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Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016) © Scholarlink Research Institute Journals, 2011 (ISSN: 2141-7016) jeteas.scholarlinkresearch.org Sensorless Control of Induction Motor Drive Using SVPWM - MRAS Speed Observer K. Suman and V. Aditya C.V.S.R College of Engineering, Hyderabad, India Corresponding Author: K. Suman ___________________________________________________________________________ Abstract This paper proposes a novel Space Vector Pulse width modulation (SVPWM) for sensor less control of induction motor using model reference adaptive system (MRAS). The steady state ripples in the torque are present in the conventionally used MRAS sensor less control of induction motor which utilizes normally used voltage source inverters. Also performance of the steady state speed is not as perfect as required having disturbances in steady state region. Hence to improve the performance of MRAS based speed observer a novel method of SVPWM based on reference voltage vector that utilizes the control variables as stator flux components is proposed. By using the proposed SVPWM control of induction motor the speed disturbances which are obtained are minimized and the speed performance is improved. Also the ripples present in the electromagnetic torque are reduced. This is proved by the simulation results for conventional MRAS speed observer and proposed SVPWM based MRAS speed observer. __________________________________________________________________________________________ Keywords: sensor less control, model reference adaptive system, reference voltage vector, SVPWM __________________________________________________________________________________________ ITRODUCTIO Induction motors have been widely used in high Simulations. The studies include the level of performance ac drives, requiring information. difficulties in tuning the adaptive Gain constants and Introducing a shaft speed sensor decreases system the tracking performances of both speed estimators. To reliability, and different solutions for sensor less ac obtain accurate estimation of the speed, a simple ondrives have been proposed. The MRAS speed line identification approach has been incorporated. estimators are the most attractive approaches (Hori and Based on the theory of MRAS, simultaneous estimation Uchida, 1991) due to their design simplicity. The of rotor speed has been described in this paper. MRAS is based on principle, in which the outputs of Comparing to other adaptation techniques, this method two models –one independent of the rotor speed is simple and needs a low computation power and has a (reference model) and the other dependent (adjustable high speed adaptation even at zero speeds. This method model)- are used to form an error vector. The error because eliminates the produced error in the speed vector is driven to zero by an adaptation mechanism adaptation, is more stable and robust. Computer which yields the estimated rotor speed. Depending on simulations results are presented to show its the choice of output quantities that form the error effectiveness. vector, several MRAS structures are possible. The most common MRAS structure is that based on the rotor flux In SVPWM methods, the voltage reference is provided error vector (Holtz 1996) which provides the advantage using a revolving reference vector. In this case of producing rotor flux angle estimate for the field- magnitude and frequency of the fundamental orientation scheme. Other MRAS structures have also component in the line side are controlled by the been proposed recently that use the back EMF and the magnitude and frequency, respectively, of the reference reactive power as the error vectors estimators. It is the voltage vector. Space vector modulation utilizes dc bus intention of this paper, therefore, to present a direct voltage more efficiently and generates less harmonic comparison between two different MRAS approaches: distortion in a three phase voltage source inverter. the rotor flux based and the back EMF based MRASS (Lassaâd and Ben Hamed, 2007). The rotor flux based Space Vector Pulse Width Modulation MRAS approach studied in this paper basically follows Space Vector Modulation (SVM) was originally that of, while the back EMF based approach is the developed as vector approach to Pulse Width modified form of the one developed in. To allow for a Modulation (PWM) for three phase inverters. It is a fair comparison, no on-line parameter methods will be more sophisticated technique for generating sine incorporated and same current controllers and a PWM wave that provides a higher voltage to the motor with generation technique will be used in both approaches. lower total harmonic distortion. The main aim of any In order to compare the performances of the two modulation technique is to obtain variable output estimators. Several performance measures are evaluated having a maximum fundamental component with in computer minimum harmonics. Space Vector PWM (SVPWM) 509 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016) t method is an advanced; computation intensive PWM method and possibly the best techniques for variable frequency drive application (Erfidan et al., 2004). equivalent to an orthogonal projection of [a b c] onto the two-dimensional perpendicular to the vector [1 1 t 1] (the equivalent d-q plane) in a three-dimensional coordinate system. As a result, six non-zero vectors and two zero vectors are possible. Six non-zero vectors (V -V ) shape the axes of a hexagonal as The space vector concept, which is derived from the rotating field of induction motor, is used for modulating the inverter output voltage. In this modulation technique the three phase quantities can be transformed to their equivalent two-phase quantity either in synchronously rotating frame (or) stationary frame. From these two-phase components, the reference vector magnitude can be found and used for modulating the inverter output. The process of obtaining the rotating space vector is explained in the following section, considering the stationary reference frame. Considering the stationary reference frame let the three-phase sinusoidal voltage component be, (1) V = V Sinωt a (2) V = V Sin(ωt-4π/3) (3) c m m 6 0 V ) and are at the origin and apply zero voltage to the 7 load. The eight vectors are called the basic space vectors and are denoted by (V , V , V , V , V , V , 0 1 2 3 4 5 V , V ). The same transformation can be applied to 6 7 the desired output voltage to get the desired reference voltage vector in the d-q plane. The objective of ref SVPWM technique is to approximate the reference voltage vector V using the eight switching patterns. m V = V Sin(ωt-2π/3) b 1 depicted in Figure-2, and supplies power to the load. The angle between any adjacent two non-zero vectors is 60 degrees. Meanwhile, two zero vectors (V and ref One simple method of approximation is to generate the average output of the inverter in a small period T to be the same as that of V in the same period ref When this three-phase voltage is applied to the AC machine it produces a rotating flux in the air gap of the AC machine. This rotating resultant flux can be represented as single rotating voltage vector. The magnitude and angle of the rotating vector can be found by means of Clark’s Transformation as explained below in the stationary reference frame. To implement the space vector PWM, the voltage the stationary dq reference frame that consists of the horizontal (d) and vertical (q) axes as depicted in Figure-1. From Figure-1, the relation between these two reference frames is below Figure-2. Basic switching, vectors and sectors Switching States For 180° mode of operation, there exist six switching states and additionally two more states, which make all three switches of either upper arms or lower arms ON. To code these eight states in binary (one-zero 3 representation), it is required to have three bits (2 = 8). And also, as always upper and lower switches are commutated in complementary fashion, it is enough to represent the status of either upper or lower arm switches. In the following discussion, status of the upper bridge switches will be represented and the lower switches will it’s complementary. Let "1" denote the switch is ON and "0" denote the switch in OFF. Table-1 gives the details of different phase and line voltages for the eight states (Rathnakumar et al,. 2005) Figure-1. The relationship of abc reference fram and stationary dq reference frame. and f denotes either a voltage or a current variable. As described in Figure-1. This transformation is 510 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016) MRAS Based On Rotor Flux-Linkage Estimation The proposed MRAS is using state observer model with current error feedback and rotor current model as two models for flux estimation. Figure 5 shows the block diagram of the proposed MRAS. The reference model is given by: The adjustable model is given by: Table-1. Switching patterns and output vectors. Model Reference Adaptive System The Model Reference Adaptive Systems (MRAS) approach uses two models. The model that does not Involve the quantity to be estimated (the rotor speed re in our case) is considered as the reference model. The model that has the quantity to be estimated involved is considered as the adaptive model (or adjustable model). The output of the adaptive model is compared with that of the reference model, and the difference is used to drive a suitable adaptive mechanism whose output is the quantity to be estimated (rotor speed in our case). The adaptive mechanism should be designed to assure the stability of the control system. Figure 3 illustrates the basic structure of MRAS. Different approaches have been developed using MRAS, such as rotor-flux-linkage estimation-based MRAS, back-EMF based MRAS (reactive-power-based MRAS) (Lassaâd and Ben Hamed, 2007). The Overall system of the proposed sensor less control algorithm is shown in Figure 2. Where estimated values of rotor fluxes in state observer model estimated values of rotor fluxes in rotor current model. Rotor speed is obtained from the adaptation mechanism as follows(Marwali and Ali Keyhani, 1997) The presence of the pure integrators brings the problems of initial conditions and drift. In (Marwali and Ali Keyhani, 1997), To reduce the effect of the derivative terms, a similar approach as that used to eliminate the pure integration problem a low pass filter was used to replace the pure integrator, but the performance in the low speed range is not satisfying, for reasons which will be explained later. Figure 3. Block diagram of the proposed MRAS Figure 5. MRAS based on rotor Flux-linkage estimation MRAS Based on Back-EMF Estimation This paper proposes a novel sensor less control algorithm based on the MRAS for the speed sensor less control of a induction motor. The proposed MRAS is using the state observer model of and the magnet flux model of and as two models for the back-EMF estimation. The rotor speed is generated from the adaptation mechanism using the error Figure 4. Configuration of overall system 511 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016) between the estimated quantities obtained by the two models as follows in Figure.6 (Toufouti, Meziane, Benalla, 2006). The reference model is given by: ITERPRETATIO OF RESULTS To validate the performances of the proposed controller, we provide a series of simulations and a comparative study between the performances of the proposed control strategy. A 1.5kW induction motor with controller is simulated using the nonlinear controller The results with the above scheme in steady state operation are shown below. It shows the behavior of the Electromagnetic torque, Stator current, THD response of stator current at steady state The adjustable model is given by: The adaptive mechanism is given by : Where Ki, and Kp, are the gain constants, i, and p are the estimated values of back-EMF in the state observer model. Figure 7 shows the block diagram of the proposed MRAS algorithm has a robust performance through combining the state observer model and the magnet flux model. a)Electromagnetic torque Figure 6: The MRAS speed observer This scheme does not have pure integrators in the Reference model. b) stator current Figure 7: MRAS based on back –EMF c) THD response of stator current 512 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016) It can be observed from the results that the value of Total harmonic Distortion (THD) is just 24.58% which is very low. So by using SVPWM–MRAS speed Observer technique Total Harmonic Distortion is also minimized T. Erfidan, S. Urugun, Y. Karabag and B. Cakir. 2004. New Software implementation of the Space Vector Modulation. Proceedings of IEEE Conference. pp.1113-1115. Y. Hori C. Ta, T. Uchida (1991): MRAS-based speed Sensor less control for induction motor drives using instantaneous reactive power. IECON, Pp. 1417-1422 COCLUSIO This work has dealt with the sensor less control of induction motor with svpwm. Its principles and basic concepts have been introduced and thoroughly explained. This paper is focused on the analysis of SVPWM-MRAS speed control schemes. The simulation shows a SVPWM-MRAS has better performance. Zhou K., Wang D., Relationship Between SpaceVector Modulation and Three Phase Carrier-Based PWM: A Comprehensive Analysis, IEEE Transactions on Industrial Electronics, Vol. 49, No. 1, February 2002, page 186-196 REFERECES D. Rathnakumar, J. Lakshmana Perumal and T. Srinivasan. 2005. A New software implementation of space vector PWM. Proceedings of IEEE Southeast conference. pp.131-136. D.W. Jin Y.A. Kwon. (1999): A novel MRAS based speed sensorless control of induction motor. IECON, 2:933-938. J. Holtz, (1996): “Methods for speed sensorless control of ac drives,” in Sensorless Control of AC Motors, K. Rajashekara, Ed. Piscataway, NJ: IEEE Press. Mohammad N. Marwali and Ali Keyhani (1997): ‘’A Comparative Study of Rotor Flux Based MRAS and Back EMF Based MRAS Speed Estimators for Speed Sensorless Vector Control of Induction Machines’’ IEEE Industry Applications Society Annual Meeting New Orleans, Louisiana, October 59 R. Blasco-Gimenez., G.M. Asher, M. Sumner, and K. J. Bradley (1996): "Dynamic Performance Limitations for MRAS based sensorless induction motor drives. Part 2: Online parameter tuning and dynamic performance studies", IEE Proc. Elect. Power Appl, Vol. 143, (2), pp. 123-134. R.Toufouti S.Meziane ,H. Benalla,(2006): “Direct Torque Control for Induction Motor Using Fuzzy Logic” ICGST Trans. on ACSE, Vol.6, Issue 2, pp. 17-24. S.Lassaâd and M.Ben Hamed (2007): “An MRAS based full Order Luenberger Observer for Sensorless DRFOC of Induction Motors” ICGST-ACSE Journal, Volume 7, Issue 1. 513

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