Vol 5, Issue 1, 2017 ISSN - 2347-1573 SIMULATION AND CONTROL OF INDUCTION MOTOR DRIVE USING ADVANCED SOFT COMPUTING TECHNIQUES JAWAHAR A, PRASANNA KL Department of Electrical and Electronics Engineering, Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India. Email: 0891jawahar@gmail.com Received: 29 September 2016, Revised and Accepted: 11 October 2016 ABSTRACT Induction 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. INTRODUCTION There 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 MOTOR Fig. 1 shows block diagram of various steps in modeling the induction machine. Dynamic model state – space equations Let’s define the flux linkage variables as follows: F qs =w b Ψ qs (1) F qr =w b Ψ qr (2) F ds =w b Ψ ds (3) F dr =w b Ψ dr (4) Where, w b =Base frequency of machine. V Ri w dF dt w w F qs s qs b qs e b ds = + + 1 ( ) (5) V Ri w dF dt w w F ds s ds b ds e b qs = + + 1 ( ) (6) 0 1 = + + - Ri w dF dt w w w F r qr b qr e r b dr ( ) (7) 0 1 = + - - Ri w dF dt w w w F r dr b dr e r b qr ( ) (8) It is assumed that V qr =V dr =0. Multiplying the equations by w b on both sides, the flux linkage expressions will be: F qs =w b Ψ qs =X ls i qs +X m (i qs +i qr ) (9) F qr =w b Ψ qr =X lr i qr +X m (i qs +i qr ) (10) F qm =w b Ψ qm =X m (i qs +i qr ) (11) F ds =w b Ψ ds =X ls i ds +X m (i ds +i dr ) (12) F dr =w b Ψ dr =X lr i dr +X m (i ds +i dr ) (13) F dm =w b Ψ dm =X m (i ds +i dr ) (14) Where, X ls =w b L ls , X lr =w b L lr and X m =w b L m or F qs =X ls i qs +F qm (15) F qr =X lr i qr +F qm (16) F ds =X ls i ds +F dm (17) F dr =X ls i dr +F dm (18) From Equations (15-18), the currents can be expressed in terms of flux linkages as: i F F X qs qs qm ls = - (19) Review Article