of
E.
M.
S.
Department of Electrical Engineering, Faculty of Engineering, Menofiya University, Egypt.
E-mail : ezeldin2000@,vahoo.com
This paper presents an approach for efficiency optimization of a vector controlled induction motor drive. The optimum flux-producing current is obtained using an artificial neural network. The artificial neural network model is established using Matlab/Simulink and based on the load torque and speed data of an indirect vector-controlled induction motor drive. The change of iron core loss resistance due to flux and frequency variation is taken into consideration.
Simulation results of the proposed approach show a significant improvement in energy saving and efficiency optimization. main problem of this approach is in the requirement of the exact values of machine parameters which are unknown to the users and may vary due to changes in operating conditions. The second approach, named on line efficiency optimization control, depends on searching for flux levels which minimizes 'the machine's measured input power for a given torque and speed [4,5,6]. This approach does not require a priori knowledge of machine parameters. It is not only insensitive to parameter Variations but also applicable to partially unknown machine. However, it is slow in locating the energy efficient point and may not work well due to measurement noise.
I. INTRODUCTION
Efficiency improvement in adjustable speed drives has been getting a lot of attention in recent years. Higher efficiency is important not only from the view points of energy saving and cooling system operation , but also from the broad perspective of environmental pollution control. From the energy saving view points, it is well known that more than 50 % of the total electric energy is consumed by motors. Induction motors, especially, squirrel-cage types, are widely used in electrical devices and are responsible for most of the energy consumed by electric motors.
For a given motor, operation under rated condition
(with rated load torque and rated speed) is highly efficient. However, in many applications, a motor drive operates far from the rated operating point.
Under these circumstances, the motor efficiency becomes low. This is due to the imbalance between iron and copper losses. It is well known that, for a given operating point, the losses in an induction machine can he minimized by adjusting appropriately the level of magnetic flux [I]. This is due to the fact that the electromagnetic losses in a machine are a direct function of the magnetic flux and by a proper adjustment of the flux, an appropriate balance between iron and copper losses can be achieved.
In this paper, an artificial neural network based as a predictor for optimum flux is presented for an indirect vector-controlled induction motor drive. The load torque and speed are used as inputs for the neural network. The optimum flux-producing current is taken as the neural network output. The neural network model is established using Matlab/Simulink and based on the data of an indirect vector-controlled induction motor drive. Two models are presented with the effect of iron core loss. The magnetizing resistance is taken as a constant in the first model. The change in the magnetizing resistance due to the flux and frequency variation is taken into consideration in the second model. Simulation results of the proposed approach show a significant improvement in energy saving and efficiency optimization.
11. SYSTEM CONFIGURATION
Figure 1
shows an indirect vector-controlled induction machine drive with an artificial neural network (ANN) based as a predictor for optimum magnetizing current
(LJ. of vector control, the synchronously rotating vector components of stator current IT5 and lh are controlled independently to control the torque and rotor flux, respectively. The electromagnetic torque of the induction motor under field orientation can be expressed as :
In general, there are two different approaches to improve the induction motor efficiency especially under light-load conditions [Z-61. The first approach, named loss model controller, depends on a motor loss model to compute the optimum flux analytically [2,3].
The main advantage of this approach is the simplicity and does not require extra hardware. However, the
0-7803-7906-3/03/$17.00 02003 IEEE. 2543
Te =
(-)(-)(-hdJqr ........ ,..... ... .....
(1)
2 2 L,
Where P is the number of poles, L, is the magnetizing inductance and L, is the rotor inductance.
This is similar to that for a separately exited DC machine. The rotor flux-linkage qdr is exited by I,

through the dynamic delay due lo the rotor circuit and given by : induction machine[7-91. In this paper, two models are presented with the effect of core loss. The first model takes R,, constant while the second one takes R, variable.
I
DC link
Fig. 1 System configuration
The symbol p in Eq. (2) is the time derivative operator
(d/dt). At steady state, Eq. (2) becomes
( p d , =
L,I, and hence we have
3 P L;
Te = (-)(-)(-)IdsIqs
2 2 L ,
..............................
(3)
As shown in Fig. 2, for a given operating point (torque and speed), the rotor flux is decreased by reducing the magnetizing current (Ib), which ultimately results in a corresponding increase in the torque current (I,)
(normally by action of the speed controller), such that the developed torque remains constant. As the flux is decreased, the iron loss decreases with the attendant increase of copper loss. However, the total system
(converter and machine) loss decreases, resulting in a decrease of dc link power. The flux is continued decreasing until the system settles down at the minimum input power point A as shown in Fig. 2, hence, getting the optimum magnetizing current IdsoP at given operating point. At any operating point, the
ANN will be used to give lhop.
111. MODELING OF THE DRlVE SYSTEM
T
I,(rated)
Idmp
Id. + Decreasing
Fig. 2 Changes in cnre and copper losses with changing flux producing current I&
The value of the magnetizing resistance R, used to model iron core loss is determined from standard no- load test. Since, the vector controlled induction machine operates at variable levels of command flux
, then iron loss resistance would need to be represented as a function of both fwquency and flux. The appropriate values of resistance for various flux levels and frequencies are also obtained from the standard no load test. The iron loss resiritance can he expressed experimentally as :
R , = f
'p ...................... ...( f k d (P rdrd
R,, : the value of resistance at rated frequency, j L e d and rated flux,
To study the effects of parameter variations in an induction motor drive running under rotor flux orientation, with an artificial neural network based predictor, and to develop a suitable controller, extensive simulations are necessary in that environment. Matlab Neural Network toolbox software was found to have adequate capacity to develop these types and models. The models developed with Simulink posses very good interface and debugging options with the above toolbox. Iron core loss is neglected in standard theory of vector control of AC drive. However, recent researches indicate that iron loss may play an important role in establishing accurate rotor flux oriented control of
The dynamic model of an induction motor in a stationary reference frame, as shown in Fig. 3, can be written as:-
V; = R, I:$
df
.....................................
( 5 )
VA = RsI& d
+-(P* dt s
.....................................
V s
' 9
= R I s r
97 d df s
..........................
( 6 )
(7)
V;, = RF12r d
+-'pir +w,(P;, df
..........................
(8)
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~ configuration for a particular application. Several NN stmctures have been test.ed and evaluated. The design criteria for the present study are: a short training time, a small error and a small number of hidden layers. The
NN is shown in
Fig. 4. The model has one input layer , one hidden layer and one output layer. The input layer includes two neurons tn which the desired rotor speed or and load torque T, are connected as inputs to network. The output layer has only one neuron for the magnetizing current
Idr
The neurons in both input and output layers have linear transfer functions. The hidden layer has nine neurons where each of these neurons has a tan-
sigmoid transfer function. Fig. 5
shows in details the view of one neuron. The weights are used to adjust the relation between inputs and outputs. Biases have a constant input of 1 which is applied to all neurons in the neural network except the input layer. n
(b)
Fig, 3. d-q axis equivalent circuits of an induction motor in stationary reference frame (a) q-axis circuit,
(b) d-axis circuit.
For squirrel cage motor, V:, = V:, =
.
Symbols !',I,
'p denote voltage, current and flux linkage, respectively. Electrical rotor speed is cor
Leakage inductances are identified with index I , index m denotes parameters and variables associated with main (magnetizing) flux. Index a is used for parameters and variables associated with iron core loss. Superscript s denotes for stationary reference frame.
IV. PRINCIPLES OF THE NEURAL NETWORK
MODELING input layer
(linear) i \ j hidden layer
~ output layer
;
(tan-sigmoid) j
( b e a r )
Fig. 4
NN structure
PI ,.., ,..., Po :inputs
Artificial neural network consists of a number of interconnected processing elements called neurons. A neuron can be modeled to perform a mathematical function such as a pure linear function, tan-sigmoid function, log-sigmoid, etc. The artificial neural network can be trained to solve complex nonlinear functions with variable parameters, which may not be attainable by conventional mathematical tools. This feature is particularly suitable to the present paper , where the optimal rotor flux value is a non linear function of both rotor speed and load torque.
A. Neural Network Structure
Fig. 5 . A detailed view of one neuron
B. Neural Network Training
Once the network weights and biases have been initialized to random values, the network is ready for training . During training, the weights and biases of the network are iteratively adjusted to minimize the network performance function and to establish the prescribed input-output pattern . The performance function used is mean square error (mse) which is the
There is no specific method to select the best NN
2545

~
~ average squared error between the network outputs and the target outputs. The training algorithm uses the gradient ofthe performance function to determine how to adjust the weights to minimize performance. The gradient is determined using a technique called back- propagation, which involves performing computations backwards through the network. In this paper, the NN will be applied to an indirect vector-controlled induction motor drive. The steady-state speed wr and torque TI the machine are used to predict the optimal magnetizing current lb. The speed and torque values are accessible from the indirect vector controlled induction motor drive models (with R, constant and with R, variable). The current
Id, to each w, and T, combination is obtained by adjusting Ib in the vector control loop until the input power to the
drive reaches the minimum, as shown in Fig. 2.
In this study, 25 sets of input-output are sufficient to train the network to predict correctly the optimal value of I&. In the input training data, the speed varies as follows:lO%, 30%, SO%, 70% and 100% of its rated values. Corresponding to each speed, the load torque has also five different values (IO%, 30%, 50%, 70% and 100% of its rated values). In the back-propagation training,_the small. changes to neuron's weights and biases are made in the direction that minimize the performance. This direction is found by taking only the sign of the derivative to determine the direction of the weight update. The magnitude of the derivative has no effect on the weight update
,
The size of the weight update is determined by a separate value. The performance h c t i o n is set to lo4 as stopping condition for training. Two 3-D plots for each output of the trained NN are shown in Fig. 6, which are sufficiently accurate in representing the original training data.
8.b and 8.c, a reasonable amount of power can be saved by implementing the pi-oposed control scheme.
Fig. 9 shows the dynamic response of the model (with
R, variable ) under variable load torque. The speed reference is set to its rated value while the load torque is varied in steps. From Fig. 9, it is shown that a good performance in speed control is achieved with a minimum overshoot.
1
V. SIMULATION RESULTS AND DISCUSSIONS
A 0.75 HP induction motor was used as a case study.
The parameters of the motor were determined experimentally and given as follows:.
3-phase, 0.75 HP, 380V, poles,
R,=16.68R,R,=8.844R,x~l2n,x,=20.5R,
1
The proposed models have been implemented using
Matlab/Simulink. The gains of the speed controllers are selected after many tries as : kp = 10 , ki
=
30.
Figures 7 and 8 show the drive efficiency, the power reduction and the percentage of power reduction for both R, constant and R, variable, respectively. The results for the model with R, constant are not acceptable because the magnetizing resistance R, should be variable due to frequency and flux changes.
With optimal flux operation, a significant efficiency improvement is obtained compared with rated flux operation as shown in Fig. 8.a. The efficiency gains are increased, especially at light loads. From Figures
Fig. 6 Response of the trained neural network for the model with (a) R,constant, (b) R, variable
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0 8
-
2 v
4
O''
B
E
VJ 0.2
-
2 -
,I a
,:;.*
;', ;
_ _ - -
- .,,
I > I 2
,I -
'r
~,
-
,:a,-- __
,:J-
_ - - w, = 1 p.u
_ _ - -
- I - - - - - a,= p.u
_ _ - - - wr 0.5 p.u
- - - w, = 0.3 p.u
0.0.
0 0
' I
0.2
I I
0 1
I
0.6
, I
0.8 r
-
0.4 h
?
PI i
O''
-
B ill 0.2
-
0.0
0.0
Or=
I p."
----- rated flux optimum flux
0.2 O I
Load Torque
@.U) la)
0 0 w, = 1 p.u
0.8
$
- 6 1
E
.c
/
, U = I p.u w,
= 0.7 p.u
0.5 p.u w,
=
0.3 p.u w, = 0.5 p.u a
Load Torque @.U)
0 2 0 1
Load Torque
@.U)
(b)
0 6 0 8
Load Torque (p.u)
(C)
Fig. I Model performance with R, constant a) system efficiency. b) power reduction. c) percentage of power reduction.
2547
Load Torque (pu)
(C)
Fig. 8 Model performance with R, variable a) system efficiency. b) power reduction. c) percentage of power reduction.

.......................
1. 0.5 ...............................
/n
9
.................................
... a m
0.995
Time (sec)
Fig. 9 Dynamic response of the closed-loop model
(with R, variable ) under variable load torque.
VI. CONCLUSION
An artificial neural network based efficiency optimization control for a voltage source inverter fed induction motor drive has been presented. The technique trains a neural network model utilizing the speed and load torque data of the motor and estimates the optimum rotor flux excitation current for the vector controller. The change of iron core loss resistance due to flux and frequency variation plays an important part and should be taken into consideration.
Simulation results show a significant improvement in energy saving and efficiency optimization.
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