CHAPTER-2

PHOTOVOLTAIC SYSTEMS

16

2. Photo Voltaic Systems

2.1 Introduction

Solar photo voltaic is currently considered to be an important energy source

since it is renewable and produces clean energy. A great deal of research has been

conducted in this field over the last few decades. The V-I characteristics of a PV panel

is a non- linear power source that needs accurate identification of optimal operating

point. The panel output power varies with temperature and isolation. It is desired to

operate Solar Photo Voltaic (SPV) panel at its maximum power output for economic

reasons. To extract maximum power from the panel, its internal resistance should be

equal to the load resistance.

Chopper circuit is interposed between the SPV and the load resistance to

adjust the load resistance seen by SPV equal to its internal resistance by varying the

duty cycle of the chopper. A few among several maximum power point tracking

methods are available in practice. A major challenge in using an SPV source

containing a number of cells in series is to deal with its non-linear internal resistance.

Cells under shade absorb a large amount of electric power generated by cells

receiving high insolation and convert it into heat. This heat may damage the low

illuminated cells under certain conditions. To relieve the stress on shaded cells,

bypass diodes are added across the modules. In such a case multiple peaks in power-

voltage characteristics are observed under non uniform illumination [13].The

Classical Maximum Power Point Tracking (MPPT) methods are not effective due to

their inability to discriminate between local and global maxima. However, it is very

important to understand the characteristics of the SPV under partially shaded

conditions to use PV installations effectively under all conditions [13].

. 2.2 Equivalent Circuit of Photovoltaic Cell

Figure-2.1 shows a simple model of a PV cell. In this model, RS is the series

resistance and RP is a parallel resistance connected to the active portion of a cell or

module consisting of a series of equivalent cells [13]. Using Equation-2.1 and I-V

measurements, the value of RS can be calculated. Figure-2.2 shows that RP vary with

the reciprocal of irradiance.

17

Figure-2.1: Simple Photovoltaic model.

Simple PV output current is

I= Iph- Io . eq V+I.RS

n.K.T - 1 - V+I.RS

RS ……………(2.1)

Figure-.2.2: Resistance v/s reciprocal of irradiance curve.

RP is parallel leakage resistance and is typically large, > 100kΩ in most of the

modern PV cells. This component can be neglected in many applications except for

low light conditions.

Figure-2.3: I-V curve of the photovoltaic cell.

-

+ I RS

RP D1 IPH V

18

Practically arrays are composed of several connected photovoltaic cells and

the observation of the characteristics of the terminals of the photovoltaic array

requires the inclusion of additional parameters to the basic equation.

Figure-2.4: I-V curve of a practical photovoltaic device

I= Ipv-Io exp V+ I. RS

Vt-1 - V+ I. RS

Rp……(2.2)

Where Ipv and Io are the photovoltaic and saturation currents of the array and

Vt = NskT/q is the thermal voltage of the array with Ns cells connected in series.

Cells connected in parallel increase the current and Cells connected in series provide

greater output voltages [15]. If the array is composed of Np parallel connections of

cells the photovoltaic and saturation currents may be expressed as: Ipv=Ipv, cell l Np,

I0=I0, cell Np. In equation (2.2) Rs is the equivalent series resistance of the array and

Rp is the equivalent parallel resistance.

Current through the diode is represented by Equation

Iph= I0 . eq. V+ I.Rs

n.K.T - 1 …………(2.3)

Where, Io= Diode saturation current

q = Electron charge (1.6x10-19 C)

K = Boltzmann constant (1.38x10-23J/K)

n = Ideality factor (from 1 to 2)

T = Temperature (ºK)

In = . .

……… (2.4)

V

I

Current Source

(0, Isc)

(Voc, 0)

(Vmp, Imp) MPP

Voltage Source

facto

shee

depe

Max

these

syste

pow

2.2.

rela

pow

the

circ

volt

resp

The abov

or as the irr

ets are:

• V

• I

• V

• I

The pow

endent on th

ximum Powe

e systems. I

em can deliv

er from the P

.1 Characte

Gener

ation betwee

wer. Accordi

PV system a

cuit conditio

tage, curren

pectively [18

ve equation

radiance cha

VOC = Open

ISC = Short c

VMP = Maxim

IMP = Maxim

wer delivere

he irradianc

er Point Tra

In the appli

ver. In this c

PV system.

eristics of PV

rally, the el

n the cell vo

ingly, severa

are identifie

ns VOC, the

nt and pow

8]. The Figu

Figure

(2.4) is a ln

anges [17].

circuit outp

circuit outpu

mum power

mum power o

ed by a PV

ce, temperat

acking (MPP

ications, the

case, a powe

V cells:

lectrical cha

oltage , curre

al electric qu

d. These ele

cell current

wer at the m

ure-2.5, 2.6

e - 2.5: I-V c

n (irradiance)

The param

put voltage

ut current

output volta

output curren

system of o

ture, and th

PT) is used

load can d

er conversio

aracteristics

ent, and a rel

uantities that

ectric quantit

under short

maximum p

shows I-V,

characteristic

) is likely a

eters genera

age

nts

one or more

he current d

to obtain th

demand mor

on system is

of PV cel

lation betwe

t are import

ties include t

t circuit con

power point

P-V charac

cs of a singl

change in t

ally given in

e photovolta

drawn from

he maximum

re power th

used to ma

ll are displ

een the cell v

tant to the op

the voltage u

nditions ISC an

tVMPP, IMPP

cteristics of

e PV cell

19

the ideality

n PV data

aic cells is

the cells.

m power of

an the PV

aximize the

ayed as a

voltage and

peration of

under open

nd the cell

and PMPP

a PV cell.

2.3 O

out:

pow

outp

repre

appr

Volt

as th

repre

F

Open Circu

Two imp

the Open Ci

er generated

put current of

esented by e

roximately e

The max

tage characte

he MPP and

esented.

Figure-2.

Figure - 2.6:

it Voltage, S

portant point

ircuit Voltag

d is zero. VO

f the cell is z

equation (2.5

qual to the li

Voc ≈ A

ISC = IL

ximum powe

eristics wher

is unique, a

7: Important

: P-V charac

Short Circu

ts of the Cu

ge VOC and th

OC can be app

zero, i.e. I=0

5). The short

ight generate

AkTq

lnIL

Io+

L

er is generate

re the produ

as can be see

t points on th

cteristics of a

uit Current

urrent-Voltag

he Short Circ

proximated

0 and the shu

t circuit curre

ed current IL

+ 1

ed by the So

uct VI is max

en in Figure-

he character

a single PV c

and Maxim

ge character

cuit Current

from the eq

unt resistanc

ent ISC is the

L as shown in

olar Cell at a

ximized [12

-2.7, where t

ristic curves

cell

mum Power

ristics must b

ISC. At both

quation-(2.5)

ce RSH is negl

current at V

n equation (2

…

…………

a point of th

]. This poin

the previous

of a solar pa

20

Point

be pointed

points, the

) when the

lected. It is

V = 0 and is

2.6).

………(2.5)

…… (2.6)

he Current-

nt is known

s points are

anel.

21

2.4 Temperature and Irradiance Effects

Two significant factors that have to be taken into consideration are the

irradiation and the temperature. These two factors powerfully affect the characteristics

of solar cells and modules. As a result, the MPP varies during the day and that is the

main reason why the MPP must continually be tracked and make sure that the

maximum available power is obtained from the panel.

The effect of the irradiance on the Voltage-Current (V-I) and Voltage-Power

(V-P) characteristics is depicted in Figure-2.8, where the curves are shown in per unit,

i.e. the voltage and current are normalized using the VOC and the ISC respectively, in

order to make obvious better the effects of the irradiance on the V-I and V-P curves.

As was formerly mentioned, the photo-generated current is directly proportional to the

irradiance level, so an increment in the irradiation leads to a higher photo-generated

current [20]. Moreover, the short circuit current is directly proportional to the photo

generated current; therefore it is directly proportional to the irradiance.

(a)

(b)

Figure-2.8(a) V-I and (b) V-P curves at constant temperature (25°C) and three different insolation values.

22

When the operating point is not the short circuit, in which no power is

generated, the photo generated current is also the main factor in the PV current, as is

spoken by Equations (2.2) and (2.3). For this reason, the voltage-current characteristic

varies with the irradiation. In difference, the effect in the open circuit voltage is

relatively small, as the dependence of the light generated current is logarithmic, as is

shown in Equation (2.4).

Figure-2.8 shows that the change in the current is greater than in the voltage.

In practice, the voltage dependency on the irradiation is often neglected [22]. As the

effect on both the current and voltage is positive, i.e. both increase when the

irradiation rises, the effect on the power is also positive: the more irradiation, the

more power is generated. The temperature, on the other hand, affects mostly the

voltage. The open circuit voltage is linearly reliant on the temperature, as shown in

the following equation:

Voc T = VocSTC+

Kv,%

100 T-273.15 …………….(2.7)

According to Equation (2.7), the result of the temperature on VOC is negative,

because Kv is negative, i.e. when the temperature raises, the voltage decreases. The

current increases with the temperature but very little and it does not compensate the

decrease in the voltage caused by a given temperature rise. That is why the power also

decreases. PV panel manufacturers provide in their data sheets the temperature

coefficients, which are the parameters that identify how the open circuit voltage, the

short circuit current and the maximum power vary when the temperature changes. As

the effect of the temperature on the current is really small, it is usually deserted [22].

Figure-2.9 shows how the voltage-current and the voltage-power characteristics

change with temperature. The curves are again in per unit, as in the previous case.

As previously mentioned, the temperature and the irradiation depend on the

atmospheric conditions, which are not constant during the year and not even during a

single day; they can differ quickly due to fast varying conditions such as clouds. This

causes the MPP to move continually, depending on the irradiation and temperature

circumstances. If the operating point is not close to the MPP, great power losses

occur. Hence, it is essential to track the MPP in any conditions to assure that the

23

maximum existing power is obtained from the PV panel. In a modern solar power

converter, this task is entrusted to the MPPT algorithms.

(a)

(b)

Figure-2.9:(a) V-I and(b) V-P curves at constant irradiation (1 kW/m2) and three different temperatures.

2.5 Photovoltaic System Configuration

PV modules produce DC current and voltage. However, to provide the

electricity to the grid, AC current and voltage are required. Inverters are the apparatus

used to convert DC to AC. In addition, they can be in charge of keeping the operating

point of the PV array at the MPP. This is frequently done with computational MPP

tracking algorithms.

There are different inverter configurations depending on how the PV modules

are linked to the inverter [23]. The main types are described in this chapter. If the

modules are not matching or do not work under the same situation, the MPP is

different in each panel and the ensuing voltage-power characteristic has multiple

maxima, which constitutes a problem, because most MPPT algorithms meet with a

local maximum depending on the starting point. If the operating point is not the MPP,

24

not all the feasible power is being fed to the grid. For these reasons, each case has to

be cautiously studied to optimize the plant and obtain the maximum performance. The

different configurations are described before long in this chapter because they are not

the focus of this thesis. More in sequence about all the following topologies can be

found in [23] and [24].

2.5.1 Central Inverter

It is the simple pattern, PV strings, consisting of a series connected PV panels,

is connected in parallel to obtain the desired output power. The resulting PV array is

connected to a single inverter, as shown in Figure-2.10 In this arrangement, all PV

strings work at the same voltage, which may not be the MPP voltage for all of them.

The problem of this configuration is the likely mismatches between the different PV

modules. If they are getting different irradiation (shading or other problems), the true

MPP is difficult to find and as a result, there are power losses and the PV modules are

underutilized [26].

Figure-2.10: Central configuration

2.5.2 String Inverter

In this configuration, each string of PV panels associate with the series which

is connected to a dissimilar inverter, as can be seen in Figure-2.11. This can get better

25

the MPP tracking in case of mismatches or shading because each string can operate at

a different MPP, if essential, while in the central inverter there is only one operating

point which may not be the MPP for each string, thus important to power losses [26].

On the other hand, the number of components of the system increases as well as the

fitting cost, as an inverter is used for each string.

Figure-2.11. String configuration

2.5.3 Multi-string Inverter

In this case, each string is linked to a dissimilar DC-DC converter, which is in

charge of the MPP tracking of the string and the converters are connected to a single

inverter, as depicted in Figure-2.12. The advantages related to MPP tracking are the

same as in the string configuration; each string can have a different MPP.

Figure-2.12: Multi-string configuration.

26

2.5.4 Module Integrated Inverter

In this configuration, as shown in Figure-2.13, each PV module is connected

to a different inverter and as a result the maximum power is obtained from each panel

as the individual MPP is tracked by each inverter [26]. This configuration can be used

when the differences in the operating point of the different modules are large.

However, it is more expensive because each panel has its own inverter.

Figure-2.13: Individual inverter.

2.6 Importance of MPPT

The PV system usually consists of a PV array that converts Solar energy to

Electrical energy, a DC/DC converter converts a low DC voltages produced by the

PV array to a high DC voltage, an inverter that converts the high DC voltage to a

single or three-phase AC voltage and a digital controller that controls the system and

implement the MPPT algorithm by controlling the current and voltage of the PV

array. The MPPT algorithm is vital in increasing the efficiency of the system [27]. In

PV system conversion of solar energy into electricity is costly in general a vital way

of generating electricity is only by producing maximum possible output for all

weather conditions.

The PV array has a highly non-linear current-voltage characteristic varying

with the irradiance and temperature that substantially affects the array power output.

The Maximum Power Point Tracking (MPPT) control of the PV system is therefore

critical for the success of a PV system. MPPT stands for Maximum Power Point

Tracking and it relates to the Solar cell itself. Each solar cell has a point at which the

27

current (I) and voltage (V) output from the cell result in the maximum power output

of the cell. A typical solar panel converts only 30-40% of the incident radiation into

electrical energy. Maximum power point tracking technique is used to improve the

efficiency of the solar panel [28].

According to theory of maximum power transfer theorem, the output power of

the circuit is maximum, when the Thevenin’s impedance of the circuit (source

impedance) matches with the load impedance. Hence our problem of tracking the

maximum power point reduces to an impedance matching problem. In the source side,

a boost converter is connected to a solar panel in order to enhance the output voltage

so that it can be used for different applications like motor load. By changing the duty

cycle of the boost converter, appropriately the source impedance can be matched with

the load impedance.

2.7 Survey of Literature

1. R. Abu Tariq in their work entitled Simulink based modelling, simulation and

presentation Evaluation of an MPPT for maximum power generation of resistive load.

A proposes work a moped which works in combination with a power electronic

converter to shift the operating point to obtain maximum power from a PV Panel with

load and changeable insolation conditions.

2. Track Salm in their work entitled Matlab/Simulink Based Modelling of Solar

Photovoltaic Cell paper focuses on a Matlab/Simulink model of a photovoltaic cell.

This model is based on mathematical equations of the solar module

3. Hairul Nissah Zainudin their work entitled Comparison Study of Maximum Power

Point Tracker Techniques for PV Systems. This Paper presents particularly a virtual

study between two most accepted algorithm technique which are an incremental

conductance algorithm and perturb and observe algorithm. The Simulation is

considered different solar irradiance and temperature variations.

4. Basim Alsayid and Jafar Jallad in their work entitled Modelling and Simulation of

Photovoltaic Cells/Modules/Arrays, presents in detail model makes use of basic

circuit equations of PV solar cell based on its performance of diode, taking the effect

28

of sunlight irradiance and cell temperature into consideration on the output current I-

V characteristic and output power P-V characteristic.

5. M. Abdulkadir, A. S. Samosir and A. H. M. Yatim in their work entitled Modeling

and Simulation based approach of photovoltaic system in Simulink model. The

proposed model is found to be improved and correct for any irradiance and

temperature variations.

6. T. Kerekes, R. Teodorescu , M. Liserre, R. Mastromauro , A. Dell’Aquila, in their

work entitled MPPT algorithm for Voltage Controlled PV Inverters, presents a novel

idea for an MPPT that can be used in case of a voltage controlled grid connected PV

inverters, and single-phase systems, the 100Hz ripple in the AC power is also present

on the DC side.

7. R. Ramaprabha, B. L. Mathur, in their work entitled Development of an Improved

Model of SPV Cell for Partially Shaded Solar Photovoltaic Arrays, present, first and

second quadrant model of SPV cell. Equivalent shunt resistance (Rsh) in the model is

variables with environmental parameters. The effect of change in Rsh hitherto

neglected by many researchers has been properly modelled and included in the

corresponding circuit.

8. Smita Ganesh Pachpande , Prof. Pankaj H. Zope in their work entitled Studying the

effect of shading on Solar Panel using MATLAB , The presentation of Photovoltaic

array is affected by solar isolation, shading, temperature and this is result in

disarticulation of the Maximum Power Point (MPP).

9. Chia Seet Chin, Prabhakaran Neelakantan, Soo Siang Yang, Bih Lii Chua, Kenneth

Tze Kin Teo in their work entitled Effect of Partially Shaded Conditions on

Photovoltaic Array’s Maximum Power Point Tracking. This presents in details

Maximum power point tracking algorithm are extensively implemented in

photovoltaic system to capitalize on the PV array output power. Under uniform solar

irradiance, PV array characteristic is non-linear and consisting only one MPP along

the efficient operating voltage.

10. Ratna Ika Putri and M. Rifa’I in their work titled Maximum Power Point Tracking

Control for Photovoltaic System Using Neural Fuzzy. This Paper presents in details

29

neural fuzzy definitions MPP point and the MPPT calculation is done by adjusting the

duty cycle of converter so that the PV array voltage remains at the MPP operating

point. In particular, the simulation of neural fuzzy is discussed.

11. B. Amrouche1, M. Belhamel1 and A. Guessoum in their work entitled Artificial

intelligence based P&O MPPT method for photovoltaic systems, presents Artificial

Intelligence (AI) concepts which are used to improve P&O algorithm. The

perturbation step is continuously approximated by using artificial neural network

(ANN). By the simulation, the potency of the proposed control algorithm is proved.

12. Antoneta Iuliana Bratcu, Seddik Bacha, Damien Picault, and Bertrand Raison, in

their paper titled Cascaded DC–DC Converter Photovoltaic Systems, investigate the

issues of ensuring global power optimization for cascaded dc–dc converter

architectures of photovoltaic (PV) generators irrespective of the irradiance conditions.

13. Souvik Dasgupta, Sanjib Kumar Sahoo, and Sanjib Kumar Panda in their paper

entitled Single-Phase Inverter Control Techniques for Interfacing Renewable Energy

Sources With Micro grid, presents a novel current control technique is proposed to

control both active and reactive power flow from a renewable energy source feeding a

micro grid system throughout a single-phase parallel-connected inverter.

14. M.S. Aït Cheikh, C. Larbes, G.F. Tchoketch Kebir and A. Zerguerras in their

work entitled Maximum power point tracking using a fuzzy logic control scheme.

The propose of this paper an intelligent control method for the maximum power point

tracking (MPPT) of a photovoltaic system under varying temperature and insolation

conditions. This method uses a fuzzy logic controller applied to a DC-DC converter

device.

15. A. Saadi and A. Moussi in their work entitled Neural Network Use in the MPPT

of Photovoltaic Pumping System, propose the embodiment of the recent outcomes in

the approach of the rudimentary theory of the neural network and its application in the

field of the photovoltaic system of pumping water with centrifugal pump.

16. Anssi M¨aki, Seppo Valkealahti in their work entitled Power Losses in Long

String and Parallel-Connected Short Strings of Series-Connected Silicon-Based

Photovoltaic Modules Due to Partial Shading Conditions, explain the long series

30

connection of modules and parallel connections of strings via a single inverter to the

electrical grid should be minimized to avoid losses in case of partial shading

conditions. Under partial shading conditions, short strings in check separately have

the lowest power losses.

17. Moacyr Aureliano Gomes de Brito, Luigi Galotto, Jr. Leonardo Poltronieri

Sampaio, Guilherme de Azevedo e Melo, and Carlos Alberto Canesin, in their work

entitled Evaluation of the Main MPPT Techniques for Photovoltaic Applications,

present, the evaluations among the most usual maximum power point tracking

(MPPT) techniques, doing significant comparisons with approbation to the amount of

energy extracted from the photovoltaic (PV) panel [tracking factor (TF)] in relation to

the available power, PV voltage ripple, dynamic response, and use of sensors.

18. Luiz Fernando Lavado Villa, Tien-Phu Ho, Jean-Christophe Crebier, and Bertrand

Raison in their work entitled A Power Electronics Equalizer Application for Partially

Shaded Photovoltaic Modules Propose topology eliminates the multiple maximum

power point peaks common to partial shading in PV modules. The topology does so,

by equalizing the overall energy of the PV module through the use of only one

inductive storage element. A theoretical study is carried out to express the physical

equations of the topology.

19. Ali Bidram, Ali Davoudi, Robert S. Balog, in their work entitled Control and

Circuit Techniques to Mitigate Partial Shading Effects in Photovoltaic Arrays, present

A Partial shading in photovoltaic (PV) arrays renders predictable maximum power

point tracking (MPPT) techniques ineffective. The condensed achievement of shaded

PV arrays is an important obstruction in the rapid growth of the solar power systems.

Thus, addressing the output power mismatch and partial shading effects is of

prevailing values.

20. M. Z. Shams El-Dein Mehrdad Kazerani, M. M. A. Salama, in their work entitled

Optimal Photovoltaic Array Reconfiguration to Reduce Partial Shading Losses, r

formulate the reconfiguration problem as a mixed integer quadratic programming

problem and finds the optimal solution using a branch and bound algorithm. The

proposed formulation can be used for an equal or non equal number of modules per

31

row. The improvement resulting from the reconfiguration with respect to the existing

photovoltaic interconnections is demonstrated by extensive simulation results.

21. Kun Ding, XinGao Bian, HaiHao Liu, and Tao Peng in their work entitled A

MATLAB-Simulink-Based PV Module Model and Its Application under Conditions

of non-uniform Irradiance, present a Matlab-Simulink-based PV module model which

includes a controlled current source and an S-Function builder. The modelling scheme

in S-Function builder is deduced by some predigested functions under the situation of

non-uniform irradiance.

22. Evagelia V. Paraskevadaki and Stavros A. Papathanassiou, in their work entitled

Evaluation of MPP Voltage and Power of mc-Si PV Modules in Partial Shading

Conditions, discuses the effect of partial shading on multi crystalline silicon (MC-Si)

PV modules is investigated. A PV module simulation model implemented in P-Spice

is first employed to quantify the effect of partial shading on the I–V curve and the

maximum power point (MPP) voltage and power. The position of the sun at any time

and location is predicted by the mathematical procedure of Julian dating; then, the

solar irradiation was obtained at each site under a clear sky.

23. Dzung D. Nguyen, Brad Lehman Sagar Kamarthi, in their work entitled

Performance Evaluation of Solar Photovoltaic Arrays Including Shadow Effects

Using Neural Network propose a neural network based approach to estimating the

maximum possible output power of a solar photovoltaic array under the non-uniform

shadow conditions at a given geographic location.

24. Kashif Ishaque, Zainal Salam, in their work entitled An improved model method

to determine the model parameters of photovoltaic (PV) modules using differential

evolution (DE), propose an improved model approach using differential evolution

(DE) method. Unlike other PV modelling techniques, this approach enables the

computation of model parameters at any irradiance and temperature point using only

the information provided by the manufacturer’s data sheet.

25. Basim Alsayid, in his work entitled Modelling And Simulink Of Two Diode

model of PV cells, a brief introduction to the behaviour and functioning of a PV

device and write the basic equation of the two-diode model, without the intention of

32

providing an in depth analysis of the photovoltaic phenomena and the semiconductor

physics. The introduction of PV devices is followed by the modelling and simulation

of PV cell/PV module/PV array.

26. Joseph A Jervase, Hadj Bourdoucen and Ali Al-Lawati, in their work entitled

Solar cell parameter extraction using genetic algorithms, present a technique based on

genetic algorithms is proposed for improving the accuracy of solar cell parameters

extracted using conventional techniques.

27. Kashif Ishaque, Zainal Salam, and Hamed Taheri in their work entitled Accurate

Matlab Simulink PV System Simulator Based on a Two-Diode Model. This model

gives a better accuracy at low irradiance levels which allows for a more accurate

prediction of PV system performance.

28. Ying-Pin Chang, Der-An Wang in their work entitled Optimization of Tilt Angle

for Photovoltaic Modules Based on the Neural-Genetic Algorithm, present a method

which combined an artificial neural network and a genetic algorithm (ANNGA) in

determining the tilt angle for photovoltaic (PV) modules. First, a Taguchi experiment

is used to perform an efficient experimental design and analyse the robustness of the

tilt angles for fixed south-facing PV modules. Following, the results of the Taguchi

experiment are used as the learning data for an artificial neural network (ANN) model

that could predict the tilt angles at discrete levels. Finally, a genetic algorithm method

was applied to obtain a robust tilt angle setting of the tilt angle of PV modules with

continuous variables. The objective is to maximize the electrical energy of the

modules. In this study, three Taiwanese areas are selected for analysis. To confirm the

computer simulation results, experimental system is conducted for determining the

optimum tilt angle of the modules.

29. Ali Nasr Allah Ali, Mohamed H. Saied, M. Z. Mostafa, T. M. Abdel in their work

entitled A Survey of Maximum PPT techniques of PV Systems, introduce a survey of

different maximum peak power tracking (MPPT) techniques use in the

implementation of photovoltaic power systems. It discusses different 30 techniques

use in tracking maximum power in photovoltaic arrays. This paper can be considered

a complete, updating, and a declaration of the good efforts made in that discusion 19

MPPT techniques in PV systems, while summarizes additional 11 MPPT methods.

33

30. B.C. Kok, H.H. Goh, H.G. Chua in their work entitled Optimal Power Tracker for

Stand-Alone Photovoltaic system using Artificial Neural Network (ANN) and Particle

Swarm Optimization (PSO), present, intelligent techniques and approaches have been

introduced into photovoltaic (PV) system for the utilization of free harvest renewable

energy. Generally, the output power generation of the PV system relies on the

intermittent solar insolation, cell temperature, the efficiency of the PV panel and its

output voltage level. Consequently, it is essential to track the generated power of the

PV system and utilize the collected solar energy optimally.

Artificial Neural Network (ANN) is initially used to forecast the solar

insolation level and followed by the Particle Swarm Optimization (PSO) to optimize

the power generation of the PV system based on the solar insolation level, cell

temperature, efficiency of PV panels and output voltage requirements. Further this

paper proposes an integrated offline PSO and ANN algorithms to track the solar

power optimally based on various operating conditions due to the uncertain climate

change. The proposed approach has the capability to estimate the amount of

generating PV power at a specific time. The ANN based solar insolation forecast has

shown satisfactory results with minimal error and the generated PV power has been

optimized significantly with the aids of the PSO algorithm.

31. Dzung D. Nguyen, Brad Lehman Sagar Kamarthi, in their work entitled

Performance Evaluation of Solar Photovoltaic Arrays Including Shadow Effects

Using Neural Network propose a neural network based approach to estimating the

maximum possible output power of a solar photovoltaic array under the non-uniform

shadow conditions at a given geographic location. Taking the solar irradiation levels,

the ambient temperature, and the Sun’s position angles as inputs, a multilayer feed-

forward neural network estimates the output power of the solar photovoltaic array.

Training data for the neural network is generated by conducting a series of

experiments on a shaded solar panel at different hours of a day for several days. After

training the neural network, its accuracy and generalization properties are verified on

test data. It is found that the neural network, which is an approximation of the actual

shading function, is able to estimate the maximum possible output power of the solar

PV arrays accurately. Further, the network is able to estimate the maximum output

34

power for field data and gives rise to the possibility that the proposed approach can be

used for making decisions regarding the installation of solar PV arrays in the field.

32. Jimenez-Brea, Andres Salazar-Llinasy, Eduardo Ortiz-Riverazand Jesus

Gonzalez-Llorentex in their work entitled A Maximum Power Point Tracker

Implementation for Photovoltaic Cells Using Dynamic Optimal Voltage Tracking,

present a maximum power point tracker (MPPT) for photovoltaic (PV) cells, PV

modules (PVM) and PV arrays is presented using a dynamic optimal voltage

estimator to estimate the voltage at which a PV cell generates its maximum power,

and, using a DC-DC converter, to force the PV cell to reach and operate at voltage in

a finite time and to stay there for all future time.

The optimal voltage estimator reads the temperature at the surface of the PV

array and the solar irradiance that reaches its surface to estimate the maximum power

voltage point. A sliding mode controller, implemented in a low cost microcontroller,

uses the estimated optimal voltage to generate a control signal which forces the PV

cell to track and operate in this estimated optimal voltage for all future time. The

procedures for the design, simulation, implementation and results are presented in this

paper.

33. M. Hatti, IEEE Member, A. Meharrar, M. Tioursi in their work entitled Novel

Approach of Maximum Power Point Tracking for Photovoltaic Module Neural

Network based model, focuse on the development of new methods for optimizing the

maximum power point with artificial neural networks. The aim is to develop a method

to optimize the energy extraction from a proposed solar energy generation system. In

order to achieve this, the components and subsystems are analysed and validated. The

validated models can then be used to maximize the power output of the conversion

system. More than thirty models are proposed in the literature.

This paper proposes an intelligent artificial technique to determine the

maximum power point (MPP) based on artificial neural network. The approach is

compared to perturb and observe (P&O) method. The improvement of tracking the

maximum power point of the photovoltaic array is determined and performed. The

experimental results show that the MPPT neural network based can be identified with

the improved MPPT simulation model. It is found that an MPPT artificial neural

35

network based proposed can reduce the noises and oscillations generated by classical

methods and can be competitiveness against other MPPT algorithms.

34. Weixiang Shen, Yi Ding, Fook Hoong Choo, Peng Wang, Poh Chiang Loh and

Kuan Khoon Tan in their work entitled Mathematical model of a solar module for

energy yield simulation in photovoltaic systems, present a new mathematical model of

a solar module. Solar module temperature, solar radiation and its effect on series

resistance are taken into account in the model. The experimental data of the solar

module under natural environment condition have been obtained to determine the

model parameters. Then, the developed model is used to simulate energy yield of the

solar module. This energy yield is compared with those obtained from experimental

data and conventional approach. The results indicate that the proposed approach can

have a more accurate energy yield than conventional approach. Thus, it can be useful

for the design of PV systems.

35. Syafaruddina, Takashi Hiyamab in their work entitled Feasibility of Artificial

Neural Network for Maximum Power Point Estimation of Non crystalline-Si

Photovoltaic Modules, present Solar cell markets are growing favourably. The

emerging non crystalline silicon (c-Si) technologies are starting to make significant

inroads into solar cell markets. The most of the artificial neural network (ANN) has

been used in maximum power points tracking applications for c-Si solar cell

technology. However, the characteristics of different solar cell technologies at

maximum power point (MPP) have different trends in current voltage characteristic.

36. Trishan Esram, Jonathan W. Kimball, Philip T. Krein, Patrick L. Chapman, in

their work entitled Dynamic Maximum Power Point Tracking of Photovoltaic Arrays

Using Ripple Correlation Control, present a dynamic rapid method used for tracking

the maximum power point of photovoltaic arrays, known as ripple correlation control,

is presented and verified against experiment. The technique takes advantage of the

signal ripple, which is automatically present in power converters. The ripple is

interpreted as a perturbation from which a gradient ascent optimization can be

realized. The technique converges asymptotically at maximum speed to the maximum

power point without the benefit of any array parameters or measurements. The

technique has simple circuit implementations.

36

37. Abdulhadi Varnham, Abdulrahman M. Al-Ibrahim, Gurvinder S. Virk, and

Djamel Azzi in their work entitled Soft-Computing Model-Based Controllers for

Increased Photovoltaic Plant Efficiencies, present an improved solar cell models and

control methods using the synergies of soft-computing techniques are used to

demonstrate increased energy efficiencies of photovoltaic (PV) power plants

connected to the electricity grid via space-vector-modulated three phase inverter. The

models and control strategies are combined to form two new model-based controllers

that are more accurate and resilient than existing solutions resulting in increased

power production.

A radial-basis-function-network (RBFN) model with a Neuro-fuzzy regulator

applied to a plant well characterized by the conventional solar cell model provided an

estimated 1.5% increase in power production over an existing conventional model

proportional integral (PI) -regulator combination. A Neuro-fuzzy model with a Neuro-

fuzzy controller applied to a plant poorly characterized by the conventional solar cell

model gave an 8.6% increase in power. An analysis of the net contributions to the

increased efficiencies shows that the improved models had the most effect on power

gains.

38. Engine Karatepe a, Takashi Hiyama b, Mutlu Boztepe a, Metin C¸ in their work

entitled Voltage based power compensation system for photovoltaic generation

system under partially shaded insolation conditions, present partially shaded

photovoltaic (PV) modules typically exhibit additional difficulties in tracking the

maximum power point since their power–voltage characteristics are complex and may

have multiple local maxima. For this reason, conventional techniques fail to track the

maximum power point effectively if the PV array is partially shaded or some of its

cells are damaged. This paper presents a novel power compensation system for PV

arrays for complicated non-uniform insolation conditions.

The proposed system is based on recovering the power of non-shaded PV

modules into the system again completely by forward biasing a bypass diode of the

shaded PV modules. For this purpose, the proposed system uses DC–DC converters

equipped with each PV string in the PV array. For identifying which shaded PV

modules should be deactivated, the operating voltage of the PV modules is monitored

and compared. The proposed system enables the non-shaded PV modules to operate

37

effectively at their normal maximum power point. The effectiveness of the proposed

system is investigated and confirmed for complicated partially shaded PV arrays.

39. Eftichios Koutroulis, Kostas Kalaitzakis, Nicholas C. Voulgaris in their work

entitled Development of a Microcontroller-Based Photovoltaic Maximum Power

Point Tracking Control System, present Maximum power point tracking (MPPT) is

used in photovoltaic (PV) systems to maximize the photovoltaic array output power,

irrespective of the temperature and irradiation conditions and of the load electrical

characteristics. A new MPPT system has been developed, consisting of a Buck-type

DC/DC converter, which is controlled by a microcontroller-based unit.

The main difference between the method used in the proposed MPPT system

and other techniques used in the past is that the PV array output power is used to

directly control the DC/DC converter, thus reducing the complexity of the system.

The resulting system has high-efficiency, lower-cost and can be easily modified to

handle more energy sources (e.g., wind-generators). The experimental results show

that the use of the proposed MPPT control increases the PV output power by as much

as 15% compared to the case where the DC/DC converter duty cycle is set such that

the PV array.

40. Eftichios Koutroulis and Kostas Kalaitzakis in their work entitled Design of a

Maximum Power Tracking System for Wind-Energy-Conversion Applications,

present a wind-generator (WG) maximum-power-point tracking (MPPT) system is

presented, consisting of a high efficiency buck-type DC/DC converter and a

microcontroller-based control unit running the MPPT function. The advantages of the

proposed MPPT method are that no knowledge of the WG optimal power

characteristic or measurement of the wind speed is required and the WG operates at a

variable speed. Thus, the system features higher reliability, lower complexity and

cost, and less mechanical stress of the WG. Experimental results of the proposed

system indicate near optimal WG output power, increased by 11%–50% compared to

a WG directly connected via a rectifier to the battery bank. Thus, better exploitation

of the available wind energy is achieved, especially under low wind speeds.

41. Adel El Shahat in his work entitled Maximum power point genetic identification

function for photovoltaic system propose the identification of maximum power point

38

(MPP) function for photovoltaic (PV) module using the genetic algorithm (GA). Then

deduction of the required function to generate the reference values to drive the

tracking system in the PV system at MPP is done with the aid of Artificial Neural

Network (ANN). This function deals with the more probable situations for variable

values of temperature and irradiance to get the corresponding voltage and current at

maximum power. The mathematical PV module modelling depends on Schott ASE-

300-DGF PV panel with the aid of MATLAB environment.

42. Masoum, Mohammad A. S. Dehbonei, Hooman in their work entitled Design,

Construction and Testing of a Voltage-based Maximum Power Point Tracker

(VMPPT) for Small Satellite Power Supply, It is shown that at maximum power, the

Photovoltaic (PV) voltage varies nonlinearly with temperature and isolation level, but

is directly proportional to the PV cell open circuit voltage. The proportionality

voltage-factor is fixed for a given PV generator regardless of temperature, isolation

and panel configuration, but depends on cell material and manufacturing. This

remarkable property is used to achieve temperature and insolation independent

maximum power point tracking of satellite’s solar cells with a simple and reliable

technique. The open circuit voltage is continuously measured by a microcontroller

and is used to estimate the maximum power operating point of the system.

43. Eftichios Koutroulis Frede Blaabjerg, in their work entitled A New Technique for

Tracking the Global Maximum Power Point of PV Arrays Operating Under Partial-

Shading Conditions, present a power–voltage characteristic of photovoltaic (PV)

arrays operating under partial-shading conditions exhibits multiple local maximum

power points (MPPs). In this paper, a new method to track the global MPP is

presented, which is based on controlling a DC/DC converter connected at the PV

array output, such that it behaves as a constant input-power load. The proposed

method has the advantage that it can be applied in either standalone or grid-connected

PV systems comprising PV arrays with unknown electrical characteristics and does

not require knowledge about the PV module's configuration within the PV array.

44. Lian Lian Jiang, Douglas L. Maskell, and Jagdish C. Patra in their work entitled

An ANN-based controller for maximum power point tracking in PV systems under

rapidly changing conditions, present In order to increase the efficiency of the

Photovoltaic (PV) system. The PV system should be operated at the Maximum Power

39

Point (MPP). The MPP Tracking (MPPT) is an essential part in achieving this

improvement. Some of the existing techniques such as Perturb-and-Observe (P&O)

and Incremental Conductance (INC) are relatively simpler to implement, but under

rapidly changing irradiance and temperature conditions, they fail to track the MPP.

Although methods such as Multilayer Perceptron (MLP) and Fuzzy Logic (FL) are

efficient in tracking the MPP, their implementation increases the system complexity.

45. Mahmoud A. Younis, Tamer Khatib, Mushtaq Najeeb, A Mohd Ariffin in their

work entitled An Improved Maximum Power Point Tracking Controller for PV

Systems Using Artificial Neural Network, present an improved maximum power

point tracking (MPPT) controller for PV systems. An Artificial Neural Network and

the classical P&O algorithm were employed to achieve this objective. MATLAB

models for a neural network, PV module, and the classical P&O algorithm are

developed. However, the developed MPPT uses the ANN to predict the optimum

voltage of the PV system in order to extract the maximum power point (MPP).

2.8 Research Objectives

Based on the review of literature and the discussion which are made earlier on

the Studies on Maximum Power Point Tracking Techniques for PV cells using

Evolutionary Algorithms. The objectives of this research work have been carried out

to accomplish the required task by focusing on the following.

1. Design a PV cell model using Single diode and Two diode model using MATLAB /

SIMULINK Software and validation of the model for different operating conditions

of temperature and irradiance.

2. Design of Simscape model of a commercial PV module SHELLSQ175 and

Development of a model for of a six panel PV array using the SHELLSQ175

Simscape model

3. Evaluate and analyze the scope of evolutionary algorithm in general i.e.

Genetic Algorithm and Differential evolution in particular for extraction of PV cell

parameters using a synthetic data approach.

40

4. Develop and analyze the MPPT techniques by Short-circuit Current and

Incremental-Conductace with Direct control Methods and Analyse the GA trained

ANN based MPPT of PV Cells under partially shadow conditions.

5. Develop a Fuzzy logic based MPPT with PWM technique for uniform insolation of

PV Cells and Design a Fuzzy logic based MPPT of PV Cells under partially

shadowed conditions. Analyze the GA trained Fazzy Logic based MPPT of PV Cells

under under partially shadowed conditions.

2.9 Problem Statement

The problem can be envisaged in two steps:

1) Extraction of PV cell parameters – An insight in to PV cell parameters is an

essential factor to know the physical process and accurate modelling and simulation.

2) Extraction of Maximum Power Point –The characteristics of Maximum Power

Point as the variable power source of the PV cell, provides a difficult task in

extracting maximum power from PV modules. This state becomes much complicated

in case of partial shading conditions resulting in multiple peaks.

2.10. Summary

This chapter discusses in detail about the characteristics of PV systems and

how the I-V characteristics of the PV modules are affected by different values of

irradiance and temperature. The survey of literature used to define the aim and

research objectives are presented. It also states the objectives that lay the foundation

of this research work and concludes by presenting the problem statement.