is well known as stability criterion for nonlinear feedback systems. 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 be applied. In the SMO, using a speed identification error ∆ω = ˆωr − ωr, a stator resistance identification error ∆RS = ˆRS − RS, and an error signal L = −K sgn (ˆI Ss − iss). III. ONLINE IDENTIFICATION OF MAGNETISING INDUCTANCE Magnetizing inductance of induction motors may vary significantly when the rotor flux reference varies in the field-weakening region. Accurate speed estimation in this region requires the precise value of magnetizing inductance. Therefore, the structure of the speed observer should be modified in such a way that the variation of main flux saturation is recognized within the speed estimation algorithm. This requires the online identification algorithm of the magnetizing inductance. The magnetizing inductance is given on the basis of the known magnetizing curve of the machine with
• 4.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 16 (23) (24) (25,26) Since the magnetizing flux is now known, it is possible to estimate the magnetizing inductance using the known nonlinear inverse magnetizing curve Figure 2. Block diagram of experimental system (27, 28) The block diagram of online identification procedures of the magnetizing inductance is shown in Fig 3. The magnetizing curve of the machine was identified offline, as described in [21], and is represented 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 is 0.85 A (rms), and the rated magnetizing flux is 0.84524 Wb (rms). The rated magnetizing inductance value 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 identification schemes
• 5.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 17 IV. SIMULATION DIAGRAM Figure 4 Speed estimation error for +20% Rs error in the observer at 150 rad/s V. RESULTS Figure 5 Speed estimation error for +20% Rs error in the observer at 150 rad/s Figure 6 Speed estimation error for +20% Rs error in the observer at 3 rad/s
• 6.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 18 Figure 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. CONCLUSION In this paper, parallel speed and stator resistance identification schemes of sensor less induction 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 with Popov’s hyper stability theory. It has been found that activation of the stator resistance adaptation mechanism quickly compensates the initial error in the estimated stator resistance value and therefore eliminates the initial speed estimation error. As a consequence, the actual and estimated speeds become in very good agreement. The speed estimation error at different reference speeds is estimated and observed that the error 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 fuzzy logic with the same reference speeds. So, we conclude that the speed estimation error can b decreased with a better control technique 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 control of induction motor drives,” Eng. Res. J., vol. 30, no. 4, pp. 433–444, Oct. 2007.
• 7.
  International Journal ofAdvanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, April (2013), © IAEME 19 [3] Q. Gao, G. Asher, and M. Sumner, “Sensorless position and speed control of induction motors using 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 speed estimation method and parameter tuning for speed sensor less vector control of induction motor drives,” 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 a nonlinear 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 less induction 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 motors using 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 estimation for 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 constant parameter 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-based rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive 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 flux observers,” Control Eng. Pract., vol. 13, no. 6, pp. 749–757, Jun. 2005. [13] Pooja Agrawal and Ritesh Diwan, “Sensorless Control of Surface-Mount Permanent-Magnet Synchronous 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 Scalar and 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.