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CHAPTER 1

INTRODUCTION

It's certainly clear that fossil fuels are mangling the climate and that the status quo is

unsustainable. There is now a broad scientific consensus that the world needs to

reduce greenhouse gas emissions more than 25 percent by 2020 -- and more than 80

percent by 2050. The idea of harnessing the sun’s power has been around for ages.

The basic process is simple. Solar collectors concentrate the sunlight that falls on

them and convert it to energy. Solar power is a feasible way to supplement power in

cities. In rural areas, where the cost of running power lines increases.

Solar power, a clean renewable resource with zero emission, has got tremendous

potential of energy which can be harnessed using a variety of devices. With recent

developments, solar energy systems are easily available for industrial and domestic

use with the added advantage of minimum maintenance. Solar energy could be made

financially viable with government tax incentives and rebates. An exclusive solar

generation system of capacity 250KWh per month would cost around Rs. 20 lakhs,

with present pricing and taxes (2013). Most of the developed countries are switching

over to solar energy as one of the prime renewable energy source.

1.1 THE NEED FOR RENEWABLE ENERGY

Renewable energy is the energy which comes from natural resources such as sunlight,

wind, rain, tides and geothermal heat. These resources are renewable and can be

naturally replenished. Therefore, for all practical purposes, these resources can be

considered to be inexhaustible, unlike dwindling conventional fossil fuels. The global

energy crunch has provided a renewed impetus to the growth and development of

Clean and Renewable Energy sources. Clean Development Mechanisms (CDMs) are

being adopted by organizations all across the globe. Apart from the rapidly decreasing

reserves of fossil fuels in the world, another major factor working against fossil fuels

is the pollution associated with their combustion. Contrastingly, renewable energy

sources are known to be much cleaner and produce energy without the harmful effects

of pollution unlike their conventional counterparts.

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1.2 DIFFERENT SOURCES OF RENEWABLE ENERGY

1.2.1 WIND POWER

Wind turbines can be used to harness the energy available in airflows. Current day

turbines range from around 600 kW to 5 MW of rated power. Since the power output

is a function of the cube of the wind speed, it increases rapidly with an increase in

available wind velocity. Recent advancements have led to aerofoil wind turbines,

which are more efficient due to a better aerodynamic structure.

1.2.2 SMALL HYDROPOWER

Hydropower installations up to 10MW are considered as small hydropower and

counted as renewable energy sources. These involve converting the potential energy

of water stored in dams into usable electrical energy through the use of water turbines.

Run-of-the-river hydroelectricity aims to utilize the kinetic energy of water without

the need of building reservoirs or dams.

1.2.3 BIOMASS

Plants capture the energy of the sun through the process of photosynthesis. On

combustion, these plants release the trapped energy. This way, biomass works as a

natural battery to store the sun’s energy and yield it on requirement.

1.2.4 GEOTHERMAL

Geothermal energy is the thermal energy which is generated and stored within the

layers of the Earth. The gradient thus developed gives rise to a continuous conduction

of heat from the core to the surface of the earth. This gradient can be utilized to heat

water to produce superheated steam and use it to run steam turbines to generate

electricity. The main disadvantage of geothermal energy is that it is usually limited to

regions near tectonic plate boundaries, though recent advancements have led to the

propagation of this technology.

1.2.5 SOLAR POWER

The tapping of solar energy owes its origins to the British astronomer John Herschel

who famously used a solar thermal collector box to cook food during an expedition to

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Africa. Solar energy can be utilized in two major ways. Firstly, the captured heat can

be used as solar thermal energy, with applications in space heating. Another

alternative is the conversion of incident solar radiation to electrical energy, which is

the most usable form of energy. This can be achieved with the help of solar

photovoltaic cells or with concentrating solar power plants.

As the Photovoltaic module exhibits non-linear V-I Characteristics, which are

dependent on solar Insolation and environment factors, the development of an

accurate power electronic circuit oriented model is essential to simulate and design

the photovoltaic integrated system. In this paper, the design of PV system using

simple circuit model with detailed circuit modelling of PV module using

MATLAB/Simulink and the physical equations governing the PV module is

presented.

1.3 LITERATURE REVIEW

Studies show that a solar panel converts 21-40% of energy incident on it to electrical

energy. A Maximum Power Point Tracking algorithm is necessary to increase the

efficiency of the solar panel.

There are different techniques for MPPT such as Perturb and Observe (hill climbing

method), Incremental conductance, Fractional Short Circuit Current, Fractional Open

Circuit Voltage, Fuzzy Control, Neural Network Control etc. Among all the methods

Perturb and observe (P&O) and Incremental conductance are most commonly used

because of their simple implementation, lesser time to track the MPP and several other

economic reasons.

Under abruptly changing weather conditions (irradiance level) as MPP changes

continuously, P&O takes it as a change in MPP due to perturbation rather than that of

irradiance and sometimes ends up in calculating wrong MPP. However this problem

gets avoided in Incremental Conductance method as the algorithm takes two samples

of voltage and current to calculate MPP. However, instead of higher efficiency the

complexity of the algorithm is very high compared to the previous one and hence the

cost of implementation increases. So we have to mitigate with a trade-off between

complexity and efficiency.

It is seen that to get maximum efficiency we are getting which type of converter. We

are choosing here boost converter because it provide us more voltage at output then

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input. We can also choose buck-boost converter but due to our simplification and

requirement we are selecting boost converter. It is very simple to implement and has

high efficiency both under stationary and time varying atmospheric conditions.

N. Pandiarajan and Ranganath Muth, This paper presents a unique step-by-step

procedure for the simulation of photovoltaic modules with Matlab/ Simulink. One-

diode equivalent circuit is employed in order to investigate I-V and P-V

characteristics of a typical 36 W solar module. The proposed model is designed with a

user-friendly icons and a dialog box like Simulink block libraries [1].

Alpesh P. parekh, Bhavarty N. Vaidya and Chirag T. Patel, In this paper, the

design of PV system using simple circuit model with detailed circuit modelling of PV

module is presented. In this paper, Equivalent circuit of the PV module & Simulink

model for each equation has presented and complete circuit oriented model has also

presented [2].

Pandiarajan N, Ramaprabha R and Ranganath Muthu, Circuit model of

photovoltaic (PV) module is presented in this paper that can be used as a common

platform for the material scientists as well as power electronic circuit designers to

develop the better PV power plant. Detailed modeling procedure for the circuit model

with numerical dimensions is presented using power system block set of MATLAB/

Simulink. The developed model is integrated with DC-DC boost converter with

closed loop control of maximum power point tracking (MPPT) algorithm. The

simulation results are validated with the experimental set up [3].

P.Sathya, Dr.R.Natarajan, this paper presents the design and implementation of

high performance closed loop Boost converter for solar powered HBLED lighting

system. The proposed system consists of solar photovoltaic module, a closed loop

boost converter and LED lighting module. The closed loop boost converter is used to

convert a low level dc input voltage from solar PV module to a high level dc voltage

required for the load. To regulate the output of the converter, closed loop voltage

feedback technique is used. The feedback voltage is compared with a reference

voltage and a control signal is generated and amplified. The amplified signal is fed to

555 Timer which in turn generates a PWM signal which controls the switching of

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MOSFET. Thus by switching of MOSFET it would try to keep output as constant.

Initially the boost converter, timer circuit, amplifier circuit and LED light circuits are

designed, simulated and finally implemented in printed circuit board. The simulation

studies are carried out in MULTISIM. The experimental results for solar PV and

boost converter obtained in both software and hardware was presented in this paper

[7].

Vandana Khanna, Bijoy Kishore Das, Dinesh Bisht, A Simulation model for

simulation of a single solar cell and two solar cells in series has been developed using

Simelectronics (Matlab/Simulink) environment and was presented in this paper. A

solar cell block is available in simelectronics, which was used with many other blocks

to plot I-V and P-V characteristics under variations of parameters considering one

parameter variation at a time. The effect of variation of parameters such as series

resistance, Rs, shunt resistance Rsh, diode parameters: diode saturation current, Is and

ideality factor, N, could be seen on the characteristics of a single solar cell. Effect of

two environmental parameters of temperature and irradiance variations could also be

observed from simulated characteristics. Matlab coding has been done to find the

maximum power output, Pm, and voltage at maximum power output, Vm, of a single

solar cell and two solar cells (in series) under different values of parameters. The

Pmand Vm values are tabulated here in this paper for variation of one parameter at a

time, considering the diode parameters: Is and N, resistances: series and shunt,

temperature and irradiance [5].

G. Venkateswarlu and Dr.P.Sangameswar Raju, The study of photovoltaic

systems in an efficient manner requires a precise knowledge of the IV and PV

characteristic curves of photovoltaic modules. A Simulation model for simulation of a

single solar cell and two solar cells in series has been developed using Sim electronics

(Mat lab /Simulink) environment and is presented here in this paper. A solar cell

block is available in simelectronics, which was used with many other blocks to plot I-

V and P-V characteristics under variations of parameters considering one parameter

variation at a time. Effect of two environmental parameters of temperature and

irradiance variations could also be observed from simulated characteristics [4].

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1.4 OBJECTIVE

The basic objective would be to study MPPT and successfully implement the MPPT

algorithms either in code form as well as using the Simulink/Simscape model.

Modelling of the solar cell in Simulink/Simscape and interfacing both with the MPPT

algorithm to obtain the maximum power point operation would be of prime

importance. After simulating our result with the help of Simulink/Simscape we would

like to implement it on hardware using Field Programmable Gate Array (FPGA).

Fig.1.1 MPPT Technique with Solar Cell

1.5 FUTURE SCOPE OF RENEWABLE ENERGY RESOURCES

The current trend across developed economies tips the scale in favour of Renewable

Energy. For the last three years, the continents of North America and Europe have

embraced more renewable power capacity as compared to conventional power

capacity. Renewables accounted for 60% of the newly installed power capacity in

Europe in 2009 and nearly 20% of the annual power production.

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Fig.1.2 Global Energy Consumption in the Year 2008

1.6 THESIS OUTLINE

This thesis has been broadly divided into 7 chapters. The first one being the

introduction, chapter 2 is on photovoltaic effect and modelling of solar cell with

Matlab Simulink/Simscape and effect of load mismatching. In chapter 3 we will study

about Boost Converter. Chapter 4 is on maximum power point tracking and study of

the various algorithms. Chapter 5 will discuss about FPGA & Hardware

Implementation. Result and conclusion is discussed in chapter 6 & 7.

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CHAPTER 2

MODELLING OF PV PANEL

2.1 PHOTOVOLTAIC CELL

A photovoltaic cell or photoelectric cell is a semiconductor device that converts light

to electrical energy by photovoltaic effect. If the energy of photon of light is greater

than the band gap then the electron is emitted and the flow of electrons creates

current.

However a photovoltaic cell is different from a photodiode. In a photodiode light falls

on n-channel of the semiconductor junction and gets converted into current or voltage

signal but a photovoltaic cell is always forward biased.

2.2 PV MODULE

Usually a number of PV modules are arranged in series and parallel to meet the

energy requirements. PV modules of different sizes are commercially available

(generally sized from 60W to 170W). For example, a typical small scale desalination

plant requires a few thousand watts of power.

2.3 PV ARRAY

A PV array consists of several photovoltaic cells in series and parallel connections.

Series connections are responsible for increasing the voltage of the module whereas

the parallel connection is responsible for increasing the current in the array.

Fig.2.1 Different Solar Modules

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2.4 PV MODELLING

Typically a solar cell can be modelled by a current source and an inverted diode

connected in parallel to it. It has its own series and parallel resistance. Series

resistance is due to hindrance in the path of flow of electrons from n to p junction and

parallel resistance is due to the leakage current.

When irradiance hits the surface of solar PV cell, an electrical field is generated inside

the cell. As seen in Fig.3 this process separates positive and negative charge carriers

in an absorbing material (joining p-type and n-type). In the presence of an electric

field, these charges can produce a current that can be used in an external circuit. This

generated current depends on the intensity of the incident radiation. The higher the

level of light intensity, the more electrons can be unleashed from the surface, the more

current is generated.

Fig.2.2 Schematic Cross-Section of a Typical Solar Cell

The most important component that affects the accuracy of the simulation is the PV

cell model. Modelling of PV cell involves the estimation of the I-V and P-V

characteristics curves to emulate the real cell under various environmental conditions.

An ideal solar cell is modelled by a current source in parallel with a diode. However

no solar cell is ideal and thereby shunt and series resistances are added to the model as

shown in the Fig.4

Fig.2.3 Equivalent Circuit of PV Cell

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The current source Ipv represents the cell photo current, Rsh and Rs are used to

represent the intrinsic series and shunt resistance of the cell respectively. Usually the

value of Rsh is very large and that of Rs is very small, hence they may be neglected to

simplify the analysis.

The PV mathematical model used to simplify our PV array is represented by the

equations (1)-(4)

Module Photo Current

[ ( )]

( )

Module Reverse Saturation Current

(

) ( )

Module Saturation Current

[

]

[(

) (

)] ( )

The Current Output of PV module is

[

] ( )

Where

Vpv is output voltage of a PV module (V)

Ipv is output current of a PV module (A)

Tr is the reference temperature = 298 K

T is the module operating temperature in Kelvin

Iph is the light generated current in a PV module (A)

Io is the PV module saturation current (A)

A = B is an ideality factor = 1.6

k is Boltzmann constant = 1.3805 × 10-23 J/K

q is Electron charge = 1.6 × 10-19 C

Rs is the series resistance of a PV module

ISCr is the PV module short-circuit current at 25 oC and

1000W/m2 = 2.55A

Ki is the short-circuit current temperature co-efficient at

ISCr = 0.0017A / oC

λ is the PV module illumination (W/m2) = 1000W/m

2

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Ego is the band gap for silicon = 1.1 Ev

Ns is the number of cells connected in series

Np is the number of cells connected in parallel

2.5 MATLAB SIMULINK MODEL OF PHOTOVOLTAIC CELL

(A) Temperature Conversion (°C to °F)

Trk=273+25(ref.temp.)

Tak=273+Top(operating Temp.)

Table 2.1 Electrical Characteristics Data of Green SolarIndia37W (AT-37) PV

Module

Electrical Characteristics

Maximum power - Pmax 36.917 W

Voltage at Pmax - Vmp 17.905 V

Current at Pmax - Imp 2.062 A

Short-circuit current - Isc 2.226 A

Open-circuit voltage - Voc 21.425 V

Total number of cells in series (Ns) 36

Total number of cells in parallel (Np) 1

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Fig.2.4 Block For Temperature Conversion

(B) Module Photo Current

[ ( - )]

Fig.2.5 Block For Module Photo Current

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(C) Module Reverse Saturation Current

Fig.2.6 Block For Reverse Saturation Current

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(D) Module Saturation Current

[

]

[(

) (

-

)]

Fig.2.7 Block for Saturation Current

(E) The Output Current of PV module

[

]

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Fig.2.8 Block for Output Current

(F) The Nsakt Will Be Prepared As Show Below

Fig.2.9 Block for NsAkT

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Fig.2.10 Interconnection of All Blocks

Fig.2.11 Complete Block of PV Module

Now we have to add a current controlled source which is connected with Ipv of panel.

The series and parallel resistances are also connected to make this model

practicle.Fig.2.12 show the complete model.

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Fig.2.12 Detailed circuit model of PV module.

2.6 EFFECT OF LOAD MISMATCHING

From PV module P-V characteristics we have seen there is only one point where

power is maximum, the corresponding voltage is VMPP and current is IMPP. If load line

crosses this point the maximum power is transferred to load. This value of load

resistance is given by:

Fig.2.13 PV Interfacing to Load

A PV cell behaves differently depending on the size/type of load connected to it. The

output power of PV panel is greatly depended upon the load at output side. The

delivered power cannot be maximum if there is load mismatching. Load mismatching

is a difference between the internal resistance of source and load at output side.

According to maximum power transfer theorem, when the equivalent resistance of

source is equal to the load resistance, the maximum power will delivered. The

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equivalent resistance is called characteristics impedance which can be easily find out

from the data sheet given by manufacturer. If load is equal to this characteristic

impedance, then we will get maximum power from the solar panel. We can calculate

characteristic impedance from VMPP and IMPP values given in data sheet. For present

case RMPP is 7.9Ω. Here we take three conditions.

Case (1): When Load resistance is more than characteristic impedance in Fig.15. The

output power is 19.83Watt which is less than its rated maximum power 36 Watt (at

1000W/m2).

Case (2): When Load resistance is less than characteristic impedance in Fig.16 The

output power is 32 Watt which is less than its rated maximum power 36 Watt (at

1000W/m2).

Case (3): When Load resistance is equal to characteristics impedance in Fig.17. The

output power is about 36 Watt which is maximum at 1000 W/m2.

Fig.2.14 Output Power of PV Module At Rload > Rmpp

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Fig.2.15 Output Power of PV Module at RLOAD < RMPP

Fig.2.16 Output Power of PV Module at RLOAD = RMPP

Figure 2.14, 2.15 & 2.16 shows the effect on efficiency of PV module due to different

load applied by user. The PV module power reduces if the load is not properly match

with the characteristics resistance of the PV module. This problem can be solved by

applying DC-DC converter in between PV module & Load.

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CHAPTER 3

BOOST CONVERTER

A boost converter is designed to step up a fluctuating or variable input voltage to a

constant output voltage of 24 volts with input range of 6-23volts in. To produce a

constant output voltage feedback loop is used. The output voltage is compared with a

reference voltage and a PWM wave is generated, here Spartan 6 FPGA kit is used to

generate PWM signal to control switching action.

A DC to DC converter is used to step up from 12V to 24V. The 12V input voltage is

from the battery storage equipment and the 24V output voltage serves as the input of

the inverter in solar electric system. In designing process, the switching frequency, f is

set at 20 kHz and the duty cycle, D is 50%.

Here we want to introduced an approach to design a boost converter for photovoltaic

(PV) system using microcontroller. The converter is designed to step up solar panel

voltage to a stable 24V output without storage elements such as battery. It is

controlled by a FPGA unit using voltage-feedback technique. The output of the boost

converter is tracked, measured continuously and the values are sent to the

microcontroller unit to produce pulse-width-modulation (PWM) signal. The PWM

signal is used to control the duty cycle of the boost converter. Typical application of

this boost converter is to provide DC power supply for inverter either for grid-

connected or standalone system. Simulation and experimental results describe the

performance of the proposed design. Spartan 6 FPGA is used to perform tasks in the

proposed design.

As stated in the introduction, the maximum power point tracking is basically a

load matching problem. In order to change the input resistance of the panel to

match the load resistance (by varying the duty cycle), a DC to DC converter is

required.

It has been studied that the efficiency of the DC to DC converter is maximum

for a buck converter, then for a buck-boost converter and minimum for a boost

converter but as we intend to use our system either for tying to a grid or for a

water pumping system which requires 230 Vat the output end, so we use a boost

converter.

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Fig.3.1 Circuit Diagram of a Boost Converter

3.1. MODE 1 OPERATION OF THE BOOST CONVERTER

When the switch is closed the inductor gets charged through the battery and stores the

energy. In this mode inductor current rises (exponentially) but for simplicity we

assume that the charging and the discharging of the inductor are linear. The

diode blocks the current flowing and so the load current remains constant which

is being supplied due to the discharging g of the capacitor.

Fig.3.2 Mode 1 Operation of the Boost Converter

3.2. MODE 2 OPERATION OF THE BOOST CONVERTER

In mode 2 the switch is open and so the diode becomes short circuited. The energy

stored in the inductor gets discharged through opposite polarities which charge the

capacitor. The load current remains constant throughout the operation. The waveform

for a boost converter are shown in figure.

Fig.3.3 Mode 2 Operation of the Boost Converter

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3.3. MODELING OF BOOST CONVERTER USING MATLAB

SIMSACPE

Fig.3.4 Modelling of Boost DC-DC Converter

3.4. DESIGN APPROACH OF PROPOSED BOOST CONVERTER

Load Requirement: The load is a simple 4 x 4 LED panel and each row containing 4

LED in a line would require a current of 10- 15 mA and thus total of 60 mA to all four

branches and thus having a resistance of 570Ω. As each LED gives a drop of 2.1

volts to become forward biased, so a minimum of 8.4 volts is required to glow 4 LED

in series, for this a voltage of 24 V is required to be supplied to LEDs. Thus the load

requirement is 570 Ω with 42 mA of total current thus required voltage was 24 V.

Since a potential divider is used whose total resistance is 1100Ω so total equivalent

resistance is Req = (1100) (570) = 375Ω.Based on this load requirement the other

parameters would be calculated and the specifications are tabulated in the following

table.

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Table 3.1 Specification for Boost Converter

S.No. Component Value

1 Inductor 290µH

2 MOSFET 1N5408 IRF 840

3 Power Diode IN5408

4 Input Capacitor 470µF

5 Output Capacitor 330 µF

6 Resistive Load 50Ω, 50W

Duty Cycle:

The duty cycle can be found using the following relation-

D=1-

Inductor value:

The value of inductor is determined using the following relation

Lmin=D (1-D2)*R/2*Fs

An inductor is practically designed using the following parameters and is shown in

the figure 22.

Formula for inductor design, L = (d2n2) / (l + 0.45d)

Required dimensions of inductor

Coil length, l= 8.1 cm

Diameter, d= 6.3 cm

Inductance value required, L= 151 μH

Number of turns, n=64

Where L is inductance in micro Henrys,

d is coil diameter in meters,

l is coil length in meters, and

n is number of turns

Capacitor value:

The value of capacitor is determined from the following equation

C=D/Fs*R*Vr

Where

C is the minimum value of capacitance,

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D is duty cycle,

R is output resistance,

Fs is switching frequency, and

Vr is output voltage ripple factor.

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CHAPTER 4

MAXIMUM POWER POINT TRACKING ALGORITHM

4.1. AN OVERVIEW OF MAXIMUM POWER POINT TRACKING

A typical solar panel converts only 30 to 40 percent of the incident solar

irradiation into electrical energy. Maximum power point tracking technique is used

to improve the efficiency of the solar panel.

According to Maximum Power Transfer theorem, the power output of a circuit

is maximum when the Thevenin 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 we are using a boost convertor connected to a solar pan el 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 we can match the source impedance with that of the load impedance.

4.2. DIFFERENT MPPT TECHNIQUES

There are different techniques used to track the maximum power point. Few of the

most popular techniques are:

1) Perturb and Observe (hill climbing method)

2) Incremental Conductance method

3) Fractional short circuit current

4) Fractional open circuit voltage

5) Neural networks

6) Fuzzy logic

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4.3 PERTURB & OBSERVE

Perturb & Observe (P&O) is the simplest method. In this we use only one sensor, that

is the voltage sensor, to sense the PV array voltage and so the cost of implementation

is less and hence easy to implement. The time complexity of this algorithm is very

less but on reaching very close to the MPP it doesn’t stop at the MPP and keeps on

perturbing on both the directions. When this happens the algorithm has reached very

close to the MPP and we can set an appropriate error limit or can use a wait function

which ends up increasing the time complexity of the algorithm. However the method

does not take account of the rapid change of irradiation level (due to which

MPPT changes) and considers it as a change in MPP due to perturbation and ends up

calculating the wrong MPP. To avoid this problem we can use incremental

conductance method.

4.4. INCREMENTAL CONDUCTANCE

Incremental conductance method uses two voltage and current sensors to sense the

output voltage and current of the PV array. At MPP the slope of the PV curve is 0.

(dP/dV)MPP=d(VI)/dV

0=I+VdI/dVMPP

dI/dVMPP = - I/V

The left hand side is the instantaneous conductance of the solar panel. When this

instantaneous conductance equals the conductance of the solar then MPP is reached.

Here we are sensing both the voltage and current simultaneously. Hence the error due

to change in irradiance is eliminated. However the complexity and the cost of

implementation increases. As we go down the list of algorithms the complexity and

the cost of implementation goes on increasing which may be suitable for a highly

complicated system. This is the reason that Perturb and Observe and Incremental

Conductance method are the most widely used algorithms. Owing to its simplicity of

implementation we have chosen the Perturb & Observe algorithm for our study

among the two.

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4.5. FRACTIONAL OPEN CIRCUIT VOLTAGE

The near linear relationship between VMPP and VOC of the PV array, under varying

irradiance and temperature levels, has given rise to the fractional VOC method.

VMPP = k1 Voc

where k1 is a constant of proportionality. Since k1 is dependent on the characteristics

of the PV array being used, it usually has to be computed beforehand by empirically

determining VMPP and VOC for the specific PV array at different irradiance and

temperature levels. The factor k1 has been reported to be between 0.71 and 0.78.

Once k1 is known, VMPP can be computed with VOC measured periodically by

momentarily shutting down the power converter. However, this incurs some

disadvantages, including temporary loss of power.

4.6. FRACTIONAL SHORT CIRCUIT CURRENT

Fractional ISC results from the fact that, under varying atmospheric conditions, IMPP is

approximately linearly related to the ISC of the PV array.

IMPP =k2 Isc

Where k2 is a proportionality constant. Just like in the fractional VOC technique, k2 has

to be determined according to the PV array in use. The constant k2 is generally found

to be between 0.78 and 0.92. Measuring ISC during operation is problematic. An

additional switch usually has to be added to the power converter to periodically short

the PV array so that ISC can be measured using a current sensor.

4.7. FUZZY LOGIC CONTROL

Microcontrollers have made using fuzzy logic control popular for MPPT over last

decade. Fuzzy logic controllers have the advantages of working with imprecise inputs,

not needing an accurate mathematical model, and handling nonlinearity.

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4.8. NEURAL NETWORK

Another technique of implementing MPPT which are also well adapted for

microcontrollers is neural networks. Neural networks commonly have three layers:

input, hidden, and output layers. The number nodes in each layer vary and are user-

dependent. The input variables can be PV array parameters like VOC and ISC,

atmospheric data like irradiance and temperature, or any combination of these. The

output is usually one or several reference signals like a duty cycle signal used to drive

the power converter to operate at or close to the MPP.

Table4.1 Characteristics of Different MPPT Technique

4.9. DETAILS OF PERTURB & OBSERVE ALGORITHM

The Perturb & Observe algorithm states that when the operating voltage of the

PV panel is perturbed by a small increment, if the resulting change in power P

is positive, then we are going in the direction of MPP and we keep on perturbing

in the same direction. If P is negative, we are going away from the direction of

MPP and the sign of perturbation supplied has to be changed.

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Fig.4.1 Solar Panel Characteristics Showing MPP And Operating Points A AndB

Figure 4.1 : Solar panel characteristics showing MPP and operating points A and B

Figure 4.1 shows the plot of module output power versus module voltage for a

solar pan el at a given irradiation. The point marked as MPP is the Maximum

Power Point, the theoretical maximum output obtainable from the PV panel.

Consider A and B as two operating points. As shown in the figure above, the

point A is on the left hand side of the MPP. Therefore, we can move towards

the MPP by providing g a positive perturbation to the voltage. On the other

hand, point B is on the right hand side of the MPP. When we give a positive

perturbation, the value of P becomes negative, thus it is imperative to change

the direction of perturbation to achieve MPP. The flowchart for the P&O algorithm

is shown in Figure

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Fig.4.2 Flowchart Of Perturb & Observe Algorithm

We implement above algorithm on FPGA using Hardware Description Language

Verilog. Hardware implementation will be discuss in next chapter.

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4.10. MODELLING OF P&O ALGORITHM

Fig.4.3 Modelling of P&O Algorithm

32

4.11. COMPLETE MODEL OF PV PANEL WITH MPPT

Fig.4.4 Complete Model of PV Panel With MPPT

33

CHAPTER 5

HARDWARE IMPLEMENTATION USING FPGA

5.1. FIELD-PROGRAMMABLE GATE ARRAY

A field-programmable gate array (FPGA) is a semiconductor device that can be

configured by the customer or designer after manufacturing—hence the name "field-

programmable". To program an FPGA you specify how you want the chip to work

with a logic circuit diagram or a source code in a hardware description language

(HDL). FPGAs can be used to implement any logical function that an application-

specific integrated circuit (ASIC) could perform, but the ability to update the

functionality after shipping offers advantages for many applications.

FPGAs contain programmable logic components called "logic blocks", and a

hierarchy of reconfigurable interconnects that allow the blocks to be "wired

together"—somewhat like a one-chip programmable breadboard. Logic blocks can be

configured to perform complex combinational functions, or merely simple logic gates

like AND and XOR. In most FPGAs, the logic blocks also include memory elements,

which may be simple flip-flops or more complete blocks of memory.

For any given semiconductor process, FPGAs are usually slower than their fixed

ASIC counterparts. They also draw more power, and generally achieve less

functionality using a given amount of circuit complexity. But their advantages include

a shorter time to market, ability to re-program in the field to fix bugs, and lower non-

recurring engineering costs. Vendors can also take a middle road by developing their

hardware on ordinary FPGAs, but manufacture their final version so it can no longer

be modified after the design has been committed. In short we can say that-

Gate array - a custom VLSI circuit consisting of huge number of unconnected

gates.

Circuit function determined at the field by the user,

Re-programmable,

Pre-tested for manufacturing defects,

It is: – An ASIC for FPGA manufacturer – An FPGA for the user

34

5.2. FPGA DEVICES

Xilinx Devices

– Virtex Series (High performance)

• Virtex-6

• Virtex-5

• Virtex-4

• Virtex II & Virtex II pro

• Virtex

– Spartan Series (Low cost)

• Spartan-6 (Used in our project)

• Spartan-3, 3A & 3E

• Spartan- II & IIE

• Spartan

Altera Devices

– Stratix Series (High Performance)

• Stratix IV

• Stratix V

• Stratix III

• Stratix II (GX)

• Stratix (GX)

– Cyclone Series (Low cost)

• Cyclone IV

• Cyclone III

• Cyclone II

• Cyclone

Note1: Devices are also available from Lattice and Actel.

35

5.3. FPGA ARCHITECTURE

Fig.5.1 FPGA Architecture

5.4. FEATURES OF SPARTAN 6 FAMILY

Spartan-6 Family:

Spartan-6 LX FPGA Logic optimized

Spartan-6 LXT FPGA: High-speed serial connectivity

Designed for low cost

Low static and dynamic power

45 nm process optimized for cost and low power

Multi-voltage, multi-standard Select IO™ interface banks

Up to 1,080 Mb/s data transfer rate per differential I/O

High-speed GTP serial transceivers in the LXT FPGAs

Up to 3.125 Gb/s

High-speed interfaces including: Serial ATA, Aurora, 1G Ethernet, PCI

Express, OBSAI, CPRI, EPON, GPON, Display Port, and XAUI

Integrated Endpoint block for PCI Express designs (LXT)

36

5.5. FPGA INTERFACES IN ULK

Fig.5.2 ULK board Top View

37

Fig.5.3 ULK Board Bottom View

5.6. IMPLEMENTATION OF ADC MODULE

The ADC0804 is an 8 bit successive approximation analog to digital convertor. The

pin configuration of the ADC0804 is shown in Figure. As shown the package type is

the 20 pin dual in line (DIP) package. The analog input voltage range is 0 to VCC

volts. The supply voltage input is represented by VCC. Its maximum is 6.5 volts. The

ADC0804 has two grounds: analog ground (A GRD) and digital ground (D GRD).

These two separate grounds insure that noise from analog circuits does not leak into

the digital circuits within the chip. It consists of 4 digital control inputs: CS , WR,

INT , and RD. The analog inputs, Vin+ and Vin, are differential. When the analog to

digital conversion of the analog input is complete, the results can be read from the

outputs, D0, D1, …D7.

Start conversion of an analog voltage on the differential inputs, Vin+ and Vin-, begin

when the chip select, CS , is low and the write, WR, are both low. The chip select,

CS, must be low in order for the chip to be functional. When WR goes from being

38

high to low, the SAR register is initialized and the interrupt, INT , is set high. When

the conversion is complete the interrupt goes active, that is, it goes from high to low

indicating that the converted data is ready to be read from the outputs D0, D1, D2,

D3, D4, D5, D6, and D7.

Read converted data To read data when the conversion is complete the chip select

must go low, followed by a high to low transition of the RD input. When the RD

input goes low this resets the interrupt and causes the output data latches to be

enabled so that the internal converted data appears on the data bus (D0, D1,…. D7).

The four digital control signals are summarized in the Table-

Table 5.1 ADC0804 Digital Control Inputs And Their Active Function

Digital control inputs Active function

CS /I Chip select

RD/I Enable output

WR/I Start conversion

INT /O Data is ready

.

Continuous conversion For continuous conversion the chip select, CS, and the read,

RD, are grounded, and the interrupt, INT , and the write, WR, are connected. In the

continuous conversion configuration, the WR and INT pins must be brought low after

power up to insure that the conversion process is started. See Figure 29 for the self-

clocking configuration of the ADC0804.

39

Fig.5.4 Pin Configuration of ADC 0804

40

Fig.5.5 Circuit Diagram of ADC 0804

The output of ADC module will applied to FPGA, and on FPGA we are generating a

verilog code for pulse with modulation. When the digital output of ADC module will

applied to FPGA we can see the output by using a Digital Storage Oscilloscope.

Means we are able to see the output of ADC module on oscilloscope using pulse

width modulation. Now we are thinking that we should explain something about

PWM.

41

Fig.5.6 Hardware Implementation of ADC Module

5.7. PULSE WIDTH MODULATION

Pulse width modulation (PWM) is a technique to provide a logic “1” and logic “0” for

a controlled period of time. It is a signal source involves the modulation of its duty

cycle to control the amount of power sent to a load. The following sections describe

the design of Pulse Width Modulation (PWM) on a Xilinx FPGA using hardware

description language (HDL). The PWM generates pulses on its output. The pulses are

made in such a way that the average value of highs and lows is proportional to the

PWM input. By filtering the pulses, we obtain an analog value proportional to the

PWM input. A PWM input can

Be of any width. Most common values are 8-bits and 16-bits. The PWM developed

can be used in many diverse and complex applications like robotics, motor and

motion control.

42

5.7.1. DIGITAL TECHNOLOGY FOR PWM

There are two approaches for implementing control systems using digital technology .

The first approach is based on software which implies a memory-processor

interaction. The memory holds the application program while the processor fetches,

decodes, and executes the program instructions. Programmable Logic Controllers

(PLCs), microcontrollers, microprocessors, Digital Signal Processors (DSPs), and

general purpose computers are tools for software implementation. On the other hand,

the second approach is based on hardware. Early hardware implementation is

achieved by magnetic relays extensively used in old industry automation systems. It

then became achievable by means of digital logic gates and Medium Scale Integration

(MSI) components[1]. When the system size and complexity increases, Application

Specific Integrated Circuits (ASICs) are utilized. The ASIC must be fabricated on a

manufacturing line, a process that takes several months, before it can be used or even

tested . FPGAs are configurable ICs and used to implement logic functions. Early

generations of FPGAs were most often used as glue logic which is the logic needed to

connect the major components of a system. They were often used in prototypes

because they could be programmed and inserted into a board in a few minutes, but

they did not always make it into the final product. Today’s high-end FPGAs can hold

several millions gates and have some significant advantages over ASICs. They ensure

ease of design, lower development costs, more product revenue, and the opportunity

to speed products to market. At the same time they are superior to software-based

controllers as they are more compact, power-efficient, while adding high speed

capabilities. The target FPGA device used in this project is Spartan-6 manufactured

recently by Xilinx. Digital controllers usually encompass input/output (I/O) modules

to communicate with users. Embedded systems typically consist of both application-

specific hardware and a general purpose microprocessor or microcontroller. Many of

the functions performed by the system can be implemented either on dedicated

hardware (for example, in an FPGA, in an ASIC, or in a special-purpose function

block added to the microcontroller itself) or can be implemented by the

microprocessor in software. The decision to implement a function in hardware or

software depends on trade-offs between the hardware/software implementations like

cost, speed, power consumption, design time, size (silicon area or program size), risk

43

and others. Partitioning functions efficiently between hardware and software can be

key to timely design of high performance, low cost digital systems. The speed of a

DC motor is approximately proportional to the supply voltage, so reducing the supply

voltage by half will reduce the speed by approximately one-half. The speed of the

motor can therefore be controlled by varying the average supply voltage. The supply

voltage could be changed using a variable supply voltage source, but this technique is

inefficient since voltage is controlled in these cases through a voltage drop across a

transistor. Since all current must go through the transistor and P=VI (the drop across

the transistor times the current), a significant amount of power is lost at the transistor.

A better way to control the motor is to switch the motor’s supply on and off very

quickly. If the on time is equal to the off time, the average voltage seen by the motor

will equal half the supply voltage and the motor will run at half the maximum speed.

As the on time increases compared with the off time, the average speed of the motor

will increase. The user should not notice the motor turning on and off, because it is

done very quickly. A pulse-width modulated (PWM) signal is a constant period

square wave with a varying duty cycle (on-time compared to off-time). In other

words, the frequency of a PWM signal is constant but the time the signal remains high

varies as shown in Figure which is below mentioned.. The duty cycle (percent ontime)

is given by τ/T.

Fig.5.7 PWM Generation With Different Duty Cycle

5.7.2. PWM TECHNIQUE

PWM can be used to reduce the total amount of power delivered to a load without

losses normally incurred when a power source is limited by resistive means. This is

because the average power delivered is proportional to the modulation duty cycle.

High frequency PWM Power control systems are realizable with semiconductor

switches. The discrete on/off states of the modulation are used to control the state of

44

the switch(es) which correspondingly control the voltage across or current through the

load .The major advantage of this system is the switches are either off and not

conducting any current, or on and have (ideally) no voltage drop across them. The

product of the current and the voltage at any given time defines the power dissipated

by the switch, thus (ideally) no power is dissipated by the switch .Realistically,

semiconductor switches such as MOSFETs or BJTs are non-ideal switches, but high

efficiency controllers can still built.

Fig.5.8 Different PWM Technique

A PWM signal can be used to switch the motor on and off as shown in figure below:

Fig.5.9 Speed Control of DC Motor Using PWM

45

The PWM signal is applied to the transistor pair, which acts as a switch. Whenever

the PWM signal is high, the switch is closed and the entire supply voltage is applied

across the motor terminals. When the PWM signal is low, the switch is open and the

supply voltage across the motor is 0 volts. If we apply a PWM signal with a 50 %

duty cycle then the average voltage across the motor is 50%. It does not take much

work to show that the average voltage across the motor is given by:

Vmotor, average = Vsupply*duty cycle

And, therefore, motor speed = (motor speed when driven by Vsupply) * duty cycle. A

PWM signal can be generated in a number of ways. One using software implemented

by the microcontroller and the other using hardware implemented in an FPGA. A

software implementation may also be lower risk than a hardware implementation

because it may be changed at the last minute by changing the program in memory

(changing the program, though, also increases the risk of undetected software bugs).

However, the capability of a microcontroller to perform these tasks may be limited.

The microprocessor may have to perform several tasks in addition to implementing

the PWM and thus may not be able to implement the PWM fast enough or with

enough accuracy. Implementing the PWM in hardware frees up the microprocessor

for these other tasks and ensures that the task is performed quickly and accurately.

The best choice - hardware or software - depends significantly on the application.

Advantages of PWM:

Low power, noise-free, low cost features

High efficiency

Flexibility in control

Light weight

Quick response

46

5.7.3. PWM PULSES AT DIFFERENT DUTY CYCLE

Fig.5.10 PWM Output At 30% Duty Cycle

Fig.5.11 PWM Output at 50% Duty Cycle

47

Fig.5.12 PWM Output at 40% Duty Cycle

5.8. HARDWARE IMPLEMENTATION OF BOOST CONVERTER

The DC-to-DC converter is implemented as shown in Fig.

Fig.5.13 Hardware Implementation of Boost Converter Circuit

48

In the hardware part, the circuit is designed to step up DC-to-DC voltage. The circuit

included parts of Boost components such as controllable switch (IRF840), inductor

and capacitor, ADC 0804, MIC 4428 MOSFET Driver and other basic components.

In order to maintain output voltage, controller will be operated in feedback circuit.

When the duty cycle is in ON state, diode become as reversed biased and the inductor

will deliver current and switch conducts inductor current. The current through the

inductor increase, as the source voltage would be greater.

The simulation was first run with the switch on no MPPT mode, bypassing the

MPPT algorithm block in the circuit. It was seen that when we do not use an MPPT

algorithm, the power obtained at the load side was around 8 Watts. It must be

noted that the PV panel generated around 37 Watts power for this level of solar

irradiation. Therefore, the conversion efficiency came out to be very low.

The simulation was then run with the switch on MPPT mode. This included the

MPPT block in the circuit a n d duty cycle D as calculated by the P&O algorithm.

Under the same irradiation conditions, the PV panel continued to generate around

36 Watts power. Thus increasing the conversion efficiency of the photovoltaic

system as a whole.

The loss of power from the available 37 Watts generated by the PV

panel can be explained by switching losses in the high frequency PWM

switching circuit and the inductive and capacitive losses in the Boost

49

Converter circuit.

Therefore, it was seen that using the Perturb & Observe MPPT technique

increased the efficiency of the photovoltaic system by approximately 60%

from an earlier output power.

5.9. COMPLETE CIRCUIT MODEL

Fig.5.14 Complete Circuit Model of Hardware Implementation

50

5.10. COMPLETE HARDWARE SETUP

Fig.5.15 Complete Hardware Setup Without PV Panel

51

CHAPTER 6

RESULT

SIMULATION RESULT FOR 37 WATT SOLAR PANEL

Fig.6.1 P-V Characteristic of Solar Panel

Fig.6.2 I-V Characteristic of Solar Cell

52

CASE I. Output Power at Constant Irradiation (1000W/m2)

Fig.6.3 Output Power of 37W PV Module at Fixed Irradiation (1000W/m2)

Above graph shows that when we are using MPPT and taking irradiance is constant

then the power become increased.

CASE II. With Varying Irradiation

Fig.6.4 Change in Irradiation

0 2 4 6 8 10 12 14

x 104

0

5

10

15

20

25

30

35

40

Time

Outp

ut

Pow

er

Output Power with MPPT

Output Power without MPPT

Actual Power

0 2 4 6 8 10 12 14

x 104

700

750

800

850

900

950

1000

Time

Irra

dia

tion(W

/m2)

53

Fig.6.5 Output Power of 37W PV Module With Change In Irradiation

Table 6.1 Observation with and without MPPT with respect to time

Time

Without MPPT

With MPPT

Voltage(V)

Current(A)

Power(W)

Voltage(V)

Current(A)

Power

(W)

2:15

pm

16.36

0.093

1.521

27

0.153

4.131

2:17

pm

17.04

0.094

1.601

28.8

0.154

4.435

3:05

pm

16.5

0.091

1.501

32

0.176

5.632

3:27

pm

16.93

0.093

1.574

29

0.157

4.553

3:52

pm

16.56

0.118

1.954

23.1

0.163

3.765

4:00

pm

15.8

0.119

1.880

23.6

0.168

3.964

4:05

pm

15.9

0.115

1.828

24.2

0.169

4.089

0 2 4 6 8 10 12 14

x 104

0

5

10

15

20

25

30

35

40

Time (usec)

Outp

ut

Pow

er

(Watt

)

Output Power with MPPT

Output Power without MPPT

Actual output Power

54

4:10

pm

15.7

0.116

1.821

23.7

0.164

3.886

4:15

pm

15.3

0.095

1.453

23.5

0.169

3.971

4:22

pm

15.1

0.112

1.691

23.9

0.170

4.063

Total power = 16.824

Total power = 42.489

Efficiency = Total Power (with MPPT) – Total Power (without MPPT) * 100

Total Power (with MPPT)

= 42.489– 16.824 *100 42.489

= 60.40% Above table shows the change in performance of PV module by using MPPT. It

shows that efficiency is increased up to 60% by using MPPT.

55

CHAPTER 7

CONCLUSION

The model shown in above Figure was simulated using SIMULINK and MATLAB.

The plots obtained in the different scopes have been shown in Chapter 6. The

simulation was first run with the switch on no MPPT mode, bypassing the MPPT

algorithm block in the circuit. It was seen that when we do not use an MPPT

algorithm, the power obtained at the load side was more fluctuating for a solar

irradiation value of 1000 Watts per sq. cm. Therefore, the conversion efficiency came

out to be very low. The simulation was then run with the switch on MPPT mode. This

included the MPPT block in the circuit and the PI controller was fed the Vref as

calculated by the P&O algorithm. Under the same irradiation conditions, the PV panel

continued to generate around 36.8 Watts power. In this case, however, the power

obtained at the load side was found to be around 36.8 Watts, thus increasing the

conversion efficiency of the photovoltaic system as a whole. The loss of power from

the available 36.8 Watts generated by the PV panel can be explained by switching

losses in the high frequency PWM switching circuit and the inductive and capacitive

losses in the Boost Converter circuit.

Therefore, it was seen that using the Perturb & Observe MPPT technique increased

the efficiency of the photovoltaic system. And the obtained output power is 37.8watt.

56

REFERNCES

[1] N. Pandiarajan and Ranganath Muth” Mathematical Modeling of Photovoltaic

Module with Simulink” in 2011 1st International Conference on Electrical Energy

Systems.

[2] Alpesh P. Parekh, Bhavarty N. Vaidya and Chirag T. Patel”Modeling and

Simulation Based Approach of Photovoltaic System” in Global Research Analysis

Volume 2 Issue 4 April 2013 • ISSN No 2277 – 8160.

[3] Pandiarajan N, Ramaprabha R and Ranganath Muthu” Application Of Circuit

Model For Photovoltaic Energy Conversion System”.

[4] G. Venkateswarlu and Dr.P.Sangameswar Raju” Simscape Model Of Photovoltaic

Cell” in International Journal of Advanced Research in Electrical, Electronics and

Instrumentation Engineering Vol. 2, Issue 5, May 2013.

[5] Vandana Khanna, Bijoy Kishore Das, Dinesh Bisht

“MATLAB/SIMELECTRONICS Models Based Study of Solar Cells” in

INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH Vandana

Khanna et al., Vol.3, No.1, 2013.

[6] Mihnea Rosu-Hamzescu, Sergiu Opera” Practical Guide to Implementing Solar

Panel MPPT Algorithms”.

[7] P.Sathya, Dr.R.Natarajan” Design and Implementation of 12V/24V Closed loop

Boost Converter for Solar Powered LED Lighting System “ in International Journal

of Engineering and Technology (IJET) Volumeg No 1 Feb-Mar 2013.

[A]http://en.wikipedia.org/wiki/Photovoltaics

[B]http://en.wikipedia.org/wiki/Microcrystalline_silicon

[C]http://en.wikipedia.org/wiki/Cadmium_telluride

[D]http://en.wikipedia.org/wiki/Copper_indium_selenide

[E]Nielsen, R. 2005, 'Solar Radiation', http://home.iprimus.com.au/nielsens/.

57

[E]http://express.howstuffworks.com/gif/solar-power-diagram.jpg

[F]http://science.howstuffworks.com/solar-cell1.htm

[G]http://www.pvresources.com/en/module.php

[H]http://www.japanfs.org/ja/files/Stand-Alone_PV_Modules.jpg

[I] http://www.autospectator.com/cars/files/images/Photovoltaic-Cell.jpg

[J] http://en.wikipedia.org/wiki/Photovoltaic_array

[K]http://www.iaei.org/magazine/wpcontent/uploads/2009/05/07ewiles_ph4_1679793

01.jpg

[L]http://www.ncsc.ncsu.edu/include/_upload/media/pubs/PVApplication.pdf

[M] http://science.nasa.gov/science-news/science-at-nasa/2002/solarcells

58

APPENDICES

COMPONENTS QUANTITY

LED 8

PUSH BUTTOM 2

PCB 2

SOLDERING ROD 1

RESISTANCE 180E 8(1/4 W)

RESISTANCE 1OKE 2(1/4 W)

RESISTANCE 1KE 2(1/4 W)

RESISTANCE 180E 2(1/2 W)

CAPACITOR 47 PF 3

CAPACITOR 47 µF 2

CAPACITOR 1 µF 2

CAPACITOR 4.7 µF 1

POTENTIOMETER(5K) 1

MOSFET IRF 840 1

MIC 4428 1

7805 (5V) 1

7812 (12V) 1

12V/7.2 AH BATTERY 1

ADC 0804 1

HEATSINK 1

LOAD (HB LED STRIP)484 1

POWER DOIDE 1

INDUCTOR 3

IC SOCKET 2

59

PUBLISHED PAPER

Gaurav Chitransh, Gaurav Kumar, Wasim Akhtar, Arpit Saxena, Swati Singh “Effect

of Load Mismatching On Active Solar Technique PV Module Using

Matlab/Simulink” in IJARSE/Volume 02/Issue 09 / September 2013.

Link: http://www.ijarse.com/pastissue.php (Volume No.02, Issue No. 09, September

2013