42 design a photovolatic array with boost … a photovolati… · the maximum power point tracking...

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-

[email protected] 2

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

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &

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ISSN 0976 – 6545(Print)

ISSN 0976 – 6553(Online)

Volume 3, Issue 2, July – September (2012), pp. 444-456

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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


6553(Online) Volume 3, Issue 2, July- September (2012), © IAEME

446

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


September (2012), © IAEME

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



447

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]

.



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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



449

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’



450

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.



451

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



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



452

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



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



453

Fig: 11 Modeling Of PV Array Using FLC

Fig: 12 Input Voltage Waveforms.



454

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

[1] Hicham fakham , Di Lu, Brouno Francois.”Power Control Design Of A Battery Charger In A Hybrid Active PV Generator For Load Following Applications,” IEEE Transaction on Industrial Electronics., vol. 58, pp. 85-94, Jan 2011.

[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

Wind/Photovoltaic/ Fuel Cell Energy System,” ,” IEEE Transaction on Energy Conversion., vol. 23, NO.3, Sep 2008.

[9] Subiyanto, Azah Mohamed, Husasin Shareef,”Hopfield Neural Network Optimized Power Point Tracking In a PhotoVoltaic System,”International Journal of Photoenergy Vol. Article Id 798361, 13 pages,2012.

[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.

[11] Power Electronics; circuits, Devices and Applications.by M.Rashid [12] R.Valarmathi,S.Palaniswami, N.Devarajan,”Simulation and Analysis of Wind Energy

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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.



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



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.



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

42 design a photovolatic array with boost … a photovolati… · the maximum power point tracking...

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