Real Coded Genetic Algorithm Based Improvement of Efficiency in Interleaved Boost Converter

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 5, No. 4, April 2015, pp. 529~540
ISSN: 2088-8694  529
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
Real Coded Genetic Algorithm Based Improvement of
Efficiency in Interleaved Boost Converter
M. Arun Devi*, K. Valarmathi**, R. Mahendhran*
* Departement of Electrical and Electronics Engineering, P.S.R Engineering College
** Departement of Electronics and Communication Engineering, P.S.R Engineering College
Article Info ABSTRACT
Article history:
Received Oct 4, 2014
Revised Dec 17, 2014
Accepted Jan 8, 2015
The reliability, efficiency, and controllability of Photo Voltaic power systems
can be increased by embedding the components of a Boost Converter.
Currently, the converter technology overcomes the main problems of
manufacturing cost, efficiency and mass production. Issue to limit the life
span of a Photo Voltaic inverter is the huge electrolytic capacitor across the
Direct Current bus for energy decoupling. This paper presents a two-phase
interleaved boost converter which ensures 180 angle phase shift between the
two interleaved converters. The Proportional Integral controller is used to
reshape that the controller attempts to minimize the error by adjusting the
control inputs and also real coded genetic algorithm is proposed for tuning of
controlling parameters of Proportional Integral controller. The real coded
genetic algorithm is applied in the Interleaved Boost Converter with
Advanced Pulse Width Modulation Techniques for improving the results of
efficiency and reduction of ripple current. Simulation results illustrate the
improvement of efficiency and the diminution of ripple current.
Keyword:
Boost Converter
Interleaved Boost Converter
PI Controller
Real Coded Genetic Algorithm
Solar Energy
Copyright © 2015 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Dr.K.Valarmathi,
Departement of Electronics and Communication Engineering,
P.S.R Engineering College,
Sevalpatti, Sivakasi -626140.
Email: krvalarmathi@yahoo.co.in
1. INTRODUCTION
A Grid-synchronized Photo Voltaic (PV) power system is constructed from a group of power
converters a DC–DC converter ensuring the Maximum Power Point Tracking (MPPT) cascaded by a grid-
synchronized inverter [1], [2]. For a PV power generation system, the actual power-generating device is the
solar panel, and it has a longer life than the power converters. Photo Voltaic module embedded power-
electronics topology derived from a battery equalizer, which eliminates the multiple maximum power point
peaks common to partial shading in PV modules [3]. In particular applications such as military uses in a
battlefield or in extreme weather conditions, repair or alternate of the converter is difficult, and a highly
reliable PV-based power system, which is compact and highly mobile in nature, is needed. Abusaleh M.
Imtiaz et al [4] describes the power Metal Oxide Semiconductor Field Effect Transistor (MOSFET) is to
ensure that the uninterrupted operation of the inverter, even though it gives higher manufacturing cost. This
high cost could be eliminated using high band-gap semiconductors.
For designing high efficiency solar power systems, a suitable DC-DC converter is necessary. The
DC-DC Boost converter is used to increase the voltage level in the solar system. In order to improve the
efficiency of boost converter, a two phase interleaved boost converter is used. The use of a Switched
Capacitor (SC) DC–DC converter for tracking the Maximum Power Point (MPP) of a photovoltaic array with
the possibility of partial shading is described in [5]. The SC converter topology can be reconfigured to
maximize conversion efficiency depending on the solar radiation and load. Generally speaking about high

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IJPEDS Vol. 5, No. 4, April 2015 : 529 – 540
530
step-up DC–DC converters for these applications have the following features such as high step-up voltage
gain, high efficiency. Naayagi et al. [6] explained the steady-state analysis of the bidirectional Dual Active
Bridge DC–DC converter. An analysis of Zero Voltage Switching (ZVS) boundaries for the buck and boost
modes while considering the effect of snubber capacitors on the DAB converter is also presented.
Boost converters are widely used as power factor corrected pre regulators. Due to an inductor of
boost converter the ripple current is increased, harmonics also increased and power factor is reduced. In high
power applications, interleaved operation of two or more boost converters has been proposed to increase the
output power and to reduce the output ripple. Genc et al. explored [7] a non isolated, high boost ratio hybrid
transformer DC–DC converter with applications for low-voltage renewable energy sources. The proposed
converter employs a hybrid transformer to transfer the inductive and capacitive energy and then achieving a
high boost ratio with a smaller magnetic component. However, the drawback of this converter is that the
voltage across the switch is very high during the resonance mode components [8]. Lee et al. [9] described the
optimal design of the resonant components and the interleaved method is proposed for resonant current
reduction. That the interleaved method distributes the input current to each phase, the current rating of the
switching devices can be decreased by using interleaved method. Also, it can reduce the input current ripple,
output voltage ripple, and size of the passive. Yong et al. [10] illustrated an Interleaved Soft Switching Boost
Converter (ISSBC) for a PV power generation system. The topology used to raise the efficiency of the DC-
DC converter. The converter of the PV Power Conditioning System (PVPCS) and it minimizes switching
losses by adopting methods of soft switching. Chien et al. [11] described a ZVS-PWM Interleaved Boost
Rectifier.
A novel grid-connected boost half- bridge PV micro inverter system and its control implementations
are presented in [12]. In order to achieve low cost, high efficiency, for easy control and higher reliability, a
post-half-bridge DC–DC converter using minimal devices is introduced to interface the low-voltage PV
module. An ultra large voltage conversion ratio converter is proposed by integrating a Switched Capacitor
circuit with a coupled inductor technology. A DC-to-DC converter is required to couple the electrolyzer to
the system DC bus [13]. A direct connection of DC bus to the electrolyzer is not suitable because it lacks the
ability to control the power flow between the renewable input source and the electrolyzer. Gopinath et al.
[14] and Arulmurugan et al. [15] illutrated the interleaved boost Converter with PI controller is used to
feedback the output signal to the input for the reduction of ripple current and improvement of efficiency.
Compared to a PID controller, PI controller has increased the efficiency and reduces the ripple current.
Ahmad et al. [16] developed the various pulses or duty cycle is applied in this Converter using PWM
Techniques.
Astrom [17] has developed the PI controller parameters are chosen incorrectly, the controlled
process input will not be stable. Tuning a control loop is the adjustment of its control parameters to the
optimum values for the desired control response. Ziegler-Nichols [18] has developed PI based on open loop
and closed loop test. It has to be noted that controllers tuned using this procedure are tuned to control, not
tracking. Thus, controllers with parameters tuned according to Ziegler-Nichols recommendation will perform
well in disturbance rejection, but it will perform poorly in tracking reference changes. Also, computing the PI
controller parameters by Ziegler-Nichols method does not provide optimum system response since they are
dependent on the exact mathematical model of the process. In this Converter PI Controller with Ziegler and
Nichols method are proposed. The improvement of efficiency is not sufficient, so that the optimization of real
coded genetic algorithm is proposed. And then the conventional method of PI controller, which means
Ziegler- Nichols, is not suitable for Interleaved Boost Converter. So, that the GA is ideally suited for
unconstrained optimization problems. But, most of the search and optimization problems are constrained in
nature. Hence it is necessary to transform it into an unconstrained problem. The binary coded GA has
Hamming cliff problems [19], which sometimes may cause difficulties in the case of coding continuous
variables. To overcome the above difficulty this chapter proposes a real-parameter genetic algorithm [20] in
which the optimization variables are represented as floating point numbers.
This artificial evolution process of real coded genetic algorithm is the foundation of the three main
different evolutionary based algorithms: Evolutions Strategies (ES) [21], Evolutionary Programming (EP)
[22], and Genetic Algorithms (GA) [23]. The proposed real coded genetic algorithm approach has been
applied for controller tuning (controlling parameters) in Interleaved Boost Converter. The simulation results
show that the proposed algorithm has resulted in minimizing ripple current and improving efficiency than the
conventional method and the traditional binary coded genetic algorithm. The proposed of real coded GA-
based approach is applied to tune the PI controller in the Interleaved Boost Converter. Also, the proposed
algorithm obtains less time for convergence compared to the binary coded genetic algorithm.

IJPEDS ISSN: 2088-8694 
Real Coded Genetic Algorithm Based Improvement of Efficiency in Interleaved Boost… (M.Arun Devi)
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2. INTERLEAVED BOOST CONVERTER
A Boost Converter is a power converter with an output DC voltage is higher than its input DC
voltage. It is a set of Switching Mode Power Supply (SMPS) with at least two semiconductor switches and
one energy storage element. This converter boost up the voltage at doubled than the other converter. The
filters made of capacitors or inductors are normally added to the output of the converter to reduce output
voltage ripple. Inductor and Input supply are together in series connection for adding the input and stored
energy in the inductor. It is not suitable for high power. The output of the boost converter having an amount
of ripple, due to ripple current, distortion is increased.
Interleaved Boost Converter has a number of boost converters connected in parallel, which have the
same frequency and phase shift, mainly used for renewable energy sources. In case of boost converter ripple
is present in the input current due to rise and fall of the inductor current. This problem can be eliminated by
using the Interleaved Boost Converter which is shown in Figure 1.
Figure 1. Circuit Diagram of Interleaved Boost Converter
In an Interleaved Boost Converter two boost converters operated in 180˚ out of phase. The input
current is the sum of two inductor currents and . Because the two inductor ripple currents are out of
phase, they cancel each other and reduce the input ripple current.
When switch is on and switch is off:
(1)

(2)
When switch is off and switch is on:
(3)
(4)
The two inductor currents will be out of phase and cancel out the ripple of each other if:

(5)
(6)
The above Equation (5) and (6) will be satisfied if and only if L1 = L2 = 0.713e-3
H.The duty cycle
of the system is 0.4. Hence the duty cycle is calculated the Equation (7) which is given below:
1

(7)
The capacitor value (C = 4.79 F) is calculated from the Equation (8) and the switching frequency of
the converter is set at the value of 50 KHz.

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∆
(8)
The calculation of basic parameter is used to design the circuit diagram of both Boost and
Interleaved Boost Converter.
100 (9)
The parameter of efficiency is calculated from the above Eq. (9) also it is represented by the ratio of
output and input power. Energy conversion efficiency (η) is the ratio between the useful output of an energy
conversion and the input.
2.1. Signal Generators
Pulse Width Modulation is the signal generators that conforms the width of the pulse based on
modulator signal information. It is a way of delivering energy through a succession of pulses rather than an
analog signal. In this paper, different types of PWM techniques are examined that are single pulse width
modulation, sinusoidal pulse width modulation and modified sinusoidal pulse width modulation. In this
PWM, the switch between supply and load is turned on/off at a very fast pace so as to control the average
value of voltage and current fed to the load. Thus the switch basically operates on the above mentioned
principle using PWM switching scheme. The term duty cycle expresses the ratio of on time to the entire
period of the time in percentage. It is generated by comparing DC reference signal with a saw tooth signal as
a carrier wave. PWM switching scheme thus offers an advantage of bearing low power loss in the switching
devices.
In single pulse-width modulation control, one pulse per half-cycle and the width of the pulse is
varied to control the output voltage. In multiple-pulse modulation, all pulses are the equal width. Vary the
pulse width according to the amplitude of a sine wave evaluated at the center of the different pulse. Figure 2
shows the modified sinusoidal pulse modulation signal.
Figure 2. Modified Sinusoidal Pulse Width Modulation
2.2. Design of Controllers
The Interleaved Boost Converter with PI controller is proposed in order to reduce the ripple current
and improved efficiency. A Proportional Integral controller (PI) is a generic control loop feedback
mechanism widely used in industrial control systems. A PI controller calculates an "error" value as the
difference between a measured variable and a desired set point. The controller efforts to minimize the error
by adjusting the process control inputs.
A block diagram of a simple closed-loop system consisting of a plant and a PI controller with unity
feedback is shown in Figure 3. The purpose of the system is to keep the process output (Y) close to the
desired output (Yd) in spite of disturbances. This is achieved by manipulating the process input (U) through
the controller. The perception of the closed loop system is defined by the Integral performance measures and
time response specifications.
Figure 3. Block diagram representation of a closed-loop system

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The PI controller makes the plant less sensitive to changes in the surrounding environment and
facilitates small changes in the plant. The transfer function of the PI controller is:
s
K
KsG i
pc )( (10)
Where is the proportional gain, is the integral gain and is the derivative gain. In the PID
controller, the proportional part deals with the error of the system at present; the integral part takes the past
into account that will happen in the future. The proportional gain of the controller reduces error responses to
disturbances. The integral of the error eliminates the steady state error, thus improves the stability of the
system. The controller has two parameters that can be adjusted like proportional gain (K ), and Integral gain
(K ). The control loop performs well if the parameters are chosen properly. It performs poorly otherwise.
Improper tuning may make the system become unstable. The procedure of finding the controller parameter is
called tuning.
3. REAL CODED GENETIC ALGORITHM
In real world industrial applications, optimization algorithms take part in an important role, as these
algorithms are used to automate several industrial processes. The manual search of a solution for optimizing
requires a great deal of insight and patience. Furthermore, manual optimizing often limits the scope of the
search process to what the human expert is trained to consider as a good solution. Conversely, optimization
algorithms automate the search and are not biased in scope regarding the solutions. The wide range of real-
world optimization problems and the importance of finding good approximate solutions have lead to a great
variety of optimizations. This chapter presents the details of GA, for solving the search and optimization
problem.
In a standard Genetic Algorithm, binary strings are applied to represent the decision variables of the
optimization problem in the genetic population, irrespective of the character of the decision variables. The
use of floating point numbers in the GA representation has a number of advantages over binary coding. The
effectiveness of the GA is increased as there is no need to convert the solution variables to the binary type,
less memory is essential, there is no loss in precision by discretization to binary or other values, and there is
liberty to use different genetic operators. With floating point representation, the evaluation process and
reproduction operator remain the same as that in binary-coded GA, but crossover operation is made variable
by variable. Also, the uniform mutation is used for the real parameter mutation operator. The details of the
crossover and mutation operator are presented in the following subsections.
3.1. Crossover Operation and Mutation
The crossover operator is mainly accountable for the global search property of the GA. Crossover
basically merges substructures of two parent chromosomes to create new structures, with the preferred
probability typically in the range of 0.6 – 1.0. The Blend crossover operator (BLX-α) (Devaraj 2005) is
utilized in this study. Figure 4 illustrates the BLX-α crossover operation for the one-dimensional case. In the
BLX-α cross over the off springy is sampled from the space [ ], 21 ee as follows:
y =  


 
otherwisesamplingrepeat
uyuifeere
:
: maxmin
121 (11)
Where, )( 1211 uuue   (12)
 1222 uuue   (13)
:r Uniform random number  1,0
It is to be noted that e1 and e2 will lie between minu and maxu , the variable’s lower and upper bound
respectively. In a number of test problems, it is examined that α = 0.5 provides good results. The feature of
this type of crossover operator is that the created point depends on the location of both parents. If both
parents are nearer to each other, the new child will also be close to the parents. On the other hand, if parents
are distant from each other, the search is similar to a random search.

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Figure 4. Schematic representation of BLX-α crossover
After crossover is completed, mutation takes place. The mutation operator is used to introduce new
genetic objects into the population. Mutation randomly adjusted a variable with a small probability. In this
work, the Uniform Mutation operator is applied, which is the variable set between the lower to upper limit.
4. GA IMPLEMENTATION
In the Real-coded Genetic Algorithm implementation, the following modifications are made to
improve the efficiency of formulating controller parameters. With a real-coded form of representation, the
selection scheme remains the same, but modifications are desired for crossover and mutation operators.
While solving an optimization problem using GA, each individual in the population signified a candidate
solution. Each individual in the population represents the parameters of the PI controller. For the Interleaved
Boost Converter the controller parameters are Kp and Ki. Where Ki=Kc/τi. In this work, the parameters of the
controller are represented as floating point numbers. A typical chromosome with floating point representation
is given below.
This type of representation has a number of advantages over binary representation. The efficiency of
the GA is improved as there is no need to convert the input variables to the binary type. The proposed
Genetic Algorithm searches for the optimal solution by maximizing or minimizing the function and therefore
an evaluation function which provides a measure of the quality of the problem solution is needed. The
Equation (14) indicates the objective function.
MSEff  (14)
During the GA run, GA searches for a solution with maximum fitness-function value. Hence, the
minimization objective function is given by (3.4) is transformed as:
Fitness= (15)
K is a constant. In the denominator a value of ‘1’ is added with ‘f ’ in order to avoid division by
zero.
5. RESULTS AND DISCUSSION
This section presents the simulation results and analysis of DC to DC interleaved boost converter. In
closed loop, the output is feedback to the gate poles of the switch (transistor) this using Pulse Width
modulator. The software for the proposed genetic algorithm is written in MATLAB and executed on a PC
with 2.4 MHZ and 256 MB RAM. The MATLAB Simulink diagram of the boost converter is shown in
Figure 5. The response of the Boost Converter with Single Pulse Width Modulation is shown in Figure 6.
From the figure, it is found that for a nominal input voltage is 24V the converter produces the output voltage
47V.

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Figure 5. MATLAB Simulink diagram of Boost Converter
Figure 6. Response of Boost Converter with Single PWM technique
The response of a boost converter with sinusoidal PWM technique is shown in Figure 7. From the
response, it is found that the output settling time is high. With nominal input voltage is 24V, the converter
produces the output voltage 48V. The output current is oscillating between 0 to 6A. This shows the high
ripple current in this method.
Figure 7. Response of Boost Converter with SPWM technique
The response of a boost converter with Modified Sinusoidal PWM technique is shown in Figure 8.
From the response, it is instigate that the output settling time is high. With nominal input voltage is 24V, the
controller produces the output voltage between 48V to 50V and the output current is oscillating between 0 to
2A. This shows the efficiency is high and the ripple current is less.
d c
pwm
Continuous
powergui
v+
-
Voltage 1
v+
-
Voltage
b1
To Workspace
Subtract
Scope4
Scope3
Scope2Scope1
Scope
Saturation
R
PID
PID Controller
g m
D S
Mosfet
L1
1
Gain
Diode
DC Voltage Source
i
+ -
Current Measurement1
i+ -
Current Measurement
48
Constant
Ca2
0 0.5 1 1.5 2 2.5 3 3.5
x 10
5
-20
0
20
40
60
80
100
120
Time(sec)
Voltage(V)
Boost Converter with Single Pulse Width Modulation
Input Voltage
Input Current
Output Current
Output Voltage
0 1 2 3 4 5 6 7 8 9 10
x 10
6
-10
0
10
20
30
40
50
60
70
80
90
Time(sec)
Voltage(V)
Boost Converter with Sinusoidal Pulse Width Modulation
Output Current
Input Voltage
Input Current
Output Voltage

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Figure 8. Response of Boost Converter with MSPWM technique
Interleaved Boost Converter reduces the ripple current due to the rise and fall of inductor current by
the parallel connection of two boost converters. Figure 9 shows the Simulink diagram of Interleaved Boost
Converters. The response of IBC with single pulse width modulation is shown in Figure 10. The response of
IBC with single pulse width modulation is shown in the Figure 11.
Figure 9. MATLAB Simulink diagram of IBC with PI controller
Figure 10. Response of IBC with Single PWM technique
0 1 2 3 4 5 6 7 8 9 10
x 10
6
-20
0
20
40
60
80
100
Time(sec)
Voltage(V)
Boost Converter with MSPWM
Output Current
Input Voltage
Input Current
Output Voltage
d c
pwm
Continuous
powergui
v+
-
Voltage 1
v+
-
Voltage
i1
To Workspace
Subtract
Scope6
Scope5
Scope4
Scope3
Scope2
Scope1
Saturation
R
PID
PID Controller
gm
DS
Mosfet1
gm
DS
Mosfet
L2
L1
1
Gain
Diode1Diode
DC
i
+ -
Current
48
Constant
Ca2
0 0.5 1 1.5 2 2.5 3 3.5
x 10
5
-20
0
20
40
60
80
100
120
Time(sec)
Voltage(V)
IBC with Single Pulse Width Modulation
Input Current
Input Voltage
Output Current
Output Voltage

537
Figure 11. Response of IBC with SPWM technique
The response of an interleaved boost converter with Modified Sinusoidal PWM technique is shown
in Figure 12. With nominal input voltage is 24V, the converter produces the output voltage 50V. The output
current is less compared with the boost converter. It shows the efficiency of the interleaved boost converter is
high due to the reduction of ripple current. Although the efficiency is high, the oscillation in output current
shows the ripple and real coded GA is proposed to tune the controller parameters.
Figure 12.Response of IBC with MSPWM technique
The closed loop proportional Integral controller cascaded with the process is tuned for the optimal
values of Kp and Ki using a binary coded GA algorithm and Real coded GA.
The optimal GA settings are
Number of generations : 10
Population size : 10
Crossover probability : 0.8
Mutation probability : 0.06
Both GA is applied to obtain the parameters of the PI controller for the Boost Converter and
Interleaved Boost Converter. The boundaries of the optimization variables are taken as 0<Kp<10; 0<Ki<5. The
optimal control gains obtained by the proposed algorithm along with the efficiency and ripple current of both
genetic algorithm with Boost and Interleaved Boost Converter are given in Table 1. The performance of the
system is found to be satisfactory with the control gains obtained using the proposed algorithm. From the
table, it is found that the proposed real coded GA with interleaved boost converter is having minimum ripple
current and maximum efficiency. Also, the computation time requirement is minimum in Proposed GA. That
all requirements of real coded GA produce better result compared than the binary coded genetic algorithm.
For an interleaved boost converter with single PWM Kp and Ki values are 7 and 1.9, this converter
produces the efficiency at 73% and ripple current at 0.002A for generation size of 10. The Kp and Ki values of
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10
6
-10
0
10
20
30
40
50
60
70
Time(sec)
Voltage(V)
IBC with Sinusoidal Pulse Width Modulation
Output Current
Input Voltage
Input Current
Output Voltage
0 1 2 3 4 5 6 7 8 9 10
x 10
6
-20
0
20
40
60
80
100
Time(sec)
Voltage(V)
IBC with MSPWM Technique
Output Current
Input Voltage
Input Current
Output Voltage

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an interleaved boost converter with Modified Sinusoidal PWM are 9 and 2, this shows the efficiency of 89%
and ripple current is 0.0009A. From the Table 1 comparing the entire techniques, interleaved boost converter
with Modified Sinusoidal Pulse Width Modulation technique shows the best result compared to boost
converter.
Table 1. Comparison of Performance Analysis
PWM Techniques GA Gn
Size
Pop
Size
Time  Ir
Boost Converter with
Single Pulse Width
Modulation
BGA 5 5 7 2 1.11e3 13.42 0.05
RGA 5 5 6 1 425.53 24.09 0.009
BGA 7 7 9 0.02 2.18e3 18.41 0.05
RGA 7 7 6 1.5 1.09e3 24.94 0.009
BGA 10 10 9 0.02 4.70e3 27.86 0.05
RGA 10 10 6 1.9 1.88e3 29.19 0.009
Sinusoidal Pulse Width
Modulation
BGA 5 5 8 0.3 8.25e3 18.22 0.03
RGA 5 5 8 4 212.27 25.13 0.005
BGA 7 7 9.6 0.5 2.35e4 40.83 0.03
RGA 7 7 8 4 249.93 45.44 0.005
BGA 10 10 9 0.5 3.14e4 60.69 0.03
RGA 10 10 8 4 759.12 65.60 0.005
Modified Sinusoidal
Pulse Width Modulation
BGA 5 5 9 2 1.12e3 11.77 0.04
RGA 5 5 9 2 154.40 10.76 0.004
BGA 7 7 9 0.5 1.15e3 25.27 0.04
RGA 7 7 9 1 195.32 26.52 0.004
BGA 10 10 6 1 1.12e3 72.36 0.04
RGA 10 10 6 1 503.20 71.11 0.004
Interleaved Boost
Converter with Single
BGA 5 5 9 0.5 1.15e3 25 0.05
RGA 5 5 9 1.9 199.15 15.14 0.001
BGA 7 7 9 0.7 1.29e3 49 0.05
RGA 7 7 8.2 1.9 381.56 30.57 0.001
BGA 10 10 9 1.2 5.47e3 67 0.05
RGA 10 10 7 1.9 840.46 71.71 0.001
Interleaved Boost
Converter with
Sinusoidal Pulse Width
Modulation
BGA 5 5 9 0.8 5.18e3 33 0.04
RGA 5 5 6 1.9 153.29 45.23 0.002
BGA 7 7 9 1.1 5.23e3 61 0.04
RGA 7 7 6.7 1.9 270.65 50.77 0.002
BGA 10 10 9 1.5 5.67e3 72 0.04
RGA 10 10 7 1.9 750.24 73.17 0.002
Interleaved Boost
Converter with
Modified Sinusoidal
BGA 5 5 9 1.2 6.12e3 34 0.03
RGA 5 5 8.9 0.8 120.25 35.50 0.0009
BGA 7 7 9 1.1 1.13e4 59 0.03
RGA 7 7 9 1 209.20 56.36 0.0009
BGA 10 10 9 1.5 1.25e4 84 0.03
RGA 10 10 9 2 683.32 89.17 0.0009
Figure 13. Real Coded GA for IBC with MSPWM
Figure 13 shows the convergence of proposed real coded genetic algorithm and it is observed that
the fitness value increases rapidly in the 2nd
generation on the genetic algorithm. During this stage, the GA
concentrates mainly on finding feasible solutions to the problem. Then the value increases slowly and settles
down near to the optimum value of 5th
generation with most of individuals in the population reaching that
point.
1 2 3 4 5 6 7 8 9 10
5.7
5.8
5.9
6
6.1
6.2
6.3
x 10
-3
Generations
Fitness
Best
Average

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6. CONCLUSION
Improving the efficiency of the photo voltaic power system, a grid connected boost converter is
used. However the improvement is not sufficient and also it produces some amount of ripple current and it
takes long time to settle the output voltage. To reduce the ripple current and improve the efficiency is
possible by an Interleaved Boost Converter with different PWM techniques. Both binary and real coded
genetic algorithms are proposed for reducing computation time, increasing efficiency and reducing the ripple
current. The simulation results were done by using Matlab Simulink for real and binary coded genetic
algorithm of an Interleaved Boost Converter. The results shows interleaved boost converter produces the
minimum ripple current with minimum computation time.
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seminar on renewable energy systems. IEEE Ind. Electron. Mag., 2010; 4: 38-51.
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 ISSN: 2088-8694
540
BIOGRAPHIES OF AUTHORS
Arun Devi M obtained her Bachelor Degree (B.E) in Electrical and Electronics
Engineering from Anna University, Chennai. She obtained her master degree (M.E) in
Applied Electronics from Anna University, Chennai .She works presently as Assistant
Professor in the department of Electrical and Electronics Engineering, P.S.R.Engineering
College, Sivakasi, India. Her research area includes Power Converter, Genetic Algorithm
and Control systems.
Valarmathi K obtained her Bachelor Degree (B.E) in Electronics and Communication
Engineering from Madurai Kamaraj University. She obtained her master degree (M.Tech)
in Process control and instrumentation from Bharadhidasan University and her doctoral
degree (Ph. D) from Anna University, Chennai. She works presently as professor in the
department of Electronics and Communication, P.S.R.Engineering college, Sivakasi, India.
She has published more than 50 international journals and conference publications. Her
research area includes Process control system, instrumentation and Soft computing.
Mahendran R obtained his Bachelor Degree (B.E) in Electrical & Electronics Engineering
from Anna University, Chennai. Also, he obtained his master degree (M.E) in Power
Electronics & Drives from Anna University, Chennai. He works presently as Assistant.
Professor in the department of Electrical and Electronics, P.S.R.Engineering College,
Sivakasi, India. He has published more than 10 conference publications. His research area
includes matrix converter and multilevel inverter.

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