42 design a photovolatic array with boost ... a...this paper proposes a method of maximum power...
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
444
DESIGN A PHOTOVOLATIC ARRAY WITH BOOST CONVERTER
USING FUZZY LOGIC CONTROLLER
T.Balamurugan1, Dr.S.Manoharan
2, P.Sheeba
3, M.Savithri
4
1 Research Scholar, Dept. Of EEE, Karpagam University, Coimbatore, India-
Professor,Dept. Of EIE, Karpagam College of Engineering, Coimbatore, India 3
Assistant Professor, Dept. Of EEE, Mount Zion College of Engg & Tech, Pudukkottai, India 4
Assistant Professor, Dept. Of EEE, Mount Zion College of Engg & Tech, Pudukkottai,India
ABSTRACT
This paper proposes a method of Maximum Power Point Tracking using Fuzzy Logic
Controller for Photo Voltaic Systems. The electric power supplied by a photovoltaic power
generation system depends on the solar radiation and temperature. Designing efficient PV
systems heavily emphasizes to track the maximum power operating point. Fuzzy Logic
Controllers provide attractive features such as fast response, accuracy and good performance.
The Maximum power point tracking control is based on Perturb and Observe method and
Fuzzy Logic Controller to control a switch of a Boost Converter. In order to increase the
efficiency of the energy conversion for a Photo Voltaic system using a resonant switching
technique. This switching pattern can reduce the switching losses, voltage and current stress
of the switching device. Mathematical modeling of the system and the results of simulations
in MATLAB/SIMULINK software are presented to investigate the correctness of the results.
Keywords: Photovoltaic (PV) systems, Fuzzy Logic Controller, Boost Converter, Rule base, Single-phase inverter, Triggering Pulses, Perturb and Observe (P&O).
I. INTRODUCTION As the cost of traditional fossil fuels continues to rise, the cost of electricity generated
by traditional means also increases. However as technology and manufacturing processes
improve the cost of alternative energy sources such as solar energy decreases [1]. Because of the
demand for electric energy and environmental issues such as pollution and these effects of
global warming, renewable energy sources are considered as an option for generating clean
energy. Technologies Photovoltaic (PV) energy has increased interest in electrical power
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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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applications. It is crucial to operate the PV energy conversion systems wear the maximum
power point to increase the efficiency of the PV system. In this paper, a fuzzy logic controller
(FLC) is developed to assign priority to the installed system loads such that all critical loads
receive a higher priority than the non-critical loads, and so when there exists a shortage of
available energy the critical loads are first met before attempting to power the non-critical
loads. This energy dispatch controller is also optimized to maintain a higher battery charge so
that the controller is better able to power critical loads during an extended period of
unfavorable weather conditions or low solar insolation. In this study, the simultaneous
optimization of the membership functions and rule base of a fuzzy logic controller is carried
out. The maximum power operating point varies with insulation level and temperature. Therefore, the tracking control of the maximum power point is a complicated problem. To overcome these problems, many tracking control strategies have been proposed such as incremental conductance, parasitic capacitance and constant voltage. The DC-DC converter for a PV system has to control the variation of the maximum power point of the solar cell output
[2]. In other words modulation of the DC - DC converter
controls Maximum Power Point Tracking. In this paper P&O - MPPT is investigated, P&O technique applies perturbation to the boost DC-DC controller by increasing the pulse width modulator (PWM) duty cycle, subsequently observes the effect on the PV output power
[2]. In Fig: 1 Represents the Typical
diagram of maximum power point tracking and fuzzy logic controller in a Photovoltaic systems. Recently FUZZY logic has been applied for tracking the maximum power point of PV systems in because it has the advantages of being robust, design simplicity and minimal requirement for accurate mathematical model. One of the most popular algorithms of MPPT is P&O (Perturb and Observe) technique; however, the convergence problem and oscillation are occurred at certain points during the tracking. To enhance the performance of the P&O algorithm Fuzzy logic converter and Boost converter to the MPPT control. The simulation study in this paper is done in MATLAB Simulink Software.
Fig: 1 Typical Diagram Of MPPT & Fuzzy Logic Controller in a PV System.
II. MODELLING OF PV SYSTEMS
2.1 EQUIVALENT CIRCUIT
PV is not a constant DC energy source but has variation of output power, which depends strongly on the current drawn by the load. Besides, PV characteristic also changes with temperature and irradiation variation. The model of solar cell can be categorized as P-N semiconductor junction, when exposed to light the DC current is generated. So an ideal Solar cell may be modeled by a current source in parallel with a diode that mathematically describes the V-I characteristic by [3].
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July
Fig: 2 Typical equivalent circuit of solar cell I=Ipv,cell – Id=Ipv,cell – I[exp(qv/α I=I0(e
Vd/VT -1) (2)
VPV=Vd-RsIpv (3) Where
Ipv is the cell current (Amps). ID is the diode saturation RS is the cell series resistance ( VD is the diode voltage. VPv is the cell voltage.
2.2 OUTPUT CHARACTERISTIC OF PHOTOVOLTAIC ARRAY
In this model, a PV cell is represented by a current source in parallel with a diode, and a
series resistance. A typical characteristic curve of PV model’s power and voltage curve is shown in Fig: 3
[3].
When the direct contact is between the source and the load, the output of the PV module maximum power and the operating point is noto add an adaptation device, MPPT controller with a Boost coand inverter, between the source and the load
Fig: 3 Typical Power
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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Fig: 2 Typical equivalent circuit of solar cell
αkT)-1] (1)
is the cell current (Amps). is the diode saturation current (Amps).
resistance (Ohms). is the diode voltage.
OUTPUT CHARACTERISTIC OF PHOTOVOLTAIC ARRAY
In this model, a PV cell is represented by a current source in parallel with a diode, and a A typical characteristic curve of PV model’s power and voltage curve is
When the direct contact is between the source and the load, the output of the PV module the operating point is no optimal. To avoid this problem, it is necessary
to add an adaptation device, MPPT controller with a Boost converter, Fuzzy logic controller and inverter, between the source and the load
[3].
l Power-Voltage Characteristic of Photovoltaic Array
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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In this model, a PV cell is represented by a current source in parallel with a diode, and a A typical characteristic curve of PV model’s power and voltage curve is
When the direct contact is between the source and the load, the output of the PV module is optimal. To avoid this problem, it is necessary
nverter, Fuzzy logic controller
f Photovoltaic Array
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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2.3 MAXIMUM POWER POINT TRACKING-P&O METHOD
For any PV system, the output power is increased by tracking the maximum power point
(MPP) of the system. To achieve this, a MPPT controller is required to track the optimum
power of the PV system and it is usually connected to a boost converter located between the
PV panel and load Several techniques for tracking MPP have been proposed. Two algorithms
are commonly used to track the MPPT - the P&O method and Inc Cond method. The P&O
method has been broadly used because it is easy to implement. Fig: 4 represent the control of
P&O algorithm using Fuzzy Logic Controller. The MPP tracker operates by incrementing or
decrementing the solar array voltage.
Fig: 4 Flow Chart P & O Method Using FLC
III. FUZZY LOGIC MAXIMUM POWER TRACKING CONTROLLERS The PV fuzzy logic controller consists of three main modules: the
fuzzification process, the inference engine, and the defuzzification process. The relationship
between these three main components is shown in Fig.:5, which shows a block diagram of the
traditional Fuzzy Logic Controller requires the expert knowledge of the process operation
for the FLC parameters setting and the controller can be only as good as the expertise
involved in the design. FLC with a fixed parameter is inadequate in applications when the
operation conditions change in wide range and the available expert knowledge is not
relatable. To make the controller less dependent on the expert knowledge, FLC could be
introducing [5]
.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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FLC requires the expert knowledge of the process operation for the FLC parameter
setting, and the controller can be only as good as the expertise involved in the design. FLC
with a fixed parameter is inadequate in applications when the operating conditions change in
a wide range and the available expert knowledge is not reliable. Fig: 5 is composed of two
parts: fuzzy knowledge base controller and a learning mechanism [10]
.
Fig: 5 Typical Diagram Of Fuzzy Logic Controller
3.1 FUZZIFICATION
The input membership functions take the inputs to the controller (after they have
been normalized by some value suitable for the membership functions) and produce a degree
of membership for each fuzzy set in the membership function. Membership function values
are assigned to the linguistic variables, using seven fuzzy subsets: NB(Negative Big), NM
(Negative Medium), NS (Negative Small), PM (Positive Medium) and PB (Positive Big). The
triangular shape of the membership function of these arrangement presumes that for any
particular input there is only are domain fuzzy subset. The input error (e) & change of error
( e) for fuzzy logic controller can be calculated from the maximum power point. Fuzzy
associate memory for the proposed system is given by Table-1.
Table -1: Fuzzy Associated Memory
E E
NB NM NS ZE PS PM PB
NB NB NB NB NM NM NS ZE
NM NB NB NM NM NS ZE PS
NS NB NM NM NS ZE PS PM
ZE NM NM NS ZE PS PM PM
PS NM NS ZE PS PM PM PB
PM NS ZE PS PM PM PB PB
PB ZE PS PM PM PB PB PB
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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3.2 INFERENCE ENGINE Once the degrees of membership for each fuzzy set have been determined for a
particular input, they are presented to the inference engine. The inference engine takes these
fuzzy set memberships and determines which rules should be evaluated. Inference engine
mainly consist of fuzzy rule base and implication sub blocks. The inputs are now fuzzy field
are fed to the inference engine and the rule base is then applied. The output fuzzy set are then
identified using fuzzy implication method. Here we are using MIN-MAX fuzzy implication
method [5]
. The resulting inference table and the rules surface is shown in Table-1 and Fig:
6(A),6(B)& 6(C)
Fig : 6(A): Typical Membership Function Plots For ‘e’
Fig: 6(B): Typical Membership Function Plots For ‘e’
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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Fig: 6(C): Typical Membership Function Plots For ‘U’
Fig: 7 Rule Surface Of FLC.
3.3 DEFUZZIFICATION
Once the degrees of membership of the outputs have been found via the inference
engine, the defuzzification process takes these values and translates them into an output
dispatch signal. Once fuzzification is over, output fuzzy range is located .since at this stage a
non-fuzzy value of control is available a defuzzification [6]
is used for defuzzification in the
proposed scheme. The membership function of the variables error, change in error and change in
reference signal for PWM generator are shown in Fig: 6a-6c respectively.
IV. CONVERTER AND ITS COMPONENTS
4.1 BOOST CONVERTER In many industrial applications, it is required to convert a fixed-voltage DC source into a
variable voltage DC source. A DC –DC converter converts directly from DC to DC and is simply known as a DC converter
[7]. A boost converter provides an output voltage greater than
the input voltage. The circuit arrangement of a boost converter is shown in Fig:7. Value of the duty cycle is determined by the fuzzy controlled which is equipped with a set
of well defined rules.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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Fig:8 Typical Boost Converter With FLC
4.2 INVERTER
The main function of an inverter is to convert the DC voltage obtained from the PV generator into an AC current
[7]. The lowest DC voltage will occurs with high ambient
temperature, and this effect predominates over the increased of optimal voltage caused by an increment of the irradiance at a constant cell temperature, so the maximum number of series connected models should be determined by this case. Inverter as higher rated voltage of DC link capacitors, inductors and switches are required.
V. SIMULATION AND RESULTS This paper simulated the adopted soft switching boost converter, fuzzy logic
controller and the PV module modeling using the MATLAB SIMULINK SOFTWARE.
5.1 SIMULATION PV MODULES
The equation from 1 to 3 for generating the current by PV array are represented by MATLAB/SIMULATION as shown in Fig: 9
Fig: 9 Modeling Of the Current Generated By PV Array in Matlab/Simulink Software
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July
5.2 SIMULATION OF BOOST CONVERTER The test signal when applied voltage waveform as shown in Fig: 10. The various parameters used for the simulation boost
Fig: 10 Boost Output
5.3 SIMULATION OF FUZZY LOGIC CONTROLLER The simulations of the MPPT show that the system is stable. The oscillations about the computed optimal operatinconverter. The designed PV module alogic controller module to tracking the maximum power point using switching techniques as shown in Fig: 11.
Switching frequency
Filter inductanc
Filter capacitance
Output resistance
Output inductance
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
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SIMULATION OF BOOST CONVERTER
The test signal when applied voltage waveform as shown in Fig: 10. The various simulation boost converter are as shown in Table-2.
Fig: 10 Boost Output from Converter
Table-2 Simulation Parameters
SIMULATION OF FUZZY LOGIC CONTROLLER
The simulations of the MPPT show that the system is stable. The oscillations about the computed optimal operating point are due to the switching action of the DC/DC
The designed PV module and DC-to-DC converter module can connected to fuzzy logic controller module to tracking the maximum power point using switching techniques as
Switching frequency 20KHZ
Filter inductance 0.3MH
Filter capacitance 250 µf
Output resistance 10 ohm
Output inductance 40mho
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The test signal when applied voltage waveform as shown in Fig: 10. The various
The simulations of the MPPT show that the system is stable. The oscillations about e switching action of the DC/DC
can connected to fuzzy logic controller module to tracking the maximum power point using switching techniques as
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Fig: 11 Modeling Of PV Array Using FLC
Fig: 12 Input Voltage Waveforms.
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Fig:13 Output Current &Voltage Waveforms
VI. CONCLUSION
This paper has presented the fuzzy logic controller for controlling maximum power point tracking of a photovoltaic system. The proposed algorithm in PV module and FLC was simulated. The simulation results show that this system is able to adapt the fuzzy parameters for fast response and good transient performances. In addition, the result of the simulation shows the increased efficiency of the system because of reducing the switching losses in the system. This system can provide high efficiency and low switching losses.
REFERENCES
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[2] Sang-hoom park ,Gil-Ro Cha, Yong-Chae Jung and Chung-yuen won .”Design and Application for PV Generationb System Using a Soft-Switching Boost Converter With SARC,” IEEE Transaction on Industrial Electronics., vol. 57, NO.2, Feb 2010.
[3] Basil M.Hamed, Mohammed S. El-Moghany.”Fuzzy Controller Design Using PhotoVolatic Maximum Power Point Tracking,” International Journal of Advanced Research inArtifical Intelligence, vol.1,no 3, 2012.
[4] Mohammed A.Elgendy, Bashar Zahawi, David J.Atkinson,”Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications,” IEEE Transaction on Sustainable Energy., vol. 3, NO.1, Jan 2012.
[5] G.Balasubramanian, S.Singaravelu,”Fuzzy Logic Based Controller For A Standlone Hybrid Generation System Using Wind and PhotoVoltaic Energy,” International Journal of advances in Engineering & Technology, May 2012.
[6] Chokri Ben Salah, Mohamed Ouali ,”Comparison Of Fuzzy Logic and Neural Network in Maximum Power Point Tracker For PV Systems,”Elsevier, Electric Power Systems Research 81, pp.no. 43-50, 2011.
[7] Jaime Alonso-Martinez,Santiago Arnaltes,”A Three-Phase Grid- Connected Inverter For PhotoVoltaic Aopplications Using Fuzzy MPPT,” International Journal of Advanced
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Research inArtifical Intelligence, vol.1,no 4, 2011 [8] Caisheng Wang,M.Hashem Nehrir,”Power Management of a Stand- Alone
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[10] Nopporn Patcharaprakitia,Suttichai Premrudeepreechacharnb,Yosanai Sriuthaisiriwong.” Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system”, 2005 Published by Elsevier Ltd.
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Balamurugan T was born in Chennai on NOV 16, 1985. He received the
B.E. degree in Electrical and Electronics Engineering from the Anna
University, Chennai in 2007, M.Tech degree in Power Electronics and
Drives in PRIST University, Tanjore in 2011, MBA degree in Human
Resource Management in Annamalai University, Chidambaram in 2009.
Currently Pursuing Ph.D degree in Renewable Energy Sources in
Karpagam University, Coimbatore. He is Assistant Professor at the
department of Electrical and Electronics Engineering of Mount Zion
college of Engineering and Technology and he is also a life time member
of ISTE. He has a long experience in the design of control systems for
power electronic converters and more exactly multi-phase and multilevel
converters. He is currently working on advanced renewable energy based
generators and energy management systems for future smart grids.
Dr.S.Manoharan took his B.E degree in Electrical and Electronics
Engineering from Government College of Technology, Coimbatore in
1997, M.E degree in Electrical Machines from PSG College of
Technology, Coimbatore in 2004 and Ph.D. in the area of Electrical
Machines and drives from Anna University Chennai in July 2010. He has
over 18 years of teaching experience. He is currently working as Professor
and Head, Department of Electronics and Instrumentation Engineering in
Karpagam College of Engineering, Coimbatore, Tamilnadu. He has
published research papers in both National and international journals of
repute and presented papers in National and International Conferences. He
has published more than half a dozen-text books on Electrical and
Electronics related fields. He is a life member of ISTE, SSI and member of
IE (India) and IEEE. Presently under his guidance, there are 14 students
are doing their doctoral work in Anna university Chennai and Karpagam
university, Coimbatore.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 3, Issue 2, July
Sheeba P
B.E. degree in E
University, Chennai in 2006,
in Anna University, Trichy in 2009, MBA degree in Human
Management in Alagappa University, Karaikudi
Professor at the
Mount Zion college of Engineering and Technology
experience in
converters. Sh
Savithri M
B.E. degree in E
University, Chennai in 2010
in Anna University,
department of Electrical
college of Engineering and Technology.
Hybrid energy based systems
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME
456
was born in Pudukkottai on FEB 11, 1985. She received the
degree in Electrical and Electronics Engineering from the Anna
University, Chennai in 2006, M.E degree in Power Electronics and Drives
in Anna University, Trichy in 2009, MBA degree in Human
Management in Alagappa University, Karaikudi in 2012. She
ofessor at the department of Electrical and Electronics Engineering of
Mount Zion college of Engineering and Technology. She has a long
experience in the design of digital electronics for power
. She is currently working on renewable energy based systems
Savithri M was born in Karaikudi on SEP 26, 1988. She received the
degree in Electrical and Electronics Engineering from the
University, Chennai in 2010, M.E degree in Power Electronics and Drives
in Anna University, Chennai in 2012. She is Assistant Professor at the
department of Electrical and Electronics Engineering of
college of Engineering and Technology. She is currently working on
energy based systems.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
September (2012), © IAEME
She received the
gineering from the Anna
degree in Power Electronics and Drives
in Anna University, Trichy in 2009, MBA degree in Human Resource
in 2012. She is Assistant
Engineering of
e has a long
digital electronics for power electronic
nergy based systems.
was born in Karaikudi on SEP 26, 1988. She received the
gineering from the Anna
, M.E degree in Power Electronics and Drives
Professor at the
Engineering of Mount Zion
e is currently working on