International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
Performance Analysis of Self-Excited Induction
Generator Using Artificial Neural Network
Ankit Bhatt1, Mohamed Samir2, Bharat Upreti3
1
P.G student, 2Assistant Professor, 3P.G student
1,2
Department of electrical engineering
DIT University, Mussoorie Diversion Road, Dehradun Uttarakhand India
3
Department of electrical engineering
G.B.Pant university of agriculture and technologyPantnagar, Uttarakhand, India
Abstract-Due to increasing demand of energy and
limitation of non-renewable energy resources, the need
of using renewable energy sources is increasing very
rapidly. The three-phase self excited Induction
Generator are very useful for generating power by use
of non-renewable resources. The paper presents the
analysis of SEIG by using artificial intelligence
technique which states that at certain value of speed
there are a set of voltage, frequency and magnetizing
reactance predicted by the ANN. ANN model is trained
by different set of data obtained from simulation of
machine using a suitable MATLAB program. The
comparison of calculated and predicted data confirms
the validity and accuracy of the ANN based model.
Keywords- Artificial Neural network, Self-excited
induction generator, magnetizing reactance, MATLAB
NOMENCLATURE
a
per unit frequency
b
per unit speed
Rs,Rr
per phase stator & rotor resistance
Xm
magnetizing reactance
X1s
per phase stator leakage reactance
X1r
per phase rotor leakage reactance
Xc
per phase capacitance reactance
Rl
per phase load resistance
f, v
per unit frequency & speed
s
slip, (a-b)/a
N
speed
ISSN: 2231-5381
I.
INTRODUCTION
The basic element required in the progress of a
country is its power capacity. Most of the electric
power generation today uses fossil fuels. The fossil
fuels as we know are limited in nature and are going
to decay, we need another source of energy which are
cheaper and are also reliable as compared to the
conventional sources of energy. This has pressurized
researchers to think about non-conventional sources
of energy. Non-conventional sources include solar
energy, wind energy, tidal energy, geothermal heat,
hydro etc. Another reason or adaption of nonconventional sources of energy is that they are easy
to handle eco-friendly and require less maintenance.
The main feature of non-conventional sources of
energy is that they create very less amount of
pollution.
Induction Generators are increasingly used with the
non-conventional sources for power generation. The
main reason behind the popularity of Induction
Generator for using it with non-conventional sources
of energy are reduced unit cost, reduced size,
ruggedness, brushless construction, absence of DC
power supply, ease of maintenance etc. Such
generators may be used for the electrification purpose
in remote areas. The operation of Self Excited
Induction Generator (SEIG) is useful under variable
speed operation; therefore it becomes the duty of a
researcher to investigate the behavior of specific
problem related issues of Induction Generator.
A proper circuit representation and accurate
mathematical modeling is essential in evaluating the
steady- state performance of SEIG for different
operating conditions. To calculate the steady state
performance of Self Excited Induction Generator,
analysts adopted different models, some researchers
[3]-[4] adopted the impedance model while some
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
used the admittance based model for such
calculations. It was found that most researchers used
the modeling which resulted in a single polynomial
equation of higher order in unknown generated
frequency and magnetizing reactance. An active
power source based new equivalent circuit model was
suggested by. In [5], an iterative technique has been
used to obtain the generated frequency of the Selfexcited Induction Generator. In order to compute the
non linear magnetization characteristics of Induction
Generator it is essential to develop a mathematical
model, terminal voltage can also be computed with
such modeling. Researchers have adopted various
techniques to estimate the performance of SEIG such
as piecewise linear approximation, genetic algorithm
etc. However in this context Artificial Neural
Network technique can prove to be a simple and
efficient method of modeling the SEIG.
In this paper ANN modeling to analyze the steadystate performance of SEIG has been proposed
through the conventional circuit. The ANN model is
trained by different set of data obtained from the
simulation of machine. The comparison of the data
gives the validity and accuracy of the proposed
model.
II.
MODELLING OF SEIG
The per phase equivalent circuit of SEIG for
the analysis of steady state operation is
shown in fig.1
jX2
R2/a-b
Im
E1
And
With low operating slips, above equation can be written as
Where generated frequency is
Omitting stator impedance and rotor reactance by using
approximate equivalent circuit gives the operating slip as
Initial value of frequency
jX1
R1/a
I2
Where,
will be
IL
I1
From above, the value of magnetizing reactance can be calculated
jXm
-jXc/a2as
Fig.1 per phase circuit representation of SEIG
R/a
V/a
The proposed model of the Self Excited Induction Generator has
been shown in fig.2
In this circuit, all the parameters are thought to be
autonomous of saturation aside from magnetizing
reactance. Analysis of equivalent circuit results in the
following equations for the steady-state operation.
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
powerful for calculating the non linear characteristics
of the Induction Machine.
II.
RESULTS AND DISCUSSIONS
Table 1 shows the comparison of proposed ANN
technique and results obtained from the simulation
model of Induction Generator (
). To
test the general capabilities of the neural network,
training is done with 100 samples, out of which 70%
samples are used for training purpose, 15% are used
for testing and 15% samples are used for validation
purposes.
Fig.2 Proposed model of SEIG
I.
ANN BASED MODELLING
Artificial Neural Network also called parallel
distributed processing systems and connectionist are
generally used for the non-linear modeling, system
identification and pattern association etc. In this
paper, the multilayer back propagation feed forward
neural network is used that gives a good
approximation of magnetization characteristics. The
structure of ANN is shown in fig 3.
Fig.3 non-linear model of Artificial Neuron
The number of input and output neurons depends on
the designer and the type of problem. In this paper
two layer feed forward back propagation neural
network has been used. The design of network and
selection of optimum training parameters are
performed by trial and error. Further, Levenburg
Marquardt training function is used which causes
fewer epochs as compared to other training function.
Therefore approximate results will be produced when
an input is applied to the network. This Training
method has been discovered to be exceptionally
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
Table 1 Comparison of Results of data set calculated and predicated
Speed
N
(rpm)
Voltage , (volts)
(calculated)
(predicated)
Magnetizing Reactance
,
(ohm)
Frequency , (Hz)
(calculated)
(predicated)
(calculated)
(predicated)
1400
103.01
103.50
46.13
46.07
112.78
112.72
1420
120.56
116.10
46.72
46.60
112.09
112.29
1440
138.72
138.90
47.43
47.42
112.14
112.30
1460
153.9
155.04
47.98
48.14
111.37
112.52
1480
166.13
166.49
48.74
48.78
112.33
112.91
1500
179.32
180.52
49.55
49.66
112.54
113.51
1520
191.63
191.54
50.24
50.19
113.71
113.43
1540
202.71
202.25
50.81
51.15
112.98
112.95
1560
213.65
212.54
51.47
51.55
112.93
112.89
1580
225.1
233.41
52.16
51.94
112.56
113.05
1600
234.57
271.93
52.79
52.59
112.38
113.37
Voltage(V)
The closeness of the results indicates the
accuracy of the proposed ANN technique.
300
200
100
0
Predicate
d
1200
1600
Frequency(Hz)
Table 1 Comparison of Results of data set calculated and predicated
54
52
50
48
46
44
1200
Calculate
d
1600
Calcula
ted
Predica
ted
Speed(Rpm)
Speed(Rpm)
Fig.4 Comparison of simulated and neural
network data of generated voltage
ISSN: 2231-5381
Fig.5 Comparison of simulated and neural
network data of generated frequency
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Magnetizing
Reactance(Xm)
International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
1
0.5
Calculated
0
1200
1600
Predicated
Speed(Rpm)
Fig.6 Comparison of simulated and neural
network data of generated magnetizing reactance
This paper attempts to use the given speed in order to
generate the suitable terminal voltage, frequency and
magnetizing reactance by the development of ANN
model which has been trained from the data obtained
from the data obtained by the test performed on the
simulation model of the machine. Comparison of the
results shows a close agreement between the
experimental data and that predicted by ANN, which
shows that such a simple and accurate model will be
useful to analyze the behavior of Self Excited
Induction Generator.
IV.
APPENDIX
Details of the machine:
Specifications
3-phase, 4-pole, star connected, squirrel
cage induction machine
2.2 kW/3HP, 230V, 8.6A
Parameters
R1=R2=8.04Ω, X1=X2=8.84Ω
Base Values
Base voltage =220V
Base current =4.96A
Base impedance =46.32Ω
Fig.7 Performance plot of ANN for magnetizing
reactance
V.
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\
Fig.8 Regression plot of ANN for magnetizing
reactance
III.
CONCLUSION
Self Excited Induction Generator are found to
be most suitable for wind energy change in remote
and breezy areas.
ISSN: 2231-5381
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