Online Stator Resistance Estimation Using Artificial

Neural Network for Direct Torque Controlled

Induction Motor Drive

C.M.F.S.Reza

Department of Electrical Engineering

University of Malaya

Kualalumpur, Malaysia

E-mail: sushan_hbk@yahoo.com

Saad Mekhilef

Department of Electrical Engineering

University of Malaya

Kualalumpur, Malaysia

E-mail: saad@um.edu.my

Abstract—For the stable and effective operation of the

induction motor proper estimation of the stator resistance is very

essential.This is because stator resistance keeps on increasing

with the temperature when the motor is in operation which

results in high torque and flux ripple.A method based on

artificial neural network to estimate the stator resistance of

induction motor for direct torque control drive is proposed in this

paper. For the training purpose of neural network a back

propagation algorithm has been used. The adjustments of the

weights of the neural network has been done by back propagating

the error signal between measured and estimated current of

stator. From simulation it has been proved that the estimator can

track stator resistance value within around 40ms when a step

change of stator resistance has been applied. Effectiveness of the

estimator is investigated in simulation by varying the stator

resistance from the nominal value which has been done in

MATLAB SIMULINK.

Keywords— Resistance estimator; neural network; induction

motor drive; DTC;

I. INTRODUCTION

Direct torque control(DTC) induction motor drive is becoming

more popular day by day[1, 2] due to its fast dynamic response

and robust to the variation of the machine parameter without

using the current controller[3-10]. Implementation of this

control strategy is very simple and also coordinate

transformation is not required. One of the most important

advantage is less dependence on the motor parameters except

stator resistance. To achieve the high performance of the

induction motor drive proper estimation of the stator flux is

needed. Due to variation of the temperature throughout the

operation of the motor stator resistance varies continuously

[11-13]. Variation of the stator resistance introduces error in

estimated flux position and magnitude of the stator flux. Error

in the estimated stator flux deteriorates the performance of

DTC drive. The effect of error in estimation is very important

mainly at low speed[11, 14].However a common disadvantage

of the conventional DTC is high torque ripple. To reduce the

torque ripple proper estimation of stator resistance is very

necessary. Recently many approaches have been reported to

estimate the accurate value of the induction motor stator

resistance when motor operates in DTC drives. Hybrid flux

estimation method has been reported in[15, 16] .By measuring

dc components of the stator current and voltage, stator

resistance has been calculated in[17, 18]. Extended Kalman

filter is very popular in this area due to the robustness and

filtering action but it requires more computational time[19,

20]. A fuzzy estimator in [21] has been used to estimate the

value of the stator resistance which uses the stator current

error for adjusting the stator resistance. Also it needs high

computational time as it has to follow many rules to provide

accuracy. Stator current is mostly affected variable of the

machine due to change in stator resistance and also there is a

nonlinear relationship between stator current and stator

resistance is nonlinear.

Recently for the control and identification of nonlinear

dynamic systems in ac drives using the artificial neural

network have been presented in [22-25] .One of the main

advantage of neural network is the capability of approximating

nonlinear function relationship. In this paper effect of the

change in stator resistance is discussed and an online neural

network stator resistance estimator is proposed to estimate the

accurate value of the stator resistance and also efficacy of the

estimator is shown.

This paper has been organized in five sections. DTC principle

is presented in the following section. Proposed stator

resistance estimation technique is given in section III.

Simulation results are described in section IV. Finally

conclusion is presented in section V.

1486978-1-4673-6322-8/13/$31.00 c©2013 IEEE

II. DTC PRINCIPLE

In DTC shown in Fig.1, errors of the electrom

and stator flux status is detected then pas

hysteresis comparator (two and three level)

Then a predetermined switching table (Table

status of the inverter switches which will be u

voltage vector ( .) location which is selected

angle of the stator. Voltage vectors used in

shown in Fig. 2

Torque

Controller

Flux

Controller

Switching

Table

Torque & flux

estimatorSpeed Controller

Torque ref

Flux ref

Speed ref

Flux est

Torque est

Speed mes

+_

+

_

+ _

Sector

selection

Fig. 1. Basic IM-DTC drive

Table I: Switching Table

T

1

1 0

-1

1

0 0

-1

Fig.2. Voltage vectors obtained from Voltage Source

mechanical torque

ssed through the

for digitization.

I) determines the

used to determine

according to flux

n DTC drive are

Inverter

IM

n

Gate

pulse

e Inverter (VSI)

The stator flux is given by the follow

= – ) dt

So d and q axis stator flux can be gi

= – )) dt

= – ) dt

Electromagnetic Torque

T= p ( – )

If we neglect the stator resistance th

as follows

= dt

According to the basic DTC strate

by integrating the back emf

electromagnetic torque estimation c

it has been seen that stator resistan

change the stator flux which will i

torque, which will deteriorate

robustness and faster response of th

in Fig.3

Fig. 3. Effect stator resistance v

III. STATOR RESISTANCE EST

The structure of the proposed neura

estimator is described by Fig.4. Eq

as voltage model of induction moto

and voltages and equation (8) & (9)

of induction motor which based on r

= ( – – )

= ( – – Ls )

= ( – )

= ( – rTr )

Stator resistance variation

Change in the stator current

wing equation

(1)

iven as

(2)

(3)

(4)

hen stator flux will become

(5)

egy stator flux is estimated

as stated in (1) and

can be done by (4).From (1)

nce variation will cause to

in turn effect the estimated

the effectiveness of the

he DTC, which is described

variation on the DTC drive

TIMATION USING ANN

al network stator resistance

quations (6) & (7) is known

or which based on currents

) is known as current model

rotor speed and currents.

(6)

(7)

(8)

) (9)

Error in flux and torque estimation

Error in flux and torque control

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) 1487

Fig.4. Stator resistance estimation with ANN

From voltage and current model of the induction motor we can

get

= vds – ids – ( ids – ) (10)

Equation (10) can be written in the discrete form as follows

= + + Vds (k-1) +

(11)

Where, = = ;

= ; = .

Equation (11) can be illustrated by a recurrent neural network

which is demonstrated in Fig. 5.Weights W1, W2, W3 are

directly calculated from the motor speed ( r), motor

parameters and sampling time ( ).Considered that motor

parameters are constant except stator resistance (RS).

Fig.5. Neural network scheme to estimate the d axis stator current

To train the neural network Back propagation is used as

learning algorithm. Adjustment of weight W4 is done for

minimizing the cost function (E) error given below.

E= =

Weight (W4) adjustment is done according to the equation (13)

(k) = (k-1) + (k) + (k-1) (13)

Where = training coefficient & =positive momentum

constant.

Similarly we can get

= - + (k-1) +

(14)

Equation (14) can be illustrated by a recurrent neural network

which is shown in Fig.6

Fig.6. Neural network scheme to estimate the q axis stator current

Equation (15) is used to calculate the stator resistance

= (15)

So using neural network scheme induction motor stator

resistance can be calculated from stator current as shown in

Fig.4.

IV. SIMULATION RESULT

The effectiveness of the proposed neural network stator

resistance estimator is justified in MATLAB/Simulink

environment. The response of the system with and without the

estimator of stator resistance is compared. Parameters of

induction motor used in simulation are given in table II.

Z-1

W4

W3

W2

Vqs(k-1)

Z-1

W4

W3

W2

W2

Vds(k-1)

1488 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

Table II : Induction Motor Parameter

Rated Power (kW) 3.7

Rated Voltage (V) 460

Rated Frequency (Hz) 60

Pole Pair 2

Stator Resistance( ) 1.115

Rotor Resistance( ) 1.083

Stator Inductance(H) 0.209674

Rotor Resistance(H) 0.209674

Magnetizing Inductance(H) 0.2037

Rated Speed(rpm) 1750

A PI speed controller is used which gives corresponding

reference torque and reference stator flux used for the drive is

0.96Wb.An initial load torque of 5 Nm is used.

To drive the induction motor an IGBT inverter is being used

and space vector modulation is used as modulation technique.

The coefficients used to train the network are = .009 and

=1e-6.

During the operation of the motor Stator resistance may vary

up to 50%. So, stator resistance of the motor has been

increased from 1.115 to 1.4 shown in Fig.7.a to verify and

investigate the effectiveness of the proposed estimator at 1.2

sec. In the conventional system fixed value of the stator

resistance is used. Therefore the speed becomes unstable

(Fig.7.b) and there are more ripples introduced in the output

torque (Fig.7.c) when changing of the stator resistance take

place. From stator flux locus it has been seen that more ripples

are introduced around the expected stator flux shown in

Fig.7.d and speed becomes unstable shown in Fig.6.c.

Fig. 7. Stator resistance variation effect.(a) step varaition of the stator

resistance, (b) speed (rad/s), (c) electromagnetic torque(N-m) (d) Stator flux

locus

From Fig.8.a it can be seen that estimated value of the

proposed estimator can track the the actual value of the stator

resistance within around 40 ms which is shown in the zoomed

view of the stator resistance tracking(Fig.8.b).Torque (Fig.8.c)

and flux(Fig.8.e) ripples is reduced after using the proposed

estimator though the stator resistance has been changed.And

also speed becomes stable which is shown in Fig.8.d

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Time(s)

Sta

tor

Res

ista

nce

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-10

0

10

20

30

40

50

60

70

80

90

100

Time(s)

Torq

ue(N

m)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

10

20

30

40

50

60

70

80

Speed (

rad

/s)

b

c

d

a

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) 1489

Fig. 8. Compansation of the Stator resistance variation effect.(a) step varaition

of the stator resistance,(b) zoomed view of stator resistance tracking (c) speed

(rad/s), (d) electromagnetic torque(N-m) (e) Stator flux locus

V. CONCLUSION

A new on-line stator resistance estimation technique is

addressed in this paper using neural network system. It has

been proved that proposed estimator can detect changes of the

stator resistance and also the estimator can converge to stator

resistance steady state value within around

40miliseconds.Simulation result reveals that the proposed

neural network estimator is excellent to estimate the stator

resistance in on-line.In future the proposed estimator will be

implemented,and also simulation and experimental result will

be compared.

ACKNOWLEDGEMENT

The authors would like to thank the Ministry of Higher

Education and University of Malayafor providing financial

support under the research grant No.

UM.C/HIR/MOHE/ENG/16001-00-D000017.

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0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

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0.6

0.8

1

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0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-10

0

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0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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