Sensor Less Speed Control of Three Phase Induction Motor by using MRAC

Sensor Less Speed Control of Three Phase Induction Motor by using MRAC

G. Balasubbarayudu1,

Student (M.Tech),

Department of Electrical and Electronics Engineering, St.Johns College of Engineering and Technology, Yemmiganur, Kurnool, Andra Pradesh, India.

S. Rehana Begum2

Assistant Professor,

Department of Electrical and Electronics Engineering and Technology, Yemmiganur,

Kurnool, Andra Pradesh, India

Abstracta new model reference adaptive controller (MRAC) for the speed estimation of the vector controlled induction motor drive is presented in this paper. the sensor less speed control of induction motor is formulated by developing a new model reference adaptive system using steadystate values of X=( *x i where v= supply voltage and i= supply current vector in synchronously rotating reference frame)which is a fictitious quantity. This is no need of physical significance. This formulation not only simply and more reliable but also made the sensor less drive stable in all four quadrants of operation. Speed estimation processing techniques are does not involve the stator and rotor flux. Modification of the vector controller drive can estimate the stator resistance in all the four quadrants of operation, if speed signal is available. The proposed MRAC- based speed sensor less vector control drive as well as the stator resistance estimation technique has been simulated in MATLAB/SIMULINK.

Index Termsinduction motor (IM), model reference adaptive controller (MRAC), sensor less control, stator resistance, vector control.

NOMENCLATURE

, d and q- components of stator voltage vector.

, and -components of stator voltage vector.

, d and q- components of stator current vector.

, Reference value of d and q-components of stator Current vector.

, and -components of stator current vector.

estimated value of

-components of rotor voltage vector.

and -components of rotor current vector.

, and -components of rotor flux vector.

Self inductance at the rotor side.

Magnetizing inductance.

I. INTRODUCTION

Vector-controlled induction motor drives are more extensively used in industry due to their cost effectiveness, ruggedness and, highly dynamic performance and easily implementation by using simple analog or digital signal processing techniques. Field-based-controlled or vector controlled drives are high performance of application. HOWEVER, the implementation of the vector controlled

Schemes are requires the knowledge of the rotor speed, stator poles and other machine parameters. Speed encoders or tacho- generators, which are used to sense the rotor actual speed and reliable operation of the drive system. Various algorithms for speed and parameter estimations are available in literature [1]- [35].the stability of the sensor less speed control algorithm needs to be maintained in all the four quadrants of operation including low speeds (zero speed) for the satisfactory performance of the drive. On other way, the vector control algorithm, estimating speed, vector control algorithm, machine parameters are available in literature [5]- [8],[23],[27],[33],[35].

The speed estimation techniques are mainly classified into two types namely model-based method and signal addition-based methods are very attractive. In this area, the use of First order Low pass filter is popularly used due to its robustness and the need of reduced number of PI controllers [9],[30].

Elimination of the speed sensor makes the drive mechanically more robust. However, the influence of noise characteristics, the absence of criteria for tuning, and the computational burden of this clean observation is the major limitation for the wide acceptability.[10]- [13],[31],[35],[36].these method requires the flux and dependent machine parameters also as reported in [31],model- based methods are not stable in all the operating range of the drive. In this modify, a classical adaptive control based on model reference adaptive controller (MRAC) depends on special report. Flux [14],[16].[17]back EMF [19],reactive power[18]-[20] based model reference adaptive controller are proposed in literature. Flux based MRAC [14] stable in all the four quadrants. Proportional controller plus Integrator is replaced by low pass filter (LPF), as reported in [15].the low pass filter introduces gain and phase angle error in the flux estimation below cutoff frequency of the filter. A neural network-adaptive based integrator for the Flux estimation is reported as [16]back EMF based MRAC [19]also suffer from the failing performance at low speed due to the presence of derivative operator .A reactive power-based MRAC [18]-[20] overcomes all such problems but at the cost of loosing satiability in the regenerative mode of operation. The instability problem of the reactive power based-MRAC is presented in [18].

In this paper represents the stability problems related to the regenerating mode of operation is solved by considering a new quantity. The quantity is defined by the outer product of v*and i(i.e., v*x i).

i(i.e., v*x i) is selected as the functional candidate of the MRAC. Note that v*x i is neither reactive power nor active power. The quantity is denoted asX. The instantaneous value of the X (i.e.,x1) is used in the reference model. On the other hand the steady state value of X under Flux based condition (i.e.,x4) is considered for the adjustable model. The error of the instantaneous value and steady state value (i.e.,=x1-x4)is fed to the variation mechanism, which yields the estimated rotor speed(i.e.,wrest). The X-based MRAC (X-MRAC) for speed estimation is shown in Fig.1(a). The schematic diagram of the complete vector control drive with the proposed X-MRAC shows the Fig.1(b) .

The X-MRAC can also be used for stator resistance estimation, if speed signal is available from speed encoder. This is discussed in section V at the end of this paper.

1. Formulation of the X-MRAC

   The induction motor stator voltages in the synchronously rotating reference frame may be expressed as

   Lr

   vsq = Rsisq + weLsisd + Lsisq + Lm (werd) (1)

   = + + ( +

   ) (2)

   Instanteous value of X(i.e.*x i)is defined as

   X1=vsdisq+vsqisd

   Fig.(a) prposed MRAC(X-mrac)structure for estimate

   speed

   (3)

   Substituting the values of vsqand vsdfrom(1)and (2)in (3),the instantaneous value of X becomes

   Lr

   X2 = [Rsisq + weLSisd + Lsisq + Lm (werd + rd)] isd + [Rsisq

   Lr

   weLsisd + Lsisd + Lsisq Lm (werd + rd)] isq (4)

   At steady state value of X is

   Fig.(b) Block diagramm of vector control drive with the

   = [

   +

   + (

   +

   )]

   + [

   proposed MRAC-based speed estimation technique

   3

   The steady state values of v*x i is used in the reference model and steady state Flux-based value of the same is considered for the adjustable model. The structure of such

   MRAC for speed estimation is shown in fig 1(a). the MRAC

   + ( +

   )] (5) The rotor flux- oriented drive, substituting =, and =0,the simplified expression of X becomes

   X4 = we[Lsi2 L i2 ] +

   sd s sq

   is the adding of X1 and X4 .The selection of v*x i is a major success in the sense that te proposed system is now stable in all the four quadrant modes of operation. The estimation speed includes the low speed or zero speed and does not require flux orientation. These make the drive easy for implementation.

   The paper is implemented in six sections. The following sections I and II deals with the basic idea of the proposed MRAC. Section III deals with the stability of the dynamic machine system. Simulation results are presented in section IV respectively. Section V deals with an MRAC that can accurately estimate the stator resistance for a standard indirect vector controlled system. The speed signal is available on the speed encoder. Section VI concludes the work.

   1. SYSTEM MODELING

      2Rsisdisq (6)

      The expression of X1 is independent of rotor speed. Hence, it is selected for the reference model.X2,X3 or X4 may be chosen as the adjustable model as they are dependent an they are dependent on the rotor speed().However,X4 is selected in the adjustable model, as this quantity does not involve flux estimation and any derivative operations.

   2. PROPOSED SPEED ESTIMATION ALGORITHM

      The speed is estimated using the concept of MRAC. Where reference and adjustable model reference controller compute a certain system variable. The system variable is computed by the reference model does not depend on the quality to be estimated, whereas the adjustable model depends directly estimated quantity. Active and reactive powers, here a

      fabricated quantity is, termed as X (X=v *x i ) is

      s s

      1. Proposed MRAC

      The block diagram of the proposed MRAC based speed estimation is shows Fig.1(a). The outer product of v* and

      considered as the functional candidate. The same value of X in adjustable model (xa) is computed with the help of reference values of voltages and currents. The actual values of

      d- and q-axis currents are obtained by transforming two-phase currents ( and ) with the help of vector rotor.

      = + (7)

      Now, the variables are perturbed as: ^=wr0+

      ^,=0+0,isq=isq0+,= 0+ and =

      = +

      0+.

      Considering the perturbed signals, can be ex-pressed as

      (8)

      =K i

      +k i

      +k w +k w

      (21)

      2 sd 3

      sq 4

      sl 5 r

      = (9)

   3. STABILITY ANALYSIS

The vector controlled induction machine drive with the proposed speed of the algorithms has been found to be stable in all the four quadrants of operation. The steady has been performed in a realistic manner by considering all the PI controller values, required in a vector controlled drive and liner zing the machine equitation around stable operating points. The investigation is carried out around a nominal speed, and the dynamics for the mechanical equations for the mechanical equation for a perturbation of wr is neglected as the mechanical time constant is much larger than the electrical time constant.

A. Basic equations

The state-space representation of the machine using stator currents and rotor flux (in the synchronously rotating reference frame) as the state variables is given by(7) and(8). This can be represented in state space domain as

= + (10)

= (11)

Linear zing the state space equations around a stable operating point x0,we get

= + 0 + (12)

[ ]= = (13) Where

X0=[isd0 isq0 rd0 rq0 ]T (14)

1. Stability of speed Estimation Algorithm

   The block diagram of speed estimation using X-MRAC is shown in Fig.1(a).the A matrix can be defined by

   TABLE I: Induction machine rating and parameters

   Symbol	Meaning	Value
   –	Rated shaft power	1.3Kw
   –	Line to line voltage	400v
   –	Stator phase current	4.4A
   –	Rated speed	1430rpm
   P	Pole pair	2
   Ls	Stator self inductance	0.6848H
   Lr	Rotor self-inductance	0.6848H
   Lm	Magnetizing inductance	0.6705H
   Rs	Stator resistance	5.71
   Rr	Rotor resistance	4.0859
   J	Machine inertia	0.011kg-m2
   B	Viscous coefficient	0.0015

   IV SIMULATION RESULTS

   1. step change of rotor speed and zero-speed operation

      The response of the induction motor for a step change in reference speed and zero speed operation can be in seen Fig.4. A step change in speed of 5 rad/sec is applied every 4s, and the actual speed is found to track the reference speed satisfactorily in Fig.2 (a). The estimated speed is available in Fig.2 (b), which shows that the same is very close to the actual rotor speed. Flux orientation is well maintained, is represented in Fig.2(c)

      								wref W ac

      6

      t

      5

      4

      speed(rad/sec)

      3

      2

      1

      0

      -1

      0 5 10 15 20 25

      Time(sec)

      Fig.2 (a)Reference speed and actual speed[rad/sec]versus time[s]

      Where,a

      =

      0

      0

      (1/ )().

      and

      (15) 6

      				West Wact

      5

      4

      speed(rad/sec)

      3

      are obtained by linear

      3=

      2

      zing the vector control equations, namely 1

      =3( ) (16) 0

      = { ( )-

      } (17)

      -1

      0 5 10 15 20 2

      Time(sec)

      2 1

      Where,r1=(kp1+(ki1/s)=transfer function of the speed PI controller,r2=(kp2+(ki2/s)) transfer function of the q-axis controller and r3=(kp3+(ki3/s)) transfer function of the d-axis controller indicated.

      = 3 (18)

      = 2{1 + } (19)

      0.7

      0.6

      0.5

      flux(web)

      0.4

      0.3

      0.2

      Fig .2 (b)Actual speed and estimated speed [rad/sec] versus time[s]

      d-axis q-axis

      From(13),(15),(19)get the expression of

      /.the X-MRAC error is given by =X1-X4

      = + [2 2 ]

      /

      and

      0.1

      0

      -0.1

      0 5 10 15 20 25

      Time(sec)

      Fig.2 (c) d-axis and q-axis rotor flux[wb] versus time[s]

      2 (20)

   2. Ramp response

      The tracking performance of the algorithm at low speeds(zero speed) is tested by apllying a traingular wave input as in Fig.3 (a).The estimated speed is following the actual speed which in turn is matching with the reference speed,as shown in Fig.3 (b).the flux orientation is not disturbed as observed in Fig.3 (c).the results have also confirmed stable operation in forword and reverse motoring modes

      6

      1.2

      					Wref Wact

      1

      0.8

      speed(rad/sec)

      0.6

      0.4

      0.2

      0

      Wref Wact

      4

      -0.2

      0 5 10 15 20 25

      Time(sec)

      speed (rad/sec)

      2

      0

      -2

      -4

      -6

      0 5 10 15 20 25

      Time(sec)

      Fig.3 (a) Reference speed and actual speed[rad/sec]versus time[s]

      6

      West Wact

      4

      2

      0.6

      0.5

      0.4

      flux(web)

      0.3

      0.2

      0.1

      0

      -0.1

      Fig.4 (b) Reference speed and actual speed[rad/sec]versus time[s]

      d-axis q-axis

      0 5 10 15 20 25

      Time(sec)

      Fig.4 (c) d-axis and q-axis rotor flux[wb] versus time[s]

      speed(rad/sec)

      0

      -2

      -4

      -6

      0.6

      0.5

      0.4

      0 5 10 15 20 2

      Time(sec)

      Fig.3 (b) Actual speed and estimated speed [rad/sec] versus time[s]

      d-axis q-axis

      4. Regenerating mode operation

      A).second quadrant opearation: The performance of the proposed speed estimator can be better seen from Fig.5 which shows the transition of the estimator from motoring to regenerating mode and back.the actual and reference speed are shown in Fig.5 (a) and the estimated and actual speed are shown in Fig.5 (b). the flux orientation is maitained ,which can be seen from shown in Fig.5 (c).the load torque is represented as shown in Fig.5 (d).

      6

      Wref Wact

      flux(web)

      0.3

      4

      0.2

      speed(rad/sec)

      2

      0.1

      0

      0

      -2

      -0.1

      0 5 10 15 20 25

      Time(sec)

      Fig.3 (c) d-axis and q-axis rotor flux[wb] versus time[s]

      -4

      -6

      0 5 10 15 20 25

      Time(sec)

   3. Low speed operation

   The performance of the algorithm at a low speed of 1 rad/sec is shown in Fig.4.the estimated and actual speed are shown in Fig.4.(a) and actual and reference speed are shown in Fig.4.(b).the flux orientation is maitained ,which can be seen from shown in Fig.4.(c)

   Fig.5 (a) Reference speed and actual speed[rad/sec]versus time[s]

   8

   6

   4

   speed(rad/sec)

   2

   0

   -2

   -4

   West Wact

   1.2 -6

   West Wact

   1

   0.8

   -8

   0 5 10 15 20 25

   Time(sec)

   Fig.5 (b) Actual speed and estimated speed [rad/sec] versus time[s]

   0.6

   speed(rad/sec)

   d-axis q-axis

   0.6

   0.4

   0.2

   0

   0.5

   0.4

   flux(web)

   0.3

   0.2

   0.1

   -0.2

   0 5 10 15 20 25

   Time(sec)

   Fig.3 (a) Actual speed and estimated speed [rad/sec] versus time[s]

   0

   -0.1

   0 5 10 15 20 25

   Time(sec)

   Fig.5 (c) d-axis and q-axis rotor flux[wb] versus time[s]

   					torque

   									Wref Wact

   0.3 6

   5

   0.2

   load torque in (newton-m2)

   4

   speed(rad/sec)

   0.1

   3

   0

   2

   -0.1

   1

   -0.2

   0

   -0.3

   -0.4

   0 5 10 15 20 25

   -1

   0 5 10 15 20 2

   Time(sec)

   Fig.5 (d)Machine torque and load torque [Nm]versus time[sec]

   Fig.7(a) Reference speed and actual speed[rad/sec]versus time[s]

   B) Fourth quadrant operation :

   The performance of the proposed speed estimator in the fourth quadrant can be seen from Fig.6. Which shows the transition of the machine from motoring to regenerating mode and back. the actual and reference speed are shown in Fig. 6(a).and the estimated and actual speed are shown in Fig. 6(b). the flux orientation is maitained ,which can be seen from

   6

   5

   speed(rad/sec)

   4

   3

   2

   1

   0

   -1

   0 5 10

   Time(sec)

   West Wact

   15 20 25

   shown in Fig. 6(c).the load torque is represented as shown in Fig.6(d)

   					Wref Wact

   2.5

   Fig.7(b) Actual speed and estimated speed [rad/sec] versus time[s]

   					d-axis q-axis

   0.6

   2 0.5

   1.5

   1

   speed(rad/sec)

   0.5

   0

   -0.5

   0.4

   speed (rad/sec)

   0.3

   0.2

   0.1

   0

   -1

   -1.5

   -2

   -2.5

   0 5 10

   time(sec)

   15 20 25

   -0.1

   0 5 10 15 20 25

   Time(sec)

   Fig.7(c) d-axis and q-axis rotor flux[wb] versus time[s]

   6.Regenerative mode operation:

   Fig.6(a) Reference speed and actual speed[rad/sec]versus time[s]

   0.7

   d-axis q-axis

   0.6

   0.5

   flux (web)

   0.4

   0.3

   0.2

   0.1

   0

   The performance of the regenerative mode is located in the second quadrant modes of operation .The torque generated by the induction machine shown in fig.8.A reference speed of – 5rad/sec is applied to the machine the actual and reference speed are shown in Fig.8(a). the estimated and actual speed are shown in Fig.8(b). the flux orientation is maitained , the machine and load torque is represented as shown in

   -0.1

   0 5 10

   Time(sec)

   15 20 2

   Fig.8(d).which shows that the proposed MRACcan estimate

   0.5

   0.4

   0.3

   Load torque in N-m2

   0.2

   0.1

   0

   -0.1

   -0.2

   -0.3

   Fig.6(c) d-axis and q-axis rotor flux[wb] versus time[s]

   the rotor speed satisfactorily even in the regenerative mode of operation.

   					Wref wact

   0

   -0.5

   -1

   -1.5

   speed(rad/sec)

   -2

   -2.5

   -3

   -3.5

   -4

   -0.4

   -0.5

   					torque

   0 5 10 15 20 25

   -4.5

   -5

   0 5 10 15 20 2

   Time(sec)

   Time(sec)

   Fig.6 (d)Machine torque and load torque [Nm]versus time[sec]

   Fig.8(a) Reference speed and actual speed[rad/sec]versus time[s]

   0

   5.Step change of rotor speed and zero speed operation

   speed(rad/sec)

   -4

   The response of the induction motor for step change and zero -1 speed operation are shown in Fig.7.A step change speed of 5 -2 rad/sec is applied every 4s from 0 rad/sec and the actual speed -3 is found to track the performance speed satisfactorily. the

   actual and reference speed are shown in Fig.7(a).and the

   estimated and actual speed are shown in Fig.7(b). the flux -5

   West Wact

   orientation is maitained ,which can be seen from shown in Fig.7(c).

   -6

   0 5 10 15 20 2

   Time(sec)

   Fig.8(b) Actual speed and estimated speed [rad/sec] versus time[s]

   0.3

   0.6

   0.5

   0.4

   flux(web)

   0.3

   0.2

   0.1

   0

   -0.1

   d-axis q-axis

   0 5 10 15 20 2

   Time(sec)

   Fig.8(c) d-axis and q-axis rotor flux[wb] versus time[s]

   8.Low speed operation:

   the simulation results corresponding to the operation of the proposed controller at 1 rad/sec reference can be seen from fig.10 the actual and reference speed are shown in Fig.10(a).and the estimated and actual speed are shown in Fig10(b). the flux orientation is maitained is shown in Fig.10(c).

   					Wref Wact

   1.2

   					torque

   1

   0.25

   Load torque in N-m2

   0.2

   0.15

   0.1

   0.05

   0.8

   speed(rad/sec)

   0.6

   0.4

   0.2

   0

   -0.2

   0

   0 5 10 15 20 25

   Time(sec)

   -0.05

   0 5 10 15 20 25

   Time(sec)

   Fig.8(d)Machine torque and load torque [Nm]versus time[sec]

   Fig.10(a) Reference speed and actual speed[rad/sec]versus time[s]

   					West

   1.2

   Response to ramp command in speed:

   The performance of algorithm for a ramp command in speed reference is shown in fig.9. A reference speed of -5rad/sec is applied to the machine the actual and reference speed are shown in Fig.9(a).and the estimated and actual speed are shown in Fig.9(b). the flux orientation is maitained is shown

   1

   speed(rad/sec)

   0.8

   0.6

   0.4

   0.2

   0

   -0.2

   0 5 10

   Time(sec)

   Wact

   15 20 25

   in Fig.9(c).

   Fig.10(b) Actual speed and estimated speed [rad/sec] versus time[s]

   Wref

   5

   Wact

   4

   0.6

   0.5

   d-axis q-axis

   3

   0.4

   2

   speed(rad/sec)

   flux(web)

   0.3

   1

   0 0.2

   -1 0.1

   -2

   0

   -3

   -0.1

   -4

   0 5 10 15 20 25

   Time(sec)

   -5

   0 5 10 15

   Time(sec)

   Fig 5.2.8.(a) Reference speed and actual speed[rad/sec]versus time[s]

   6

   West Wact

   Fig.10(c) d-axis and q-axis rotor flux[wb] versus time[s]

   V STATOR RESISTANCE ESTIMATION

   4

   speed(rad/sec)

   2

   0

   -2

   -4

   -6

   0 5 10

   15

   Time(sec)

   1. stator resistance Estimation

      The proposed controller requires an estimate of Rs to compute X4 in the adjustable model.This dependency may be Fig.9.(a) Actual speed and Estimated speed[rad/sec]versus Time[s].(c)d-axis nad q-axis rotor flux[Wb] versus time[s].Fig.11.X-MRAC-based stator resistance estimation.

      Fig.9 (b) Actual speed and estimated speed [rad/sec] versus time[s]

      					d-axis q-axis

      0.6

      0.5

      0.4

      flux(web)

      0.3

      0.2

      0.1

      0

      -0.1

      0 5 10 15 20 25

      Time(sec)

      Fig.9 (c) d-axis and q-axis rotor flux[wb] versus time[s]

      Exploited to from an algorithm to estimate Rs in case the speed signal is available from the speed encoder or tacho- generator.the corresponding the controller is shown in fig.11.

   2. Simulation Result

The performance of the Rs-estimator (Fig. 10) under the step change of stator resistance is shown in Fig. 10. It can be seen that the estimated stator resistance is following the actual stator resistance satisfactorily

VI. CONCLUSION

A new MRAC-based speed estimation technique is presented in this paper. In the proposed system, a ctitious quantity (called as X = v*xi) is used as the functional candidate to form the MRAC. Such selection has resulted in several merits over the existing approaches. The proposed controller is stable in all the four quadrant modes of operation. Computation of ux is no longer required. Absence of pure integration in the controller has resulted in excellent performance at zero or low speeds. Selection of instantaneous and steady-state (with ux orientation) value of X in the reference and adjustable models, respectively, reduced the computational burden signicantly. In case the speed signal is available from the encoder, the proposed MRAC may be slightly modied to estimate Rs. This is discussed in brief at the end of this paper. A correct estimation of Rs provides indirectly the rise in stator temperature of the machine, which is extremely important for condition monitor- ing of the drive. The usefulness of the proposed algorithms has been con- rmed through stability study (small signal analysis in state space domain), simulation (in MATLAB/SIMULINK).Note that the implementation of the proposed method requires no extra hardware, which makes it suitable for retreat applications.

APPENDIX CONTROLLER GAINS

Proportional gain of the speed controller	0.05
Integral gain of the speed controller	0.5
Proportional gain of the MRAC adaption mechanism	1
Integral gain of the MRAC adaption mechanism	9

REFERENCES

[1]. Bose, Power Electronics and Motor Drives: Advances and Trends, 2nd ed. New York: Academic, 2006.

[2]. I. Boldea and S. A. Nasar, Electric Drives, 2nd ed. Boca Raton, FL: CRC, 2006.

[3]. A. Emadi, Energy-Efcient Electric Motors, 3rd ed. Boca Raton, FL: CRC, Aug. 2004.

[4]. R. Krishnan and S. Aravind, A review of parameter sensitivity and adap- tation in indirect vector controlled induction motor drive systems, IEEE Trans. Power Electron., vol. 6, no. 4, pp. 695703, Oct. 1991.

[5]. J. Holtz and T. Thimm, Identication of the machine parameters in a vector-controlled induction motor drive, IEEE Trans. Ind. Appl., vol. 27, no. 6, pp. 11111118, Nov./Dec. 1991.

[6]. V. Vasic, S. N. Vukosavic, and E. Levi, A stator resistance estimation scheme for speed sensorless rotor ux oriented induction motor drives, IEEE Trans. Energy Convers., vol. 18, no. 4, pp. 476483, Dec. 2003.

[7]. T. Pana, Model based speed and rotor resistance estimation for sen- sorless vector-controlled induction motor drives using oating point DSP, in Proc. 4th Int. Workshop AMC-MIE, Mar. 1821, 1996, vol. 1, pp. 168173.

[8]. S. Shinnaka, A unied analysis on simultaneous identication of velocity and rotor resistance of induction motors, Trans. Inst. Elect. Eng. Jpn., vol. 113-D, no. 12, pp. 14831484, Dec. 1993.

[9]. R. Kim, S. K. Sul, and M. H. Park, Speed sensorless vector control of induction motor using extended Kalman lter, IEEE

Trans. Ind. Appl., vol. 30, no. 5, pp. 12251233, Sep./Oct. 1994.

[10].M. Hinkkanen, Analysis and design of full-order ux observers for sensor less induction motors, IEEE Trans. Ind. Electron., vol.51, no. 5, pp. 10331040, Oct. 2004.

[11].H. Kubota, I. Sato, Y. Tamura, K. Matsuse, H. Ohta, and Y. Hori, Regenerating-mode low-speed operation of sensor less induction motor drive with adaptive observer, IEEE Trans. Ind. Appl., vol. 38, no. 4, pp. 10811086, Jul./Aug. 2002.

[12].K. B. Lee, J. H. Song, I. Choy, and J. Y. Yoo, Improvement of low speed operation performance of DTC for three-level inverter-fed induc- tion motors, IEEE Trans. Ind. Electron., vol. 48, no. 5, pp. 10061014, Oct. 2001.

[13].M. Hinkkanen and J. Luomi, Parameter sensitivity of full- order ux observers for induction motors, IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 11271135, Jul./Aug. 2003.

[14].C. Schauder, Adaptive speed identication for vector control of induction motor without rotational transducers, IEEE Trans. Ind. Appl., vol. 28, no. 5, pp. 10541061, Sep./Oct. 1992.

[15].J. Hu and B. Wu, New integration algorithms for estimating motor ux over a wide speed range, IEEE Trans. Power Electron., vol. 13, no. 5, pp. 969977, Sep. 1998.

[16].M. Cirrincione, M. Pucci, G. Cirrincione, and G.-A. Capolino, A new adaptive integration methodology for estimating ux in induction ma- chine drives, IEEE Trans. Power Electron., vol. 19, no. 1, pp. 2534, Jan. 2004.

[17].M. Cirrincione, M. Pucci, G. Cirrincione, and G.-A. Capolino, Sensor- less control of induction machines by a new neural algorithm: The TLS EXIN neuron, IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 127149, Feb. 2007.

[18].S. Maiti and C. Chakraborty, A new instantaneous reactive power based MRAS for sensorless induction motor drive, Simul. Modell. Pract. Theory, vol. 18, no. 9, pp. 13141326, Oct. 2010.

[19].F. J. Peng and T. Fukao, Robust speed identication for speed sensorless vector control of induction motors, IEEE Trans Ind. Appl., vol. 30, no. 5, pp. 12341240, Sep./Oct. 1994.

[20].S. Maiti, C. Chakraborty, Y. Hori, and M. C. Ta, Model reference adap- tive 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. 594601, Feb. 2008.

[21].A. Trentin, P. Zanchetta, C. Gerada, J. Claire, and P. W. Wheeler, Op- timized commissioning method for enhanced vector control of high- power induction motor drives, IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 17081717, May 2009.

[22].S. M. Gadoue, D. Giaouris, and J. W. Finch, Sensorless control of induc- tion motor drives at very low and zero speeds using neural network ux observers, IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 30293039, Aug. 2009.

[23].I. Vicente, A. Endemaño, X. Garin, and M. Brown, Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation, IET, Control Theory Appl., vol. 4, no. 6, pp. 993 1004, Jun. 2010.

[24].T. Orlowska-Kowalska and M. Dybkowski, Stator-current- based MRAS estimator for a wide range speed-sensorless induction-motor drive, IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 12961308, Apr. 2010.

[25].S. M. Gadoue, D. Giaouris, and J. W. Finch, MRAS sensorless vector control of an induction motor using new sliding-mode and fuzzy-logic adaptation mechanisms, IEEE Trans. Energy Convers., vol. 25, no. 2, pp. 394402, Jun. 2010.

[26].M. S. Zaky, M. M. Khater, S. S. Shokralla, and H. A. Yasin, Wide- speed-range estimation with online parameter identication schemes of sensorless induction motor drives,

IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 16991707, May 2009.

[27].P. Zhang, B. Lu, and T. G. Habetler, An active stator temperature estima- tion technique for thermal protection of inverter-fed induction motors with considerations of impaired cooling detection, IEEE Trans. Ind. Appl., vol. 46, no. 5, pp. 18731881, Sep./Oct. 2010.

[28].C. Patel, K. Gopakumar, R. Ramchand, K. Sivakumar, and A. Das, A rotor ux estimation during zero and active vector periods using current error space vector from a hysteresis controller for a sensorless vector control of IM drive,IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2334 2344, Jun. 2010.

[29].L. Yi and Q. Wenlong, Low speed performence improvement of sensor- less IM control system based on MRAS and NN ux observers, in Proc. Int. Conf. ICIS, Oct. 2931, 2010, vol. 2, pp. 421425.

[30].N. Salvatore, A. Caponio, F. Neri, S. Stasi, and G. L. Cascella, Opti- mization of delayed-state kalman-lter-based algorithm via differential evolution for sensorless control of induction motors, IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 385 394, Jan. 2010.

[31].E. Etien, C. Chaigne, and N. Bensiali, On the stability of full adaptive observer for induction motor in regenerating mode, IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 15991608, May 2010.

[32].A. V. Ravi Teja and C. Chakraborty, A novel model reference adaptive controller for estimation of speed and stator resistance for vector con- trolled induction motor drives, in Proc. IEEE ISIE, Bari, Italy, 2010, pp. 11871192.

[33].Z. Gao, R. S. Colby, L. Turner, and B. Leprettre, Filter design for estimating parameters of induction motors with time- varying loads, IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 15181529, May 2011.

[34].P. Zhang, Y. Du, T. G. Habetler, and B. Lu, Magnetic effects of DC signal injection on induction motors for thermal evaluation of stator windings, IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 14791489, May 2011.

[35].L. Zheng, J. E. Fletcher, B. W. Williams, and X. He, A novel direct torque control scheme for a sensorless ve-phase induction motor drive, IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 503513, Feb. 2011.

[36].M. Pucci and M. Cirrincione, Neural MPPT control of wind generators with induction machines without speed sensors, IEEE Trans. Ind. Elec- tron., vol. 58, no. 1, pp. 3747, Jan. 2011

G.Balasubbarayudu was born in 1990.studying M.Tech(power electronics and electrical drives) in st.Johns college of engineering and technology from JNTUA. he received B.Tech degree in electrical and electronics engineering from anurag college of engineering from JNTU Hyderabad.

S.Rehana Begum was born in 1989.she received M.Tech(control system) degree in electrical and electronics engineering from JNTUA anatapur.she received B.Tech from sri sai institute of technology and science of JNTUA.presently she is working as Assistant professor in st.Johns college of engineering and technology.