Induction motor drives have certain advantages like less cost, ruggedness and required low maintenance. Field oriented control provides good solution for industrial applications. Normally in order to implement a vector control operation we generally require number of position sensors like speed, voltage, current sensors. But if we use the position sensors then the cost and size will be increased. So, to overcome this we need to use limited number of sensors. Reducing the number of sensors will increase the reliability of the system. So, if we eliminate the number of sensors we need to estimate the required quantity. The estimation can be done by using different strategies like model based and signal based out of this model based estimation the best method to estimate the speed by using Model Reference Adaptive System (MRAS).Â

JAWAHAR A

PRASANNA KL

English

Innovare Academic Sciences: E-Journals

Vol 5, Issue 1, 2017 ISSN -  2347-1573 SIMULATION AND CONTROL OF INDUCTION MOTOR DRIVE USING ADVANCED SOFT COMPUTING TECHNIQUESJAWAHAR A, PRASANNA KLDepartment of Electrical and Electronics Engineering, Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India. Email: 0891jawahar@gmail.comReceived: 29 September 2016, Revised and Accepted: 11 October 2016ABSTRACTInduction motor drives have certain advantages such as less cost, ruggedness, and required low maintenance. Field oriented control provides a good solution for industrial applications. Normally to implement a vector control operation, we generally require number of position sensors such as speed, voltage, and current sensors. But if we use, the position sensors then the cost and size will be increased. Hence, to overcome this, we need to use a limited number of sensors. Reducing the number of sensors will increase the reliability of the system. Hence, if we eliminate the number of sensors, we need to estimate the required quantity. The estimation can be done using different strategies like model based and signal based out of this model-based estimation the best method to estimate the speed using model reference adaptive system.Keywords: Induction motor, Vector control, Model reference adaptive system.INTRODUCTIONThere are different model reference adaptive system (MRAS) methods available such as flux-based MRAS and reactive power based MRAS. [1-4] flux-based MRAS has certain disadvantages like consisting of a pure integrator and the effect of stator resistance [2]. The back electromotive force based MRAS does have problem of pure integrator but it has disadvantage of derivative terms [2]. If we go for a reactive power based MRAS, it has advantages like the absence of pure integrator and it also does not have effect of stator resistance, but the problem with this kind of MRAS is that it is unstable in regenerative mode of operation [3]. A vector controlled induction motor offers an exact control of induction motor over a scalar control because a scalar control although provides good steady state response but it possesses very poor performance during dynamic situation [3]. To achieve field oriented control the entire flux should be aligned on the direct axis [1]. To convert the three phase machine variables into two phase variables, we have to perform Clarks transformation [4]. Different reference frames are discussed [4]. The machine modeling equations are considered in synchronous reference frame where all the variables appear as DC quantities [3-6].MODELING OF AN INDUCTION MOTORFig. 1 shows block diagram of various steps in modeling the induction machine.Dynamic model state – space equationsLet’s define the flux linkage variables as follows:Fqs=wbΨqs (1)Fqr=wbΨqr (2)Fds=wbΨds (3)Fdr=wbΨdr (4)Where, wb=Base frequency of machine.V R iwd FdtwwFqs s qsbqs ebds= + +1 ( ) (5)V R iwd FdtwwFds s dsbds ebqs= + +1 ( ) (6)01= + +−R iwd Fdtw wwFr qrbqr e rbdr( ) (7)01= + −−R iwd Fdtw wwFr drbdr e rbqr( ) (8)It is assumed that Vqr=Vdr=0.Multiplying the equations by wb on both sides, the flux linkage expressions will be:Fqs=wbΨqs=Xlsiqs+Xm(iqs+iqr) (9)Fqr=wbΨqr=Xlriqr+Xm(iqs+iqr) (10)Fqm=wbΨqm=Xm(iqs+iqr) (11)Fds=wbΨds=Xlsids+Xm(ids+idr) (12)Fdr=wbΨdr=Xlridr+Xm(ids+idr) (13)Fdm=wbΨdm=Xm(ids+idr) (14)Where, Xls=wbLls, Xlr=wbLlr and Xm=wbLm orFqs=Xlsiqs+Fqm (15)Fqr=Xlriqr+Fqm (16)Fds=Xlsids+Fdm (17)Fdr=Xlsidr+Fdm (18)From Equations (15-18), the currents can be expressed in terms of flux linkages as:iF FXqsqs qmls=− (19)Review ArticleInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 8iF FXqrqr qmlr=− (20)iF FXdsds qmls=− (21)iF FXdrdr qmlr=− (22)F XF FXF FXqm mqs qmlsqr qmlr=−+−  (23)F F XXF XXqm qsmllsqrmllr= +  (24)Where, XX X Xmlm ls lr=+ +11 1 1  (25)And for Fdm as:F F XXF XXqm qsmllsqrmllr= +  (26)V RXF Fwd FdtwwFqs slsqs qmbqs ebds= − + +( )( )1 (27)V RXF Fwd FdtwwFds slsds dmbds ebds= − + −( )( )1 (28)01= − + +−RXF Fwd Fdtw wwFrlrqr qmbqr e rbdr( )( ) (29)01= − + +−RXF Fwd Fdtw wwFrlrdr dmbdr e rbqr( )( ) (30)It can be expressed in state-space form as:d Fdtw V wwF RXF Fqs b qs ebdsslsqs qm( )( )= − − −  (31)d Fdtw V wwF RXF Fds b ds ebqsslsds qm( )( )= − − −  (32)d Fdtw wwF RXF Fqr b ebdrrlrqr qm( )( )= − + −  (33)d Fdtw wwF RXF Fqs b ebqrrlrdr dm( )( )= − + −  (34)(or)F w V wwF RXF Fqs b qs ebdsslsqs qm= − − −∫ ( )  (35)F w V wwF RXF Fds b ds ebqsslsds dm= − − −∫ ( )  (36)F w wwF RXF Fqr b ebdrrlrqr qr= + −∫ ( )  (37)F w wwF RXF Fdr b ebqrrlrlr dm= + −∫ ( )  (38)Finally, from Equation (6),Te=(3/2)(p/2)(1/wb)(Fdsiqs−Fqsids) (39)Equations (35-39) describe the complete model in state-space form where Fqs, Fqr, Fds, Fdr are the state variables.T T J dwdtBwe L m m= + +(or)wJT Tm e= −∫1 1( )Simulation resultsFree acceleration characteristics of an induction motor are given maintaining the load torque as zero (Graphs 1-3).SENSORLESS VECTOR CONTROLClosed loop observersAmong all the closed loop observers a MRAS attracts more attention. In this MRAS observer, the output is generated from comparison between two estimators.Fig. 1: Steps involved in modeling of induction machineInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 9Adaptive controlAdaptive control can be explained in many ways the best way to explain is “a system which adapts itself accordingly with the changes in the system.”MRASIt consists of reference model and adaptable model as shown in Fig. 2. The speed adaption mechanism will adjust its output based on the outputs of reference and adaptive models.MRAS based on rotor flux estimation (Fig. 3)By using the stator voltage equations in stationary reference frame, the reference model equations can be expressed as:Reference model equations:ψ υ σdrrmdsrms s dsLLLLR L ddti= − +Graph 1: No-load response of stationary frame induction motor modelGraph 2: Torque response in direct AC startup with TL=0Graph 3: Speed response in direct AC startup with TL=0Innovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 10ψ υ σqrrmqsrms s qsLLLLR L ddti= − + (40)Where, Ψ is flux linkage,Lr, Lm are inductances,Rs is resistance and,σ is the leakage coefficient of the motor.1ˆ ˆ ˆdr qr drs s smds rr rL i wT T   = − −  ∫1ˆ ˆ ˆqr dr qrs s smqs rr rL i wT T   = − −  ∫  (41)Where, wr is rotor electrical speed and Tr=Lr/Rr is rotor time constant.Fig. 4 shows the block diagram sensorless vector control.Simulink implementation of MRASVds, Vqs, ids, iqs inputs are derived from the IM, and their stationary values are calculated in the stationary reference frame.Fig. 2: Model reference adaptive systemFig. 3: Model reference adaptive system based on rotor flux estimationInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 11Using Vdss , Vqss , idss , iqss  values drs , qrs  are calculated from reference model equations.ψ υ σdrrmdsrms s dsLLLLR L ddti= − +ψ υ σqrrmqsrms s qsLLLLR L ddti= − +Similarly by using ids, iqs and ˆ drs , ˆqrs  values ˆdrs , ˆ qrs  are calculated from the equations, these are the actual estimated values.1ˆ ˆ ˆdr qr drs s smds rr rL i wT T   = − −  ∫1ˆ ˆ ˆqr dr qrs s smds rr rL i wT T   = + −  ∫Using these values, the error can be calculated as:i.e., ˆ ˆ ˆ ˆdr qr qr drs s s sX Y    = − = −Equation for speed estimation.Simulation of proposed system (Figs. 5 and 6)The simulation study is carried out using fuzzy-MRAS  based induction motor drive control system in sensorless estimation mode. The Fig. 5 and 6 depict the simulation diagrams of the proposed system Simulation resultsCase A: Ramp response (Graphs 4 and 5).Case B: Regenerating mode of operation (Graphs 6 and 7).Case C: Performance under low speed (5 rad/s) (Graphs 8 and 9).Case D: Linear increase in speed (Graphs 10 and 11).Fuzzy logic controllers (Figs. 7 and 8)Simulation results of MRAS observer with fuzzy controllerCase A: Regenerative mode of operation (Graphs 12 and 13).Case B: Regenerative mode of operation (Graph 14).Case C: Response under low speed (Graphs 15).Case D: Linear increase in speed (Graphs 16).CONCLUSION AND FUTURE SCOPEThis work presented a speed sensorless vector controlled induction motor drive which provides same dynamic and satisfactory performance as that of a vector controlled Induction motor drive using a sensor. The dynamic performance of the proposed system is Fig. 4: Block diagram for sensor-less vector controlInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 12Fig. 5: Simulink model of sensorless vector controlled induction motor driveFig. 6: Simulink model of model reference adaptive system observerGraph 4: Reference and estimated speeds with ramp responseInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 13Graph 5: Estimated speed with ramp response Graph 6: Reference and estimated speeds in regenerative modes of operationGraph 7: Estimated speed in regenerative modes of operationGraph 8: Reference and estimated speeds  under low speed rangeInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 14Graph 9: Estimated speed under low speed rangeGraph 10: Reference and estimated speeds with linear raiseGraph 11: Estimated speed with linear raiseGraph 12: Reference and estimated speeds with ramp response using fuzzy-MRASInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 15Fig. 7: Simulink block diagram of model reference adaptive system observer with fuzzy controllerFig. 8: Editor view of fuzzy controller in MATLAB/SimulinkGraph 13: Estimated speed using Fuzzy-MRAS controller for ramp responseInnovare Journal of Eng. & Tech, Vol 5, Issue 1, 2017, 7-16 Jawahar and Prasanna 16tested under many cases. This work also presented a fuzzy controller based MRAS which will give better results than a PI controller based MRAS.REFERENCES1. Verma V, Chakraborty C, Maiti S, Hori Y. Speed sensorless vector controlled induction motor drive using single current sensor. IEEE Trans Energy Convers 2013;28(4):938-50.2. Maiti S, Chakraborty C, Hori Y, Ta MC. Model reference adaptive controller-based rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power. IEEE Trans Ind Electron 2008;55(2):594-601.3. Yaonan W, Jiantao L, Shoudao H, Sihai Q. Speed Sensorless Vector Control of Induction Motor Based on the MRAS Theory, College of Electrical and Information Engineering. Hunan, China: University of Hunan Changsha; 2010.4. Bose BK. Modren Power Electronics & Drives. 1st ed. New Jersey: Prentice Hall Publishers; 2010.5. Krishnan R. Electric Motor Drives. 1st ed. New Jersey: Prentice Hall Publishers; 2010.6. Schauder C. Adaptive speed identification for vector controlled induction motors without rotational transducers. IEEE Trans Ind Appl 1992;28(5):1054-61.Graph 14: Reference and estimated speeds for regenerative mode of operation using Fuzzy-MRASGraph 15: Reference and estimated speeds for  low speeds using Fuzzy-MRASGraph 16: In case of linear increase in speed using Fuzzy-MRAS

SIMULATION AND CONTROL OF INDUCTION MOTOR DRIVE USING ADVANCED SOFT COMPUTING TECHNIQUES

https://innovareacademics.in/journals/index.php/ijet/article/download/15430/9038/73681

SIMULATION AND CONTROL OF INDUCTION MOTOR DRIVE USING ADVANCED SOFT COMPUTING TECHNIQUES

Abstract

Similar works

Full text

Available Versions

Innovare Academic Sciences: E-Journals