A New Switching Controller Based Soft Computing-High Accuracy
Implementation of Artificial Neural Network
Dr. Ammar Hussein Mutlag, Siraj Qays Mahdi, Omar Nameer Mohammed Salim
Department of Computer Engineering Techniques, Electrical Engineering Technical College, Middle Technical
University, Baghdad, Iraq.
[email protected], [email protected], [email protected]

Abstract
Space vector modulation (SVM) controller
is an advanced computation intensive pulse
width modulation (PWM) technique. System
performance can be accomplished by
applying a proper switching technique. To
obtain a sinusoidal AC output waveform, the
SVM switching technique is widely used
and implemented in the inverter control
algorithm to reduce harmonics. By
controlling the inverter switching scheme,
the harmonic content of the output voltage
can be minimized. The SVM suffer s from
the complex computational process es.
Therefore, this paper presents a new space
vector modulation controller based soft
computing -high accuracy implementation of
artificial neural network .
An artificial neural
network (ANN) structure is proposed to
identify and estimated the conventional
SVM for avoid ing the complex
computational problem and hence improve
the performance of the photovoltaic inverter
generation
. The ANN model receives the ??
voltages information at the input side and
generates the duty ratios (T
a, Tb, and Tc) as
an output. The training data for ANN is
generated by simulating the conventional
SVM. The total harmonic distortion (THD)
rate with ANN and conventional based SVM
methods are presented. Three indices
namely root mean square error (RMSE),
mean absolute error (MAE), and mean error
(ME) are used to assessment the
performance of the proposed ANN model.
Moreover, statistical analysis using
histogram method is presented as well for
further evaluating. The results show that the
proposed ANN model is significantly robust
to realize a favorable response compared
with the conventional SVM model .
Keyword: Soft computing; Artificial Neural
Network; Space Vector M odulation;
Inverter Controller.

1. Introduction
The voltage source inverter (VSI) has been
utilized in the last view decades in various
applications such as connect the
photovoltaic (PV) with load or with utility
grid 1. The performance of the VSI is
highly depends on the pulse width
modul ation (PWM) switching control
strategy 2. Therefore , many PWM
approaches have been mentioned in the
literature review such as carrier based pulse
width modulation, sinusoidal pulse width
modulation, and space vector modulation
(SVM) 3 -7. Among them, the SVM is the
dominant switching controller strategy 8.
Its importance comes from it is capable to
reduce the harmonic which is one of the
most important issues 9. However, the
main drawback of the SVM is the limiting in
the inverter switching frequency which
comes from the complex computational
process conducted by SVM . Therefore,
additional memory is required for real time
implementation . Gaballah et al. in 10
shown a way to decrease the complex
computations in SVM and thus applied in
real -time.
Recently, artificial intelligent systems
(AIs) have been reported in the literature to
deal with SVM drawbacks. Genetic
algorithm (GA) has been used to enhance the performance of the SVM through
decreases the complex computational
process
. The GA based SVM has been
utilized in 11 to solve the complex online
computation. Nonetheless, trap in local
minima is the main drawback of the GA .
Furthermore, the difficulty of solving the
multimodal problems and slow convergence
rate are also drawbacks of the GA . Another
type of AIs which is fuzzy logic system
based SVM has been revealed as well in the
last years. In 1 2, a comparison of the fuzzy
logic based SVM for voltage source inverter
has been presented. The performance of the
developed fuzzy logic (FL) base d SVM has
been compared with conventional SVM.
However, the time consumption of the FL
tuning is the main drawback. Moreover, the
FL can explain the knowledge but cannot
learn from the training. To overcome the
problem of the artificial intelligent systems ,
developed machine learning systems have
been used. Artificial neural network (ANN)
is one of the most important methods in the
machine learning systems which has been
used in many applications. Tracking of the
maximum power based ANN has been
proposed in 13. In this study, the
forecasting of the maximum voltages and
currents have been achieved using ANN.

Alternatively; the ANN can be utilized to
improve the performance of the SVM. In this stud y, a new space vector
modulation controller based soft computing-
high accuracy implementation of artificial
neural network is proposed. This paper
includes six sections. Section 2 explains the
conventional space vector modulation.
Developed artificial neural network model
has been introduced in section 3 ; meanwhile
the proposed artificial neural network based
SVM has been presented in section 4 .
Results and discussion has been drawn in
section 5. Finally, the conclusion has been
portrayed in section 6.
2. Conventional Space Vector
Modulation
The space vector modulation (SVM) is the
most common switching controller because
of their high efficiency capabilities and easy
control 14. The SVM is depended on the
three phase quantities which are V
a, Vb, and
V
c. To simplify the calculations, t he three
phases ( V
a, Vb, and Vc) can be converted to
?? voltages using Clark’s transformation as ,

? ?
?
?
?
? ?
?
?
?
?
? ?
?
?
?
??
?
=
?
? ?
?
?
?c
b aV
V
V
/
/ /
/
V V
2
3
2
3
0 2
1
2
1
1
3 2?
?
(1)

Using the ?? voltages, the reference
voltage (V
ref) and angular (?) between
voltages (V
? and V?) can be written as ,
2
2
?
?V
V
Vref+
=
(2)

? ?
? ?
? ?
? ?
=
?
?
?
?
V
V
tan
1

(3)

The output signal is consists from eight
vectors which are V
0 to V7. The vectors V1,
V
2, V3, V4, V5, and V6 are known as non- zero
vectors whereas the V
0 to V7 are known as
zero vectors. These eight vectors will form
the output signal in a form of hexagon.
Hence, the time share (T
1 and T2) can be
calculated using (V
ref) and (?) inside the
hexagon.
Eight topologies of switches will
be realized when (V
ref) passes through the
sectors which mean one cycle is completed .

3. Devel oped Artificial Neural
Network Model
The artificial neural network (ANN) is a
powerful parallel information processing
system which draws the mapping between
the inputs and outputs. The ANN consists
from the neurons connected by the links
which are passing the information from the
inputs to the outputs 15. Simply, the inputs
neurons in the input layer are relay the input
signals to the neurons in the hidden layer
which i s connected to the output layer where
the final values are generated. Many
artificial neural networks (ANNs) have been
reported in the literature such as hebb
network, adaline network, perceptron
network, radial basis function, probabilistic
neural network, and back- propagation neural
network (BP -NN) 16. Since the BP -NN is
multi -layered, fully connected, and fe ed
forward structure ; therefore it is employed
in this study. It is simply decrease the mean
squared error of the output calculated by the
network.

Problems in numerous subjects can be
solved utilizing the BP -NN. The goal from
the training of the neural network is to
achieve the response of the training data,
additionally, achieve reasonable response to
the inputs that are similar to the training
data. The training of the BP -NN should passes through three steps which are
feedforward
of the training data,
backpropagation to calculate the error, and
update the weight s. T he appropriate weights
for the links are being found after the
training process is achieved.
Regarding to the hidden layers, increase
the number of the hidden layers will
increase the resolu tion but they will lead to
complex and long computational process.
Therefore, s ingle hidden layer is used in this
study to reduce the computational process .
Since the proposed system consists from the
one hidden layer, the net input of the hidden
layer is define as,

?
=
+
=
P
j j
i
ij
j
b
x
v
net
1 0
(4)

Three types of activation functions have
been mentioned in the literatures which are
identity function, binary sigmoid function,
and bipolar sigmoid function. However, the
bipolar sigmoid function is the
recommend ed function in the hidden layer
since its range belong to ( -1,1). The
response of the hidden layer using bipolar
sigmoid function is describe as,

1
1 2
?
+
=?
net
je
Z
(5)

The final layer of the proposed ANN
model is the output layer. The net input of
the output layer is written as,
?
=
+
=
M
k k
j
jk
k
b
Z
w
net
1 0
(6)

Since the target data are continuous
rather than binary; the identity function is
preferable to use in the output layer in this
work. The response of the output layer using
identity function is define as,
k
knet
)
net
(
f =

(7)

4. Proposed Artificial Neural
Network Based SVM
The block diagram of the proposed ANN
based SVM for two -level inverter is shown
in Fig. 1. The ANN model has two inputs
and three outputs. The inputs are the voltages (
V
? and V?), meanwhile the outputs
are the duty ratios (T
a, Tb, and Tc). Hence,
the ANN model should be developed to
predict the duty ratios (T
a, Tb, and Tc) which
are compared with sampling period to
generate the switching control signals for
the VSI . The conventional SVM is used to
generate the training data for the ANN
model. The Levenberg- Marquardt back-
propagation algorithm has been employed to
train the ANN model to define the mapping
between the inputs (V
? and V?) and the
output s (T
a, Tb, and Tc).
Regarding to the number of the hidden
nodes, s mall number of hidden nodes causes
high error; mean while large number of
hidden neurons causes high generalization
error and complex computational process .
Therefore, numerous studies have been
discussed the optimal number of the nodes
in the hidden layer which in turn lead to
optimal performance of the ANN. The
summery of the theses studies concluded
that the best number s hould be around two
to three times of the total number of input
and output nodes. Thus, in this study, ten
nodes have been used in the hidden layer.

Fig. 1. The block diagram of the proposed ANN based SVM for VSI

The proposed ANN is depicted in Fig. 2. It consists from three layers; input layer , hidden
layer , and output layer which are 2- 10- 3 neurons, respectively. The proposed ANN receives the
V
? and V? voltages as inputs and generates Ta, Tb, and Tc as output s. Hence, the input of each data
sample consists of two input s values (V
? and V?) and three output s or target values (Ta, Tb, and
T
c). The training of the proposed ANN is repeated for all data samples to achieve one epoch. The
process of the training will continue until achieve the goal of the error or complete the predefined
epochs. Finally, the proposed ANN can be utilized to generate the duty ratios (T
a, Tb, and Tc)
after the end of the training proc ess when it is exposed to new input data. The proposed ANN
can be assessment using various error type indices such as root mean square error (RMSE), mean
absolute error (MAE), and mean error (ME) which are defined as,
(8)
(9)
(10)

?V
?V
aT
bT
cT
Input layerHidden layer
Output layer

Fig. 2. The architecture of the proposed ANN model

5. Results and Discu ssion
The performance of the proposed artificial
neural network based space vector
modulation (ANN-SVM) is investigated
using MAT LAB environment and compared
with conventional space vector modulation
(CON -SVM) . As explained previously, the
artificial neural network is trained to
generate the duty ratio s (T
a, Tb, and Tc). To
achieve the best results, the ANN is trained
using back- propagation method.
Since the frequency used in this study is
5 kHz, thus the duty ratios are various from
zero to 2E -4. The duty ratios (T
a, Tb, and Tc) corresponding to the conventional and ANN
space vector modulation are depicted in Fig.
3
to Fig. 5. These figures show three cycles
of the duty ratios (T
a, Tb, and Tc). They are
clearly showed that the responses of the
ANN -SVM are stable and very similar to the
responses of the CON -SVM without any
negative impact such as oscillation.
Moreover, the response of the ANN-SVM
distinctly succeeds to track the exact CON-
SVM. Hence, Fi g. 3 to Fig. 5 responses
indicates that the proposed ANN model is
significantly robust to realize a favorable
response.

(a)
(b)
Fig. 3. Duty ratio (T
a) using (a) conventional and (b) ANN space vector modulation
0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5x 10
-4
T ime (s)
Duty Ratio
0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5x 10
-4
T ime (s)
Duty Ratio

(a)
(b)
Fig. 4. Duty ratio (T
b) using (a) conventional and (b) ANN space vector modulation

(a)
(b)
Fig. 5. Duty ratio (T
c) using (a) conventional and (b) ANN space vector modulation
Fig. 3 to Fig. 5 do not clearly show how the ANN -SVM response is close from the CON –
SVM response. For that reason, the errors between the CON -SVM response and ANN -SVM
response are drawn in Fig. 6. The errors of T
a, Tb, and Tc for three cycles show very small values
which indicate a high performance of the ANN -SVM.
0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5x 10
-4
T ime (s)
Duty Ratio
0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5x 10
-4
T ime (s)
Duty Ratio

0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5
x 10
-4
T ime (s)
Duty Ratio
0.020.030.040.050.060.070.08-0.5
0
0.5
1
1.5
2
2.5x 10
-4
T ime (s)
Duty Ratio

Fig. 6. Errors in duty ratios (T
a, Tb, and Tc)

0.020.030.040.050.060.070.08
-5
0
5 x 1 0
-6
Error ( T
a )
0.020.030.040.050.060.070.08-5
0
5x 1 0
-6
Error ( T
b )
0.020.030.040.050.060.070.08-5
0
5x 1 0
-6
T ime (s )
Error ( T
c )

Three types of indices are used to evaluate the responses of the ANN model as can be shown
in Table1. The first index is the root mean square error (RMSE). This index shows very low
values which are 8.1249E -07, 9.2207 E -07, and 7.1081 E -07 for T
a, Tb, and Tc, respectively.
The mean absolute error (MAE) are calculated for the T
a, Tb, and Tc as the second index which
are 6.3169E -07, 7.0460 E – 07, and 5.2679 E – 07 for T
a, Tb, and Tc, respectively. Finally, the
mean error (ME) is used as the third index. The ME again shows very small values for T
a, Tb,
and T
c which are 7.0615 E -08, 5.3579 E -08, and 5.8819 E -08, respectively. The low values from
theses indices (RMSE, MAE, and ME) indicate a high accuracy of the proposed ANN -SVM
model.
Table 1: RMSE, MAE, and ME indices
Indices Ta Tb Tc
RMSE 8.1249E-07 9.2207 E-07 7.1081 E -07
MAE 6.3169E-07 7.0460 E-07 5.2679 E -07
ME 7.0615 E -08 5.3579 E -08 5.8819 E -08

For further evaluati on for the performance of the proposed ANN -SVM, the histogram
statistical analysis is used which is the most popular statistical analysis. It describes the feature
representation and frequency distribution 17. Fi g. 7 to Fi g . 9 show the graphical histogram of
the errors between the conventional and ANN duty ratios T
a, Tb, and Tc, respectively. The x -axis
represents the class boundaries whereas the y -axis represents the frequencies of the class es. The
bar in the class becomes hig her when the numbers of the points are high; meanwhile the bar
becomes lower when the numbers of the points are low. It is important to show that the measured
values by the ANN -SVM model are compatible with the measured values by the CON -SVM.
The graphical of the histogram analysis show that the values based ANN -SVM model are
comparable with those values CON -SVM where a very small errors have been found. Most of
the errors values are found to be in the middles bars which are the lowest error bars.
Furthermore, t he values of the errors are various from -2.5E -6 to 3E -6 which are very small
values. Moreover, the distributions of the errors are very close to normal distribution. This
finding shows high accuracy and performance of the proposed ANN -SVM mo del.

Fig. 7. Histogram of the error between the conventional and ANN duty ratio T
a

Fig . 8. Histogram of the error between the conventional and ANN duty ratio T
b

Fig . 9. Histogram of the error between the conventional and ANN duty ratio T
c
-2.5-2-1.5-1-0.500.511.52
x 10
-6
0
1000
2000
3000
4000
5000
6000
Error
Frequency
-2-10123
x 10
-6
0
1000
2000
3000
4000
5000
6000
Error
Frequency
-2-10123
x 10
-6
0
1000
2000
3000
4000
5000
6000
7000
Error
Frequency

The last assessment is conducted based
on the quality of the output waveforms. One
of the criterions that is used to show the
quality of the output waveforms is the total
harmonic distortion (THD). The researches
aims always to decrease the value of the
TH D which mean s increase the quality of
the output waveforms. According to the
IEEE Std 929 -2000 standard, the value of
the measured THD should be less than 5% 18.
Fi g. 10 to Fig. 12 depicted the THD
rates of the conventional and ANN space
vector modulation for V
a, Vb, and Vc
respectively. These figures clearly show that
the proposed A NN-SVM model succeed to
achieve low THD rates which are 0.40%,
0.49%, and 0.53% for V
a, Vb, and Vc
respectively . T hus, the proposed ANN -SVM
model is implemented successfully
with
high efficiency.

(a)
(b)
Fig. 10. THD of the V
a using (a) conventional and (b) ANN space vector modulation

(a)
(b)
051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.013 , THD= 0.43%
Mag (% of Fundamental)
051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.014 , THD= 0.40%
Mag (% of Fundamental)
051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.013 , THD= 0.56%
Mag (% of Fundamental)
051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.013 , THD= 0.49%
Mag (% of Fundamental)

Fig. 11. THD of the Vb using (a) conventional and (b) ANN space vector modulation
(a)
(b)
Fig. 12. THD of the V
c using (a) conventional and (b) ANN space vector modulation

Finally, the THD rates are presented in Table 2 to show the difference between the CON –
SVM and the ANN -SVM model. The THD rates in T able 2 show that the ANN-SVM model
succeed to accomplish the IEEE Std 929 -2000 standard. Furthermore, the ANN -SVM model
gives better results with high quality compar ed to CON-SVM model. Hence, the performance of
the proposed system is highly improved.
Table 2: The comparison of the THD rates
Voltages CON-SVM ANN-SVM
;#55349;;#56393;;#55349;;#56398; 0.43 0.40
;#55349;;#56393;;#55349;;#56399; 0.56 0.49
;#55349;;#56393;;#55349;;#56400; 0.61 0.53

051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.014 , THD= 0.61%
Mag (% of Fundamental)
051015200
0.2
0.4
0.6
0.8
1
Harmonic order
Fundamental (50Hz) = 1.014 , THD= 0.53%
Mag (% of Fundamental)

6. Conclusion
This paper presented a new space vector
modulation controller based soft computing-
high accuracy implementation of artificial
neural network to solve the complexity in
the computational process of the SVM. The
modified ANN model has been train to
receive the voltages V
? and V? as inputs and
generate the duty ratios ( T
a, Tb, and Tc) as
outputs . The training data have been
generated by simulates the conventional
SVM. The ANN model has been trained
using Levenberg- Marquardt back-
propagation algorithm to draw the mapping
between the inputs ( V
? and V?) and outputs
(T
a, Tb, and Tc). Three indices namely root
mean square error (RMSE), mean absolute
error (MAE), and mean error (ME) have
been used to assessment the response of the
ANN model. These indices show very low
values which demonstrate the robustness of
the ANN -SVM model . The quality of the
output waveforms signals based ANN -SVM
have been calculated using total harmonic
distortion (THD). The THD values based
ANN -SVM have been found to be 0.40%,
0.49%, and 0.53% for V
a, Vb, an d Vc,
respectively; whereas the THD values based
CON -SVM have been found to be 0.43%,
0.56%, and 0.61%. This finding show that
the performance of the VSI based ANN -SVM has been significantly
improved by
decrease the THD and decrease the complex
computational processes as well. Finally,
statistical analysis using histogram method
has been employed for further evaluation.
The histogram method show a normal data
distribution and very small error values.
Thus, the proposed ANN model can be
ef ficiently used to highly improve the whole
system.

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