This document summarizes a research paper that examines the effect of rotor resistance variation in indirect vector control of induction motors. It proposes estimating the rotor resistance using an artificial neural network (ANN) approach to compensate for resistance changes due to temperature rise. Simulation and experimental results show that without compensation, resistance variation leads to performance deterioration. The ANN model estimates resistance from the error between flux estimates from voltage and current models. Estimated resistance is fed back into the controller to improve performance under varying resistance conditions. Results demonstrate compensation of resistance increases the accuracy of torque, flux and current control.

1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
255
EFFECT OF ROTOR RESISTANCE AND COMPENSATION IN INDIRECT
VECTOR CONTROL USING ARTIFICIAL NEURAL NETWORKS (ANN)
B.Mouli Chandra1
, Dr.S.Tara Kalyani2
1
EEE Department, JNTUH, Hyderabad, INDIA
2
EEE Department, JNTUH, Hyderabad, INDIA
ABSTRACT
In this paper the effect of rotor resistance in indirect vector control of Induction Motor (I.M)
is examined and compensated using Artificial Neural Networks. Since Rotor resistance is prone to
error due to temperature rise in machine. Because of this, resistance used in controller differ from
resistance used in machine. This leads to performance deterioration of machine unless otherwise it is
compensated. Here the rotor resistance estimated from the voltage and current model equations
using ANN. The performance of the machine is analysed using MATLAB/SIMULINK in terms of
output currents, rotor flux, and torque and verified experimentally.
Keywords: Induction motor, Indirect Vector control, Rotor resistance estimation, ANN.
1. INTRODUCTION
For the high performance control, Induction motors are controlled from vector control
method and because of rugged in construction the I.M were employed for constant speed application.
In the few decades back after the development of DSP, microprocessor controllers they were
employed in adjustable speed applications. Separately excited D.C machine like performance can be
obtained by vector control (decoupling control). Vector control improves the dynamic response of
machine by providing independent control channels to torque and flux quantities. Since the rotor
flux position is mandatory in vector control, based on the method for which rotor flux position is
obtained vector control has subdivided into two methods: 1. Direct Vector Control. 2. Indirect Vector
Control. In the first method flux position is directly measured from position sensors which increased
the complexity of machine to the manufacturer. In the second method the flux is estimated from
voltage and current measurements in the feedforward manner [1]. In the indirect vector control slip
speed is added to actual speed from which synchronous speed is obtained there by flux position (θ) is
obtained by integration.
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But the main drawback of the indirect vector control is parameter variation. Since the slip
speed (ωsl) mainly depends on rotor resistance (Rr), which increases with temperature rise in
machine. So error in slip speed leads to wrong alignment of torque and flux vectors used in vector
rotation. Therefore there is need to estimate the rotor resistance such that estimated resistance track
with the changes in resistance used in controller.
There are several methods of rotor resistance identification proposed by different authors
namely stochastic estimator method known as Extended Kalman Filter [2][3], deterministic estimator
method known as Extended Luenberger Observer [ELO] [4][5], since these methods demands high
level computations based on 5th
order state space model and therefore the algorithms developed
increases the complexity. In [6] Model Reference Adaptive Systems (MRAS) scheme was
developed which adjusts rotor resistance by constructing reference model. In [7] with the help of
search coils, on the flux axis a sinusoidal current was injected. In [8] least square technique based on
recursive algorithm was developed, this method provides 30% approx error between actual and
estimated values. In [9] rotor resistance was identified by using Pseudorandom binary sequence into
flux axis, the main drawback of this method is convergence method is more than 30sec. In this paper
a rotor resistance is estimated from the flux calculated from the voltage model equations and flux
calculated from current model. From the error produced by these two models rotor resistance
algorithm based on ANN is developed which is used to tune rotor resistance in the form of weights
in current model and thus the performance of motor is improved.
II. INDIRECT VECTOR CONTROL
The ideal vector control of Induction Motor equations considering in synchronously reference
frame is given by
߰௤௥ ൌ 0 (1)
߰ௗ௥ ൌ ߰௥ (2)
From which we obtain the slip gain Ks which is useful in flux positioning and is given by
‫ܭ‬௦ ൌ ߱௦௟ ݅௤௦⁄ ൌ ‫ܮ‬௠ܴ௥ ‫ܮ‬௥߰௥⁄ (3)
It is evident from the equation (3) the slip gain constant Ks which is used to generate slip
speed command ωsl depends on rotor resistance and magnetizing inductance and rotor inductance and
also rotor flux. Treating all the parameters to be constant under steady state conditions except rotor
resistance which varies with temperature rise. From the voltage model the rotor flux linkage equations
are given by
‫ݒ‬ௗ௦ ൌ ܴ௦݅ௗ௦ାߪ‫ܮ‬௦
ௗ೔೏ೞ
ௗ௧
൅ ቂ
௅೘
௅ೝ
ቃ
ௗట೏ೝ
ௗ௧
(4)
‫ݒ‬௤௦ ൌ ܴ௦݅௤௦ା
ߪ‫ܮ‬௦
ௗ೔೜ೞ
ௗ௧
൅ ቂ
௅೘
௅ೝ
ቃ
ௗట೜ೝ
ௗ௧
(5)
III. EFFECT OF ROTOR RESISTANCE VARIATION
As the temperature of the machine rises during the normal running conditions of induction
motor the rotor resistance parameter would increases upto 50-100% of its nominal value. Therefore it
firstly effect on slip gain constant, which leads to wrong calculations of slip speed there by rotor flux
angle calculated with this value is incorrect. Also it effect on wrong calculation of unit vectors used

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in vector rotation. The block diagram of indirect vector control with rotor resistance estimation using
ANN which includes the structure of neural network system is shown in Fig.1.
Fig.1. Indirect Vector Control with rotor resistance control using ANN
IV. ROTOR RESISTANCE ESTIMATION
In the method proposed, the flux developed from the voltage model is compared with flux
developed from the current model; the error is given to ANN controller as input quantities and rotor
resistance estimated is given to the current model (neural model) in the form of weights. The
structure for the rotor resistance estimation is shown in fig.2.
The voltage model equations are already shown in (4) and (5). The current model equations
are given by
ௗ
ௗ௧
߰ௗ௥ ൌ
ோೝ௅೘
௅ೝ
݅ௗ௦ െ ߱௥߰௤௥ െ
ோೝ
௅௥
߰ௗ௥ (6)
ௗ
ௗ௧
߰௤௥ ൌ
ோೝ௅೘
௅ೝ
݅௤௦ െ ߱௥߰ௗ௥ െ
ோೝ
௅௥
߰௤௥ (7)
Writing the equations (6) and (7) in matrix form
ቌ
ௗట೏ೝ
೎೘
ௗ௧
ௗట೜ೝ
೎೘
ௗ௧
ቍ ൌ ቌ
ିோ௥
௅௥
െ߱௥
߱௥
ିோ௥
௅௥
ቍ ቆ
߰ௗ௥
௖௠
߰௤௥
௖௠ቇ ൅
௅௠.ோ௥
௅௥
൬
݅ௗ௦
݅௤௦
൰ (8)

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Equation (8) can be written as
ௗట೅ೝ
೎೘
ௗ௧
ൌ ቄቀ
1 0
0 1
ቁ ቀ
ିோ௥
௅௥
ቁ ൅ ߱௥ ቀ
0 െ1
1 0
ቁቅ ்݀߰௥
௖௠
൅
௅௠.ோ௥
௅௥
்݅௦ (9)
After sampling the data model (9) is given by
்݀߰௥
௖௠ሺ݊ሻ ൌ ‫1ݓ‬ ቀ
1 0
0 1
ቁ ்݀߰௥
௖௠ሺ݊ െ 1ሻ ൅ ‫2ݓ‬ ቀ
0 െ1
1 0
ቁ ்݀߰௥
௖௠ሺ݊ െ 1ሻ ൅ ‫3ݓ‬ ்݅௦ሺ݊ െ 1ሻ (10)
Where,
‫1ݓ‬ ൌ 1 െ ܴ‫ݎܮ/ݏܶݎ‬ ; ‫2ݓ‬ ൌ ߱௥ܶ‫;ݏ‬ ‫3ݓ‬ ൌ ܴ‫ݎܮ/ݏܶ݉ܮݎ‬
Equation (10) can be written as
்݀߰௥
௖௠ሺ݊ሻ ൌ ‫1ݔ1ݓ‬ ൅ ‫2ݔ2ݓ‬ ൅ ‫3ݔ3ݓ‬ (11)
Where, x1=൬
݀߰ௗ௥
௖௠
ሺ݊ െ 1ሻ
݀߰௤௥
௖௠
ሺ݊ െ 1ሻ
൰; x2=൬
െ݀߰௤௥
௖௠
ሺ݊ െ 1ሻ
݀߰ௗ௥
௖௠
ሺ݊ െ 1ሻ
൰; x3=൬
݅ௗ௦ሺ݊ െ 1ሻ
݅௤௦ሺ݊ െ 1ሻ
൰
Here x1, x2, x3 represents inputs to the network, and w1, w2; w3 represents weights to the network.
Fig.2. Structure of ANN for Rr estimation
Since w2 is already known and updated weights are only w1 and w3. Since the weights w1
and w3 involves rotor resistance parameter it is just sufficient to update either w1 or w3 to find the
estimated parameter. The weights update is done by using delta rule with back propagation
algorithm the weights w1 and w3 are found from training the network so as to minimize the error
function and which is given by
ࣟ ൌ
ଵ
ଶ
ሾ்݀߰௥
௩௠ሺ݊ሻ െ ்݀߰௥
௖௠ሺ݊ሻሿଶ
(12)
Weight update is done by using current weight and the correction required which is given by
‫1ݓ‬ሺ݊ሻ ൌ ‫1ݓ‬ሺ݊ െ 1ሻ ൅ ߟ∆‫1ݓ‬ሺ݊ሻ (13)
Where ߟ is known as training coefficient
∆‫1ݓ‬ሺ݊ሻ ൌ െ߲ࣟ/߲‫1ݓ‬ =െߜ‫1ݔ‬ where ߜ ൌ
డࣟ
డట೅ೝ
೎೘ሺ௡ሻ

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It is known that for the convergence of algorithm the current weight is added with most
recent weights
‫1ݓ‬ሺ݊ሻ ൌ ‫1ݓ‬ሺ݊ െ 1ሻ െ ߟ ߜ‫1ݔ‬ ൅ ߙ ∆‫1ݓ‬ሺ݊ െ 1ሻ (14)
Where, ߙ is known as momentum coffiecient
Now rotor resistance Rr can be calculated from w1 as
ܴ‫ݎ‬ ൌ ‫ݎܮ‬ሺ1 െ ‫1ݓ‬ሻ/ܶ‫ݏ‬ (15)
V. SIMULATION AND EXPERIMENTAL ANALYSIS
In order to validate the Indirect Vector control with rotor resistance estimation simulation
was done using MATLAB/SIMULINK with 1H.P 3-φ I.M. Initially the machine was simulated with
indirect vector control, since the main drawback of the indirect vector control is slip gain constant
highly dependent on Rr parameter, in order to examine the effect of rotor resistance a step variation
in rotor resistance was done, by varying the Rr the effect of Rr on torque, flux, and currents were
analysed and the proposed experimental work was shown in Fig.3. Experimentally in order to show
the effect of rotor resistance in IVC and additional resistance of 30 Ohms is connected abruptly in
star connected rotor winding and the changes in currents were noted as shown in Fig.4. to Fig. 9. and
there by developing the rotor resistance algorithm using VHDL program and embedded to
SPATRON 3A FPGA Controller, the parameter is adapted and thereby improving the performance
of machine shown in Fig.10. to 15.
Fig. 3. Experimental Setup for Rotor resistance Estimation
TABLE.1
V 3-Phase Voltage 415V
I Rated Current 2.4A
Vr Rotor Voltage 230V
P Poles 4
Rr Rotor Resistance 18.1 Ohms
Rs Stator Resistance 10.6 Ohms
Lls Stator Inductance 0.654 H
Llr Rotor Inductance 0.311H
Tr Rated Torque 33.075 N-m

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VI. SIMULATION AND EXPERIMENTAL RESULTS
Fig.4. Rotor resistance changed from 18.1 to 26.5
Fig.4. Torque is varying from 33.075 N-m to 32.1 N-m
Fig.5. Torque is varying from 33.075 N-m to 32.1 N-m (Experimental)
Fig.6. Rotor Flux varying from 1 to 1.1 T
Fig.7. Rotor Flux varying from 1 to 1.1 T (Experimentl)
0 0.5 1 1.5 2 2.5
10
20
30
Time in Sec
RrinOhsms
0 0.5 1 1.5 2 2.5
30
35
40
Time in Sec
TorqueinN-m
Actual torque
Estimated torque
0 0.5 1 1.5 2 2.5
0
1
2
Time in Sec
RotorFluxinT

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Fig.8. Current magnitude change from 2.4A to 2A
Fig.9. Current magnitude change from 2.4A to 2A (Experimental)
Fig.10. Tracking of Estimated Rr with Actual Rr at 0.9 sec
Fig.11. Torque compensates after 0.9 N-m
Fig.12. Torque compensates after 0.9 N-m
0 0.5 1 1.5 2 2.5
0
2
4
Time in Sec
CurrentinA
0 0.5 1 1.5 2
10
20
30
Time in Sec
RrinOhms
Actual Rr
Estimated Rr
0 0.5 1 1.5 2
30
35
Time in Sec
TorqueinN-m

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Fig.13. Rotor Flux compensates at 0.9sec
Fig.14. Rotor Flux compensates at 0.9sec (Experimental)
Fig.15. Current compensates at 0.9 Sec
Fig.15. Current compensates at 0.9 Sec (Experimental)
VII. CONCLUSION
The problem of rotor resistance variation in indirect vector control of induction motor is
analysed using MATLAB/SIMULINK software, and also experimentally by using SPATRON 3A
controller board. The changes in rotor flux, torque, and currents were compensated by developing
Rotor resistance algorithm using Artificial Neural Networks.
0 0.5 1 1.5 2 2.5
0
0.5
1
Time in Sec
RotorFluxinT
0 0.5 1 1.5 2 2.5
0
2
4
Time in Sec
CurrentinA

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REFERENCES
[1] B.K.Bose, Modern power electronics and AC drives (Pears Education, Singapore 2002)
[2] T. Kataoka, S.Toda, and Y.Sato, “On line estimation of induction motor parameters by
extended Kalman filter,” in Proc. Europe. Conf. Power Electron. Applicat., vol.4, 1993, pp.
325-329.
[3] R.S. Pena and G.M.Asher, “Parameter sensitivity studies for induction motor parameter
identification using extended Kalman filter,” in Proc. Europe. Conf. Power Electron.
Applicat., vol.4, 1993, pp.306-311.
[4] T.Du, and M.A. Brdys, “Implementation of extended Luenberger observers for joint state and
parameter estimation of PWM induction motor drive,” in Proc. Europe. Conf. Power Electron.
Applicat., vol.4, 1993, pp.439-444.
[5] T.Du, P.Vas and F.Stronach, “Design and application of extended observers for joint state and
parameter estimation in high-performance AC drives,” Proc. Inst. Elect. Eng-Elect. Power
Applicat., vol.142, no.2, 1995, pp.71-78.
[6] G.Griva, M.C. Ficcara, and F. Profumo, “Design of a speed regulator for induction motor
drives based on model reference robust control,” in Proc. IEEE Int. Symp. Ind. Electron.,
1997, pp. 484-488.
[7] D.Y.Ohm, Y.Khersonsky, and J.R.Kimzey, “Rotor time constant adaptation method for
induction motors using DC link power measurement,” in Proc. IEEE Ind. Applicat. Soc. Annu.
Metting, 1989, pp.588-593.
[8] N.R. Klaes, “Least Square Technique based online tuning of the rotor resistance in an inverter
fed induction machine with diret-self-control,” Europe. Trans. Electr. Power, vol.4, no.1,
1994, pp. 5-11.
[9] K.tungpimolrut, F.Z.Peng, and T.Fukao, “A robust rotor time constant estimation method for
vector control of induction motor under any operating condition”, in Proc. IEEE Ind. Electron.
Soc. Annu. Meeting, 1994, pp. 275.
[10] Prof. Hemant chouhan, Ritesh Kumawat and Dr. H. K. Verma, “Comparative Analysis of
Scalar and Vector Control Induction Machine Drive Through Modeling and Simulation”,
International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2,
2012, pp. 39 - 50, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
[11] Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “The Scheme of Three-Level Inverters
Based on Svpwm Overmodulation Technique for Vector Controlled Induction Motor Drives”,
International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2,
2013, pp. 245 - 260, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
[12] Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “Application of Neuro-Fuzzy Controller in
Torque Ripple Minimization of Vector Controlled Vsi Induction Motor Drive”, International
Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013,
pp. 121 - 127, ISSN Print : 0976-6545, ISSN Online: 0976-6553.