PERFORMANCE ANALYSIS OF PHOTOVOLTAIC SYSTEM

USING DIFFERENT HEURISTIC MODELS

A Thesis Submitted to the Department of Mechanical Engineering, Dhaka

University of Engineering and Technology in partial fulfillment of the

requirements for the degree of

M. Sc. in Mechanical Engineering

By

Shameem Ahmed

Student ID: 142353-P

Department of Mechanical Engineering

Dhaka University of Engineering and Technology

Gazipur-1707, Bangladesh

May, 2018

CERTIFICATE

This is to certify that the thesis entitled “Performance Analysis of Photovoltaic System Using

Different Heuristic Models”, submitted by Shameem Ahmed, in partial fulfillments for the

requirements of M. Sc. degree in Mechanical Engineering at Dhaka University of

Engineering and Technology, Gazipur-1707, Bangladesh in May, 2018

Approved by:

Professor Dr. Mohammad Asaduzzaman Chowdhury Supervisor

Department of Mechanical Engineering

Dhaka University of Engineering and Technology,

Gazipur – 1707, Bangladesh

Date: 15th May, 2018

Professor Dr. Md. Emdadul Hoque External Examiner

Department of Mechanical Engineering

Rajshahi University of Engineering and Technology,

Rajshahi, Bangladesh

Date: 15th May, 2018

CANDIDATE'S DECLARATION

It is hereby declared that this thesis or any part of it has not been submitted elsewhere for the

award of any degree or diploma.

Signature of the Candidate

Shameem Ahmed Date: 15th May, 2018

Student ID. No: 142353-P

DEDICATION

To My Parents,

Late Md. Mohiuddin and Azizun Nessa

My wife Farhad Jarin Zaman

And My Children

Wasif Ahmed

And

Alveena Sabahat

ACKNOWLEDGEMENTS

I would first like to thank my thesis Supervisor Dr. Mohammad Asaduzzaman Chowdhury,

Professor & Head, Department of Industrial Production Engineering, Dhaka University of

Engineering and Technology, Gazipur – 1707, Bangladesh for providing me with all the

straightforward support during every steps of the study without which this thesis would not have

been possible. He consistently allowed this paper to be my own work, but steered me in the right

direction whenever he thought I needed it.

I would like to acknowledge and thank Professor Dr. Mohammad Zoynal Abedin, Dean, Faculty

of Mechanical Engineering and my thesis Co- Supervisor Professor Dr. Md. Arefin Kowser,

Department of Mechanical Engineering, Dhaka University of Engineering and Technology,

Gazipur – 1707, Bangladesh for providing me with all the support during the study. Without

their passionate reading of the manuscript, participation and very valuable comments on this

thesis, the thesis paper could not have been successfully published.

I would also like to thank Assistant Professor Mr. A.N.M. Mominul Islam Mukut as the first

reader of this thesis, and I am gratefully acknowledge his valuable comments on this thesis that

guided me in the right direction to make this thesis paper a presentable one.

Furthermore, I would like to thank Mr. Md. Bengir Ahmed, Student, Department of Industrial

Production Engineering, Dhaka University of Engineering and Technology, Gazipur for his

support and assistance by his whole hearted support in collecting the related data and documents

during my study by providing me with helpful insights into the Bangladeshi Solar sector and

forthright sharing of his personal opinions and experiences.

A warm thank goes to Mr. Shaim Mahmud, Lecturer, Department of Industrial Production

Engineering, Dhaka University of Engineering and Technology, Gazipur for his support during

the experiment.

Finally yet importantly, I like to thank my Drivers Mr. Hashem, Mr. Enamul, Mr.Shahidul and

Mr. Shahadat who drove me all the way from Dhaka to Gazipur with utmost patience.

Above all, I am indebted to my wife Farhad Jarin Zaman, my son Wasif Ahmed and daughter

Alveena Sabahat for their deep concern, encouragement and blessings during this research. They

provided me with unfailing support and continuous encouragement throughout my years of study

and through the process of researching and writing this thesis. This accomplishment would not

have been possible without them.

Shameem Ahmed

vii

ABSTRACT

This study deals with the performance analysis of 250 KWp grid-connected solar power plant

under different tilt angles in various regions of Bangladesh. The research has been done with a

practical project of Bangladesh Army aimed to Mitigate Carbon Emission and Development of

Renewable Energy.

This study presents a solar irradiation predict model based on fuzzy logic and artificial neural

networks which aims to achieve a good accuracy at different weather conditions. The accuracy

of predicted solar irradiation will affect the power output forecast of grid-connected photovoltaic

systems which is important for power system operation and planning. Used data are taken from

NASA. The performance of ANN is better than fuzzy logic model by comparing RMSE, R2 and

percentage of accuracy. These fuzzy logic and ANN models can be used to forecast solar

irradiation of Dhaka city in different environment condition and to provide enough information

about the feasibility of solar power projects.

To extract the maximum power from solar panels, sunlight should fall with steep angle on the

panels. Hence, the tilt angles should not be fixed and changed seasonally to derive maximum

output from the solar panels. Therefore optimum fixed tilt angles of PV panels should be

changed monthly and seasonally. In the mathematical analysis of the study, the monthly,

seasonal and the annual optimum fixed tilt angles of PV panels depending on solar angles are

calculated for 7 cities or Locations. The tilt angles of solar panels exposed to sunlight in different

seasons and time should not be fixed. The results show that it is better to be changed the tilt

angles 4 times a year. But to avoid the hassle of changing and cost related to the procedure of

changing, it must be changed minimum twice a year.

viii

TABLE OF CONTENTS

Page

TABLE OF CONTENTS i

LIST OF TABLES ii

LIST OF FIGURES iv

NOMENCLATURE vi

ACKNOWLEDGEMENT vii

ABSTRACT viii

CHAPTER 1 Introduction

1.1 Research Background 1

1.2 Existing Solar systems of Bangladesh Army 3

1.3 A brief study of the solar plants 5

1.3.1 ON-Grid Concept 5

1.3.2 Schematic view 5

1.3.3 Environment benefits of the projects 6

1.4 The detailed explanation of the proposed project's adaptation or

mitigation measures to address the root causes and barrier of the

climate change 6

1.5 The impact of the proposed project implementation 6

1.6 The taken adaptations and measures for the sustainability of the

activities after completion of the project 7

1.7 The benefit of the proposed project implementation 7

1.8 Objectives of this works 9

CHAPTER 2 Literature Survey

2.1 Present Scenario of Energy Sector in Bangladesh 10

2.2 Prospects of Solar Energy in Bangladesh 12

2.3 Installed Solar Energy in Bangladesh 14

Ongoing Projects of BPDB 17

2.4 Photovoltaic (PV) overview 17

2.4.1 Photovoltaic effect 17

2.4.2 Photovoltaic characteristics 18

2.4.3 Photovoltaic module technologies 22

2.5 Meteorological Study 27

2.6 Literature Review 29

CHAPTER 3 Mathematical Model

3.1 Performance Characterization 31

i

3.2 Solar Angles 32

CHAPTER 4 Expected Output of Exiting Solar Panel of Bangladesh Army

4.1 Theoretical efficiency of solar panel 39

4.2 Expected performance of the solar panels after sun simulator test 40

CHAPTER 5 Study of Hour Angles and tilt Angles

5.1 Effects of hour angles and tilt angles 44

5.1.1 Effects of hour angles 44

5.1.2 Estimated tilt angles 48

CHAPTER 6 Analysis of Fuzzy Logic and ANN for Predicting Irradiation

6.1 Fuzzy logic Background 51

6.1.1 Fuzzy logic 51

6.1.1 Fuzzy logic rules 53

6.1.2 Defuzzification 54

6.2 Artificial Neural Network 63

CHAPTER 7 Results and Discussion

7.1 Performance analysis of 80 KWp PV solar plant 68

7.2 Performance analysis of 30KWp PV solar plant 72

7.2 Performance analysis of 6 plants in 2016 73

7.3 Performance analysis of 6 plants in 2017 79

CHAPTER 8 Conclusion and Recommendation 84

REFERENCE 85

LIST OF FIGURES

Figure No. Description Page

Figure 1.1 Study area of the PV system plant 4

Figure 1.2 Schematic View of PV solar plant 5

Figure 2.1 Domestic Generation of Electricity by Fuel Type 11

Figure 2.2 Domestic generation of electricity projection 2021 11

Figure 2.3 The amount of hours of sunlight in Bangladesh 14

Figure 2.4 A diagram showing the photovoltaic effect 18

Figure 2.5 Photovoltaic systems applications 19

Figure 2.6 Effects of the incident irradiation on module voltage and current 20

Figure 2.7 Effect of ambient temperature on module voltage and current 21

Figure 2.8 Fill Factor 21

Figure 2.9 PV module layers 23

Figure 2.10 Monocrystalline PV module and cell layered structure 24

Figure 2.11 Polycrystalline cell and module 24

Figure 2.12 Comparison of cell thickness, material consumption and energy

expenditure for thin-film cells (left) and crystalline silicon cells

(right) 26

Figure 2.13 Land surface temperature map of Dhaka City 28

Figure 3.1 Declination angle 33

Figure 3.2 The Basic solar angle 36

Figure 3.3 Incidence and tilt angle 38

Figure 4.1 Sun simulator testing lab 41

Figure 4.2 Interface of the sun simulator 42

Figure 4.3 Output interface of the sun simulator 42

Figure 5.1 Experimental investigation of solar panel 44

Figure 5.2 Energy generated by varying hour angle of sun 45

Figure 5.3 Energy generation for fixed angles and changing angles 47

Figure 5.4 Comparison graph of theoretical Rb and actual Rb 47

Figure 6.1 Input variable Gaussian membership functions for temperature 56

Figure 6.2 Input variable Gaussian membership functions for wind speed 56

Figure 6.3 Input variable Gaussian membership functions for humidity 56

iv

Figure 6.4 Output triangular membership function for A tilt angles and B

irradiation

57

Figure 6.5 Fuzzy rules interface 58

Figure 6.6 Surface plot of predicted tilt angle (degree) values by fuzzy logic

in relation to parameters change: A temperature and wind speed,

B temperature and humidity and C Humidity and wind speed 59

Figure 6.7

Surface plot of predicted irradiation values by fuzzy logic in

relation to parameters change: A temperature and wind speed, B

temperature and humidity and C Humidity and wind speed 60

Figure 6.8 Typical structure of an MLP network 63

Figure 6.9 Performance regression plot for predictive model 67

Figure 6.10 Comparison graph of ANN and Fuzzy logic with actual

irradiation 67

Figure 7.1 Monthly generation of 80KWp solar plant 71

Figure 7.2 Monthly specific yield of 80KWp plant 71

Figure 7.3 Monthly performance ratio (PR) of the 80KWp plant 79

Figure 7.4 Monthly energy generation of 33KWp PV system 74

Figure 7.5 Monthly specific yield of 30KWp PV system 74

Figure 7.6 Performance ratio of 30KWp PV system 75

Figure 7.7 Monthly energy generation (KWh) of 6 plants in 2016 77

Figure 7.8 Monthly specific yield of six plants in 2016 77

Figure 7.9 Monthly performance ratio of six plant in 2016 79

Figure 7.10 Monthly energy generation (kwh) of 6 plants in 2017 80

Figure 7.11 Monthly specific yield of six plants in 2017 80

Figure 7.12 Monthly performance ratio of six plant in 2017 82

v

iii

LIST OF TABLES

Table No. Description Page

Table 1.1 Geographical locations of Solar Plants 4

Table 1.2 Benefit of the PV based solar project 8

Table 2.1 Major solar PV systems implemented by BPDB 15

Table 2.2 Solar energy and surface meteorology of Dhaka 29

Table 3.1 Declination angles 34

Table 3.2 Hour angles 35

Table 4.1 STC performance of the solar panel 39

Table 4.2 PV module and inverter specification 40

Table 4.3 Test data of sun simulator 43

Table 5.1 Energy generation per square meter collected by panels at different

tilt angles 46

Table 5.2 Tilt angle for each month of a year 48

Table 5.3 Tilt angle for a year of each plant 49

Table 5.4 Tilt angle for 2 times a year for each plant 49

Table 5.5 Tilt angle for 4 times a year 50

Table 6.1 Fuzzy linguistic variables and parameters 53

Table 6.2 Meteorological data for fuzzy logic 54

Table 6.3 Rules used for fuzzy logic model 55

Table 6.4 Fuzzy logic model error and accuracy for tilt angles 61

Table 6.5 Fuzzy logic model error and accuracy for irradiation 62

Table 6.6 Performance of the fuzzy logic model 62

Table 6.7 Meteorological data for ANN 65

Table 6.8 ANN model error and accuracy for irradiation 68

Table 6.9 Performance of the ANN model 66

Table 7.1 Monthly energy generation and performance indicators of

80KWp plant 70

Table 7.2 Monthly energy generation and performance indicators of

30KWp plant 73

Table 7.3 Monthly energy generation of 6 plants in 2016 76

Table 7.4 Average energy generation of 6 plants in 2016 76

Table 7.5 Monthly performance ratio of six plants in 2016 78

Table 7.6 Average performance ratio of six plants in 2016 78

Table 7.7 Monthly energy generation of 6 plants in 2017 79

Table 7.8 Average energy generation (kwh) of 6 plants in 2017 79

Table 7.9 Monthly performance ratio of six plants in 2017 81

Table 7.10 Average performance ratio of six plants in 2017 81

Table 7.11 Performance summary of some selected grid connected PV

systems. 83

NOMENCLATURE

PV Photovoltaic Cell

SDG Sustainable Development Goal

HSD High Speed Diesel

FO Furnace Oil

SPS Solar Power System

EME Electrical and Mechanical Engineers

GHG Green House Gas

ANN Artificial Neural Network

GTC Green Technology Center

SHS Solar Home System

CHT Chattogram Hill Tracts

RE Renewable Energy

SIP Solar Irrigation Pump

PR Performance Ratio

SY Specific Yield

Incidence solar irradiation

Efficiency of the PV modules.

Ø Latitude

λ Longitude

δ Declination angle

ω Hour angle

θz Zenith angle

α Solar elevation angle

γs Solar azimuth angle

β Tilt Angle

STC Standard Test Condition

vi

1

CHAPTER 1

Introduction

1.1 Research Background

Life without a sustainable supply of energy is almost unimaginable. The importance of

energy is even more supplementary in the context of developing countries, which have

traditionally experienced prolonged periods of energy crises. For instance, use of traditional

indigenous energy resources in Bangladesh has proven to be inadequate in ensuring energy

sufficiency across the nation. As a result, the country's growth prospects are being hampered.

Moreover, the nation's vast dependence on imported fuel has also attributed to an unnecessary

fiscal burden, exerting multidimensional pressures on its economic development drives.

Furthermore, in the past there was a global trend of being heavily dependent on the use of fossil

fuels and non-renewable energy resources which not only minimized their reserves but also caused

environmental degradation. As a result, the utmost significance of ensuring the availability of

green and affordable energy across the world has been deeply acknowledged through the

enlistment of energy as the seventh Sustainable Development Goal (SDG) of the United Nations.

Electricity is the main form of energy that is tapped on both private and commercial scales

in Bangladesh. However, following the oil price shocks in the 1970s, the government decided to

employ natural gas in the production of electricity. However, recent shortages of natural gas have

compelled the nation to resort to the use of imported fuels. It is worth mentioning that the primary

energy resources and power generation capacity and efficiency are limited in Bangladesh, which

obliges it to rely significantly on expensive oil-based power generation in order to avoid major

power cuts. Moreover, it has been estimated that at the current rate of natural gas employment and

provided no new natural gas fields are discovered any time soon, the country is likely to run out

of its natural gas reserves by 2031 [1]. Given the ominous concerns, the use of imported High

Speed Diesel (HSD) and Furnace Oil (FO) has risen alarmingly which, although added electricity

to the national grid, actually meant that the government's public expenditure budget was

inefficiently allocated to pay the corresponding import bills. This had probably crowded out the

nation's potential investment in other productive sectors creating adverse economic impacts. Thus,

2

it is crucial for Bangladesh to prepare itself for the near future and plan its fuel diversification

strategies keeping in line with the trends in the global energy markets.

Socio-economic development of any country depends on per capita consumption of energy.

On the other hand increased consumption of energy has direct effect on environment which in turn

affects the economic development. Energy is available in different forms in the universe. These

various forms of energy are related and can be transformed from one form to another. Hence,

energy cannot be destroyed, it can only be converted to other forms only. Presently, fossil fuels

are used to generate electrical energy in most of the countries of the world. But this form of energy

is expensive, exhaustive, non-renewable and day by day decreasing. As a part of fuel

diversification drive, Bangladesh can look forward to replacing fossil fuel and non-renewable

energy with renewables in order to match its local energy demand. Hence, various other forms of

renewable energy gained popularity, solar energy is one of those renewable energy that are

available in abundant in the universe. Electricity generated from solar power is relatively cost

effective compared to imported oil-based electricity, which makes it a go to option in the near

future. Solar energy is believed to be the most efficient and sustainable source of energy with

absolutely no contribution to environmental degradation.

In the present era, solar energy is one of the most promising renewable resources that can

be used to produce electric energy through photovoltaic (PV) process. A significant advantage of

PV systems is the use of the abundant and free energy from the sun. However, these systems still

face major obstacles that hinder their widespread use due to their high cost and low efficiency

when compared with other renewable technologies. Moreover, the intermittent nature of the output

power of PV systems reduces their reliability in providing continuous power to customers [2]. In

addition, the fluctuations in the output power due to variations in irradiance might lead to

undesirable performance of the electric network. The support of governments, electric utilities,

researchers and consumers is the key to overcoming the aforementioned obstacles and enhancing

the maturity of the technology in this field.

Many characteristics make solar energy technology unique and different from other kinds

of renewable energy. Due to no emissions being released, it is considered an environmentally

friendly. Because the sun comes up every day it is considered a secure source of energy. Solar

energy harvesting systems can easily scale to varied sizes and there exists many ideas for

3

implementation: such as panels, roof tiles, and paints. Flexibility in mounting and the small amount

of maintenance required for solar panels make them attractive. In addition, solar energy systems

can be easily implemented in remote areas without the need for long power lines. The fact that

solar panels can directly deliver electrical energy is advantageous, as the need for an electric

generator is removed. Vast amounts of solar energy are available on the surface of the earth with

all locations receiving at least some sun. Even though solar panels are still a little expensive, cost

has been dropping exponentially and continues to drop.

Testing and modelling the PV module/system in the outdoor environment with specifying

the influences of all significant factors, are very important to check the system performance and

to facilitate efficient troubleshooting for photovoltaic module/system through considering hourly,

daily and monthly or annual basis [3].

Photovoltaic output power depends on many factors; such as sun position, weather

conditions, module temperature, thermal characteristics, module material composition and

mounting structure [4].

Real time power generation should be investigated precisely for grid performance, because

a high penetration of PV production could create instability in the grid [5]. The uncertainty of the

photovoltaic performance models is still too high; the early existing PV performance models

mainly deal with the ideal PV module characteristics rather than the dynamic situation under the

surrounding conditions [5].

In this study, performance of the 250 KWp PV system solar power plants were

investigated.

1.2 Existing Solar systems of Bangladesh Army

Bangladesh Army established a 250 KWp solar power plant project in 8 locations of the

country. Since the power plant in Ghatail area is inactive due to some renovation of the existing

building, therefore 7 locations were considered for this study excluding Ghatail cantonment power

plant. Table 1.1 shows the value of latitude and longitude of these locations. Figure 1.1 and 1.2

shows the graphical representation of these locations. Latitude and longitude of these location were

identified by google map [6].

4

Table 1.1 Geographical locations of Solar Plants

Geolocation of Solar Plants Symbol Capacity

(KWp)

Latitude, Ø

(NORTH)

Longitude, λ

(EAST)

Dhaka Army Head Quarters AHQ 80 23.815755 90.412709

Dhaka Armed Forces

Division

AFD 30 23.815755 90.412709

Chittagong Cantonment HQ-24Div 20 22.410922 91.815332

Jessore Cantonment HQ-55Div 20 23.171072 89.189665

Comilla Cantonment HQ-33Div 20 23.470972 91.129895

Savar Cantonment HQ-9Div 20 23.919861 90.283982

Ghatail Cantonment HQ-19Div 20 24.492668 89.997344

Bogra Cantonment HQ-11Div 20 24.760676 89.394486

Rangpur Cantonment HQ-66Div 20 25.764876 89.230823

One of the study area of PV type solar power plant are shown in Figure 1.3. This

picture was collected from Google satellite.

Figure 1.1 Study area of the PV system plant (Google Map) [6].

5

The amount of energy output by a solar panel depends on the solar radiation collected by

that panel. In order to capture the most no-atmosphere, radiant energy a solar panel should

continually have its normal surface vector along the same line as the beam radiation from the sun

[7].

1.3 A brief study of the solar plants

1.3.1 On-grid Concept

i. Eliminates the use of environmental hazardous.

ii. Eliminates the hassle of periodic maintenance and replacement cost of battery.

iii. Reduces the initial installation cost by 40% (approximately) and.

iv. Make optimum use of solar during day time, mainly in the Office block where requirement

of electricity is minimum at night.

v. On-Grid solar power system is the trends of the world especially for urban areas.

vi. No need of separate wiring.

1.3.2 Schematic view

The schematic view of the connection system of one of the PV type power plants is referred

below. Since the other plants are of similar design therefore only one view is shown for

understanding.

Figure 1.2 Schematic view of PV solar plant [2]

6

1.3.3 Environment benefits of the projects

i. Non-polluting, cost free renewable energy.

ii. The system will off-set carbon footprint by 135 tons/yr and about 3375 tons in life cycle

iii. Reducing emission of 3375 tons CO2 which is equal to effectively planting around 3000

acres of forest in Bangladesh.

1.4 Proposed project's adaptation or mitigation measures to address

the root causes

Most of the power plant in Bangladesh is using fossil fuel to generate electricity. It generates huge

amount of carbon-di-oxide and other greenhouses gases as by product which ultimately imbalance

the natural stability and bring disaster. Natural resources like solar energy can be used for

generating electricity in order to reduce carbon-di-oxide and other greenhouse gases which

ultimately effect on the natural stability. Besides, presently gas generated power plant are facing

acute shortage of gas and also huge amount of diesel is imported every year for generating

electricity. Now country faces severe electric power crisis.

i. To reduce dependency on the artificial resources and also to address reduction of carbon-

di-oxide and other greenhouse gases we need investment to develop many solar power

generation plants (renewable resource). So the solar PV plant will be appropriate solution

for present power crisis.

ii. Mitigation Measure:

1. To set up solar PV plant for supplying electricity for running electric appliances.

2. To introduce silent, eco-friendly renewable solar energy based power plant.

3. To reduce pressure or dependency on the national electric grid.

1.5 The impact of the proposed project implementation

The present fossil fuel based power plant is not sustainable for a growing demand over a

long period due to limited supply of fossil fuel but solar powered plant generates sustainable green

power. So, environment will not be affected through emission of carbon-di-oxide and greenhouse

gases. Light, fan and other electric appliances at Army Headquarters and other Formation

Headquarters will be run by Solar Powered Plant generated electricity. It will reduce the pressure

7

on the national power grid as well as it will reduce the dependency on national resources. It will

also have influence in poverty alleviation.

1.6 Adaptations and measures for the sustainability of the project.

Total 250 KWp solar power plant will be installed to generate 250 kW load power in two

phases. The sustainability of the project will be ensured by the developed expertise of the

organization on solar power system. All the 17 goals of Sustainable Development Goals (SDG) of

Bangladesh emphasized the necessity of developing energy sector of the country keeping the mind

about environmental sustainability [8]. The proposed project will offer electricity access to people

of remote location and at the same time will keep the environment free from the emission of GHGs.

This project directly supports the following Goal of SDG:

Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

1.7 The benefit of the proposed project implementation

i. On Environment

Remote Army Camps of CHT mainly depends on fossil fuel based generator as a source of

electricity. Burning of fossil fuels causes environmental degradation through emission of

carbon-di-oxide and other greenhouse gases. On the other hand SPS uses renewable solar

energy which is sustainable, free from pollution and lead to independency from fossil fuel

based generator

ii. On Climate Change

As SPS does not emit any GHGs, hence it acts as mitigation measures to address the root

cause of climate change

iii. On Institution & Productivity

Electricity generated from SPS is free. Availability of electricity from SPS will help the locals

to do income generating activities. Hence, the project will increase the productivity of the remote

areas

8

iv. In Alleviating Poverty

Electricity supply from Solar Power System (SPS) will expedite the development activities of

Government in the remote area of Chittagong Hill Tracts. Thus the project will influence in

alleviating the poverty of that area.

v. Influence on the Women & Children's Welfare

Availability of electricity at remote areas will help the local women to do domestic works and

some other income generating activities. The project will help in the education and amusement

aspect for the children of the remote areas. Table 1.2 shows benefit of the PV based solar projects.

Table 1.2 Benefit of the PV based solar project

Size

of

SPP

Electricity Generation Cost of

Electricity

CO2 Emission

Control REC HEB

Total

Savings

Max

KW

Per

Hou

r

Max

KW

h P

er D

ay

Max

KW

h P

er Y

ear

Yea

rly

Aver

age

kW

h

Yea

rly

Aver

age

(BD

T)

Ton/y

ear

BD

T/y

ear

BD

T/Y

ear

BD

T/Y

ear

Per

Yea

r(B

DT

)

250

KWp 250 1500 547500 438000 8760000 274 766500 438000 547500 10512000

a. Savings of Electricity : 430 - 540 MWh/Year

b. Savings of CO2 Emission : 274 Ton/Year

c. Benefits in terms of Money : BDT 105 Lac /Year (Minimum)

d. Payback Period : 11Years (without considering social benefits, increase of

electricity tariff, inflation rate etc)

9

Benefits obtained till the reporting date

a. Grid Electricity Savings : 95.21 MWh

b. Electricity Cost Savings : 12.63 Lac BDT

c. GHG Emission Control : 47.6 Tons [2]

d. REC Value : 1.30 Lac BDT

e. Total Benefits in Money : 13.93 Lac BDT

Intangible Health & Environment Benefit has not been considered in this list.

1.8 Objectives of this research work

i. To investigate the solar irradiation using fuzzy logic and ANN.

ii. To determine the different solar parameters which affect the solar cell efficiency.

iii. To optimize the results and percentage of contribution of different solar parameters for

improving solar plant efficiency.

iv. The results of this study are compared with results available in the literature.

10

CHAPTER 2

Literature Review

2.1 Present scenario of energy sector in Bangladesh

In Bangladesh power sector showed a rapid growth in the recent years, with currently

providing 80 percent of the country's total population effective access to electricity [9]. The latest

data of Power Division of the Ministry of Power, Energy and Mineral Resources showed that the

number of electricity customers rose to 2.49 crore in 2017 from 1.08 crore in 2009. Nearly 45 lakh

households in remote areas across the country also got solar power system in the past few years

[10]. The 2.49 crore customers together with the 45 lakh home solar systems effectively made

electricity available to 80 percent of the country’s total population. The electricity generation

capacity rose this year to 15,379 megawatts (MW) from 4,942 MW in 2009 when the per capita

power generation surged to 407 KWh (Kilowatt-Hour) from 220 KWh. The distribution lines also

expanded to 1.41 lakh kilometers. The government of Bangladesh set a target of generating 24,000

MW power by 2021 [10]. To achieve this goal, 34 power plants would come into operation from

2017 to 2021, adding 11,363 MW to the national grid [10]. In 2016, total installed capacity of fuel

type power plants are shown in Figure 2.1 and domestic generation of electricity projection 2021

is shown in Figure 2.2.

11

Figure 2.1 Domestic Generation of Electricity by Fuel Type [10].

R

Figure 2.2 Domestic generation of electricity projection 2021 [10].

Installed Capacity as on December, 2016

(By Fuel Type)

Hydro, 230MW, 1.75%

Natural Gas,

8256MW,

62.78%

Total Installed Capacity: 13, 151 MW

MW

Furnace Oil, 2787MW

21.19%

Diesel, 1028MW, 7.82%

Power Import, 600MW,

4.56%

Power Import,

900MW, 5% HSD, 150MW, 1%

Gas, 3360MW, 19%

LNG, 2050MW, 12%

Dual Fuel, 2499MW

14% Coal, 5952MW, 34%

HFO, 2605MW, 15%

12

2.2 Prospects of solar energy in Bangladesh

Bangladesh is an over populated (1015 km2) [11] developing country, having no supply of

electricity in many remote areas of the country. Rural electrification through solar photovoltaic

(PV) technology is promising and becoming more popular. Solar Home Systems are highly

decentralized and particularly suitable for remote, inaccessible areas, therefore, the business of

solar power system was introduced by both governmental and nongovernmental organizations.

Solar power systems are contributing a huge amount of energy and changing the current energy

requirements, especially in rural areas of Bangladesh. At present there are 30 organizations

conducting solar energy businesses in Bangladesh [12]. The Government has taken an ambitious

target to ensure access to electricity for all by 2021. The government of Bangladesh has set an

ambitious goal of providing electricity connection to every rural household. Solar and other

renewable energy can be the key component of this ambitious goal. In some rural areas of

Bangladesh, people live distant from the main grid connection to have reliable, affordable, and

efficient electricity connections for their development.

To ensure energy access, energy security and pollution free clean electricity for all there is

not much alternative available for Bangladesh except Renewable Energy especially solar energy.

Solar energy has very small share in the present energy mix in Bangladesh.

As of 2017, Bangladesh has the world’s largest SHS program with about 5 million SHS.

Over 30 million people are benefitting directly from solar energy and over 100,000 new

employments have already been created [13]. Bangladesh is blessed with year round sunshine

(over 300 days per year) and has an enormous potential for solar energy. The country has made

significant progress in the rural renewable energy development by installing SHS in the off-grid

areas. Back in 1996, SHS became popular among the rural people of Bangladesh for its affordable

monthly installment-based financial model at the price of kerosene.

Green Technology Centers (GTC) were established in rural areas to train rural women, for

capacity-building, and after-sales services at clients’ door steps. A strong network of supply chain

and branches also help SHS become popular and acceptable. At present, people can easily charge

their mobile phones, watch television, use fans, children can study better, and people can do their

important household chores at night with the Solar Home System (SHS) [13].

13

Bangladesh government has taken a systematic approach towards renewable energy

development. The initiative includes development of awareness, legal and regulatory framework,

institutional development, and financing mechanism to drive the RE sector.

Some Systems (SHS) in Bangladesh has ignited the future prospects for solar energy, and

will expedite to make the country the first solar nation in the world. Along with the SHS, 617 Solar

Irrigation Pumps (SIP) have already been installed, along with solar street lights in Dhaka,

Chittagong, Khulna, and remote rural areas of the country, 7 solar mini-grids in remote islands,

urban rooftop solar program, and solar-powered arsenic water treatment plants are complementing

the effort to generate clean power [13]

Community-based solar approach such as solar irrigation pumps, solar mini-grid, arsenic

water treatment plants, and solar street lights have the potential of benefitting the community

people by ensuring food security, arsenic free pure water, improved socio-economic conditions in

off-grid areas of Bangladesh etc. It’s possible to produce additional electricity of 30,000MW from

the utilization of solar PV at schools, colleges, universities, mosques, temples madrasahs,

government buildings, factories, bus stations, train stations, unused lands, community-based PV

plants, and grid tie mega projects. At present, more than 1.6 million irrigation pumps are there and

among them 1.3 million pumps are run by diesel. To replace traditional diesel-run irrigation

pumps, 617 solar irrigation pumps have already been installed. By replacing all these diesel-run

pumps, about 10,000MW electricity can be produced by solar energy [13] Bangladesh is

committed to achieve 17 Sustainable Development Goals by 2030, which includes combating

climate change and increasing energy access from renewable energy sources. As a climate

vulnerable country, and for sustainable energy development and energy security, a Bangladesh

solar mission needs to be designed to achieve SDGs by 2030 and to build the foundation to reach

100% renewable energy (RE) in the future.

Bangladesh has huge potential, but it must overcome many challenges, especially the

challenges of global warming and energy crisis along with poverty reduction to realize its full

potential. We can dream of a future where RE technology is the major contributor to our energy

mix. We can dream of providing all the modern facilities to thousands of rural villages through the

next decade. Moving towards renewable energy can bring a true green revolution for the rural

14

people by developing agricultural output, offering food security, providing modern facilities,

creating new businesses and jobs for both men & women.

From the experiences of the last two decades, we can say that solar energy has become

very familiar and accepted by the people of Bangladesh.

2.3 Installed solar energy in Bangladesh

Bangladesh is situated between 20.30 and 26.38 degrees north latitude, 88.44 and 92.44

degrees east latitude which is an ideal location for solar energy utilization [14] At this position the

amount of hours of sunlight each day throughout a year is shown in the following graph in Figure

2.3. Also the highest and lowest intensity are shown in this figure.

Figure 2.3 The amount of hours of sunlight in Bangladesh [15].

Under the Hill Tracts Electrification Project BPDB has already implemented three solar

projects in Juraichori Upazilla, Barkal Upazilla and Thanchi Upazilla of Rangamati District. Under

1st, 2nd and 3rd Phases, 1200 sets Solar Home Systems of 120 Wp each, 30 sets Solar PV Street

Light Systems of 75 Wp each, 3 sets Solar PV Submersible Water Pumps of 1800 Wp each, 6 stes

Solar PV Vaccine Refrigerators for the Health Care Centers of 360 Wp each and 2 sets 10 kWp

capacity Centralized Solar System for market electrification has been installed. So, a total of

15

173.81 kWp Solar PV Systems have been installed in Juraichori, Barkal and Thanchi upazilla of

Rangamati District under the Hill Tracts Electrification Project [16]. The t use of renewable energy

is increasing day by day in Bangladesh as shown in Table 2.1.

Table 2.1: Major solar PV systems implemented by BPDB [16].

Fiscal Year Location Capacity (kWp)

2010-2011 WAPDA Building, Motjheeel. 32.75

Chairman Banglo, BPDB 2.82

Agrabad Bidyut Bhaban, Chittagong 6

Cox's BPDB Rest House 1.8

2011-2012 15th floor of Bidyut Bhaban 4

PC Pole Factory, Chittagong. 3

Khagrachori BPDB Rest House. 3

Swandip Power House and Rest House. 2.16

Sales & Distribution Division, HatHajari. 2.16

Sales & Distribution Division, Fouzdarhat. 3.12

Sales & Distribution Division, Rangamati. 3.12

Titas 50 MW Peaking Power Plant. 1.6

Baghabari 50 MW Peaking Power Plant. 1.6

Bera 70 MW Peaking Power Plant. 1.6

Chittagong Power Plant. 1.5

Ghorashal Power Plant. 3.5

2012-2013 Khulna Power Station. 4

16

Faridpur 50 MW Peaking Power Plant.

1.6

Goplagonj 100 MW Peaking Power Plant. 1.6

Sales & Distribution Division, Bakolia. 2

Sales & Distribution Division, Pathorghata and Madarbari. 2

Sales & Distribution Division, Stadium. 2

Sales & Distribution Division, Agrabad. 2

Sales & Distribution Division, Halishohor. 2

Sales & Distribution Division, Khulshi. 2

Sales & Distribution Division, Pahartoli. 2

Sales & Distribution Division, Mohora. 2

Distribution Division, Patiya. 2

Distribution Division, Bandarban. 2

Regional Civil Construction Division, Medical centre and

Magistrate Building.

6

Sales & Distribution Division, Feni 2

Sales & Distribution Division, Chowmuhuni, Noakhali. 2

Santahar 50 MW Peaking Power Plant. 3

Residential building of Katakhali 2

Dohazari 1.6

Chandpur 27.2

Grid Tied Power System at Chittagong Power Station. 25

17

2.3.1 Ongoing projects of BPDB

i. 650 KWp (400 kW load) Solar Mini Grid Power Plant at remote Haor area of Sullah

upazila in Sunamgonj district under Climate Change Trust Fund (CCTF) on turnkey

basis.

ii. 8 MWp Grid Connected Solar PV Power Plant at Kaptai Hydro Power Station,at

Rangamati on turnkey basis.

iii. 3 MWp Grid Connected Solar PV Power Plant at Sharishabari, Jamalpur on IPP basis.

iv. 30 MWp Solar Park Project adjacent to new Dhorola Bridge, Kurigram on IPP basis.

v. Solar Street Lighting Projects in seven (7) City Corporations of the country.

2.4 Photovoltaic (PV) overview

An introduction about the main PV electrical characteristic is explained,

2.4.1 Photovoltaic effect

Voltage or electric current is generated by the photovoltaic effect in a photovoltaic cell

when sun light is exposed in a photovoltaic cell. By this photovoltaic effect, panel converts sunlight

to electrical energy. Edmond Becquerel discovered photovoltaic effect in 1839. Edmond Becquerel

investigated that the voltage of the wet cells increased when its silver plates were exposed to the

sunlight [18].

A p-n junction is created by joining together a p-type and n-type semiconductors. When electrons

move to the positive p-side and holes moves to the negative n-side an electric field is generated

[19].

A photon is an elementary particle of electromagnetic radiation. Photovoltaic cell can absorb the

photon. When light is incident to the Photovoltaic cell energy from the photon is transferred to an

atom in the p-n junction. When unexcited, electrons are formed the bonds by surroundings atoms,

for that electrons cannot move. Again when excited, electrons are free to move through the

semiconductors material. These freed electrons tend to move to the n-type semiconductor and

electric current is created by this motion in the Photovoltaic cell. After moving electrons, hole is

18

created and this hole can also be move to the p-side. By this process a current in cell is created

[18]. A diagram of this process is shown in Figure 2.4

Figure 2.4 A diagram showing the photovoltaic effect [20].

2.4.2 Photovoltaic characteristics

Photovoltaic systems are mainly grouped in two categories; Stand-alone system (also

called offgrid) and grid connected system (also called on-grid). Stand-alone systems can be

integrated with another energy source such as Wind energy or a diesel generator which is known

as hybrid system. The storage is the main difference between these categories, where the produced

19

electrical energy is stored in batteries in off-grid system and the public grid utility is the storage

tank for the excessive produced energy from on-grid systems.

Photovoltaic systems can provide electricity for home appliances, villages, water pumping,

desalination and many other applications. Figure 2.5 explains briefly the different photovoltaic

system applications:

Figure 2.5 Photovoltaic systems applications [21].

Irradiation effect

Photovoltaic output power is affected by incident irradiation. PV module short circuit current

(Isc) is linearly proportional to the irradiation, while open circuit voltage (Voc) increases

exponentially to the maximum value with increasing the incident irradiation, and it varies slightly

with the light intensity [22]. Figure 2.6 describes the relation between Photovoltaic voltage and

current with the incident irradiation.

PV

systems

Stand-alone

systems Grid-connected

systems

Without

storage

With

storage Hybrid

systems

Applia

nces

With wind

turbine

Small

applications

With cogeneration

engine

AC stand-alone

systems

With diesel

generator

DC stand-alone

systems

Directly connected to the

public grid

Connected to public grid

via house grid

20

Figure 2.6 Effects of the incident irradiation on module voltage and current [22]

Temperature effect

Module temperature is highly affected by ambient temperature. Short circuit current increases

slightly when the PV module temperature increases more than the Standard Test Condition (STC)

temperature, which is 25oC. However, open circuit voltage is enormously affected when the

module temperature exceeds 25oC. In other word, the increasing current is proportionally lower

than the decreasing voltage. Therefore, the output power of the PV module is reduced [22]. Figure

2.7 explains the relation between module temperature with voltage and current.

Photovoltaic Array Irradiance Characteristic

PV Voltage

Decreasing Solar

Irradiance

PV

Cu

rren

t

21

Figure 2.7 Effect of ambient temperature on module voltage and current [22]

Fill factor (FF)

The fill factor is an important parameter for PV cell/module; it represents the area of the largest

rectangle, which fits in the I-V curve. The importance of FF is linked with the magnitude of the

output power. The higher the FF the higher output power. Figure 2.8 illustrates the fill factor which

is the ratio between the two rectangular areas and is given by the following formula. The ideal FF

value is 1 which means that the two rectangles are identical [23].

Figure 2.8 Fill factor [23]

Max Power Point

Vmp Voltage

Area= Imp x Vmp

Isc

Curr

ent

Area=

Isc x V

oc

Voc

22

Module efficiency (ηPV):

The PV cell/module efficiency is the ability to convert sunlight to electricity. The efficiency is

necessary for space constraints such as a roof mounted system. Mathematically, it determines the

output power of the module per unit area. The maximum efficiency of the PV module is given by:

𝜂𝑃𝑉𝑚𝑎𝑥=

𝑉𝑀𝑃∗𝐼𝑀𝑃

𝐺∗𝐴∗ 100% (2.1)

Where G is global radiation and considered to be 1000 W/m2 at (STC) and A is the Area of the

PV module [24].

2.4.3 Photovoltaic module technologies

Different modules technology have been invented. In this study, two main types of single junction

technology are silicon crystalline and thin film technologies were studied. Figure 2.9 shows the

main parts of the PV module structure, they are as follows:

Front surface: it is a glass cover which has the capability of high transmission and low

reflection for capturing sun light wavelength. This front surface is made by low iron glass

because of low cost, stable, highly transparent and impermeable to water also has self-

cleaning properties [24].

Encapsulated: For providing firmed bond between the solar cells encapsulated is used. It

has the stable capability at various operating temperatures and also transparent with low

thermal resistance. EVA (ethyl vinyl acetate) is used to make a thin layer at the front and

back surface of the cell [24].

PV cells: It is the main parts of the module for generating electricity.

Back surface: It is made by the thin polymer sheet as a back sheet of the cells. Back surface

must have low thermal resistance [24].

23

Figure 2.9 PV module layers [24].

The following is a summary of some of the available PV technologies in the market.

2.4.3.1 Crystalline technology

Crystalline technology is the most efficient PV modules available in the market. In general,

silicon based PV cells are more efficient and longer lasting than non-silicon based cells. On the

other hand, the efficiency decreases at higher operating temperature [25]. In this study, the PV

cells are made by the crystalline technology.

2.4.3.2 Monocrystalline technology

Monocrystalline is the oldest, most efficient PV cells technology which is made from

silicon wafers after complex fabrication process [25].

Monocrystalline PV cells are designed in many shapes: round shapes, semi-round or square

bars, with a thickness between 0.2mm to 0.3mm [25]. Round cells are cheaper than semi-round or

square cells since less material is wasted in the production. They are rarely used because they do

not utilize the module space. However, in BIPV or solar home systems where partial transparency

is desired, round cells are a perfectly viable alternative [25] Figure 2.10 shows the monocrystalline

PV module and cell layered structure.

EVA

Tedlar

24

Figure 2.10 Monocrystalline PV module and cell layered structure [26]

The main properties of monocrystalline PV module are [21]:

Efficiency: 15% to 18% (Czochralski silicon).

Form: round, semi round or square shape.

Thickness: 0.2mm to 0.3mm.

Color: dark blue to black (with ARC), grey (Without ARC).

2.4.3.3 Polycrystalline

Polycrystalline PV modules are cheaper per unit area than monocrystalline; the module

structure is similar to the monocrystalline [25]. To increase the overall module efficiency,

larger square cells should be used. By using larger cells the module cost will be lower, because

less number of cells are used [21]. Figure 2.11 shows a polycrystalline cell and module.

EVA EVA

25

Figure 2.11 Polycrystalline cell and module [21]

The main properties of polycrystalline PV module are [21].

Efficiency: 13% to 16 %.

Form: Square.

Thickness: 0.24mm to 0.3mm.

Color: blue (with ARC), silver, grey, brown, gold and green (without ARC).

2.4.3.4 Thin film technology

Thin film technology represents the second PV generation; due to less production materials

and less energy consumption, it’s cheaper than crystalline technology. Amorphous silicon, Copper

Indium Silinum (CIS) and Cadmium Telluride (CdTe) are used as semiconductor materials.

Because of the high light absorption of these materials, layer thicknesses of less than 0.001mm are

theoretically sufficient for converting incident irradiation [21] Figure 2.12 shows a comparison

between the Crystalline and thin film technologies. It can be seen that thin film technology has the

lower cell thickness, semiconductor consumption and primary energy consumption.

26

Figure 2.12 Comparison of cell thickness, material consumption and energy expenditure for thin-

film cells (left) and crystalline silicon cells (right) [21]

Thin-film cells are not limited to standard wafer sizes, as in the case of crystalline cells.

Theoretically, the substrate can be cut to any size and coated with semiconductor material.

However, because only cells of the same size can be connected in series for internal wiring, for

practical purposes only rectangular formats are common. “The raw module” is the term which is

used for thin film technology [14].

Despite the relatively low efficiency per unit area, thin film technology has many advantages when

compared to crystalline technology [21].

Better utilization of diffuse and low light intensity.

Less sensitive to higher operating temperature.

Less sensitive to shading because of long narrow strip design, while a shaded cell on

crystalline module will affect the whole module.

Energy yield at certain condition is higher than crystalline technology.

Cell thickness in μm Semiconductor

consumption in kg/KWp

Primary energy consumption

in MWh/KWp

1-6 0.2

10-12 6-10.5

10.5-19

27

2.5 Meteorological study

Dhaka experiences a hot, wet and humid tropical climate. Under the Köppen climate

classification, Dhaka has a tropical wet and dry climate [27] The city has a distinct monsoonal

season, with an annual average temperature of 25 °C (77 °F) and monthly means varying between

18 °C (64 °F) in January and 29 °C (84 °F) in August [27] Nearly 80% of the annual average

rainfall of 1,854 millimetres (73.0 in) occurs during the monsoon season which lasts from May

until the end of September. Figure 2.13 shows the land surface temperature of Dhaka city. Bayes

et al.[28] investigated that built-up areas exhibited the highest temperature. Table 2.2 represents

the solar energy and surface meteorology data of Dhaka city. The data were obtained from the

NASA Langley Research Center Atmospheric Science Data Center and other locations of

meteorological data are shown in Appendix A. It was observed from Table 2.2 that maximum

irradiation or insolation was found on March 5.18 KWh/m2/day. Average temperature, wind speed

and irradiation in a year of Dhaka City are 24.58 oC, 27.2 m/s and 4.67 KWh/m2/day respectively.

Increasing air and water pollution emanating from traffic congestion and industrial waste

are serious problems affecting public health and the quality of life in the city. Water bodies and

wetlands around Dhaka are facing destruction as these are being filled up to construct multi-storied

buildings and other real estate developments.

28

Figure 2.13 Land surface temperature map of Dhaka City [28].

29

Table 2.2 Solar energy and surface meteorology of Dhaka [29].

Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Insolation,

kWh/m2/day

4.29 5.18 5.96 5.83 5.28 4.49 4.09 4.20 3.95 4.43 4.37 4.07

Clearness,

0-1 0.63 0.65 0.54 0.56 0.48 0.40 0.37 0.40 0.41 0.53 0.62 0.63

Temperature, oC

18.61 21.81 25.69 26.61 27.11 27.67 27.42 27.44 26.67 24.95 21.81 19.22

Wind speed,

m/s 2.59 2.85 3.00 3.11 3.05 2.88 2.59 2.34 2.21 2.07 2.30 2.40

Precipitation,

mm 6 19 54 138 269 377 376 315 266 162 33 7

Wet days, d 0.5 1.3 2.6 6.7 10.7 14.7 15.2 14.9 11.4 6.0 1.4 0.4

2.6 Recent Literatures

Dincer and Rosen [30] studied the paper on a comprehensive of the future of energy and

the environmental effects such as acid rain, ozone layer depletion and the greenhouse effect. With

relation to this various current and probable issues pertaining to energy environment and

sustainable development have been examined.

Pillai and Banerjee [31] studied the growth of renewable sources in India and set the targets

using diffusion model which besides using with the past trends for wind, small hydro and solar

water heating is used to predict future targets. The economic feasibility and the extent up to which

greenhouse gases are reduced are estimated for each option. Renewable sources like wind,

Photovoltaic (PV) module manufacture and solar water heaters grow at a high rate.

Sharma [32] carried out a survey on development and evolution of non-conventional

energy sources. Renewable energy sources such as solar, hydro, bioenergy etc are moving into

focus. Although solar energy is most promising energy areas in current times but the future of its

technology and markets is still debatable. This report explores the multifarious opportunities

opened by solar energy.

Sharma & Chandel [33] studied performance of a 190 kwp roof top grid connected PV

system in Eastern India. They studied various parameters of the system including PV module

efficiency, array yield, final yield, and inverter efficiency and performance ratio of the system.

30

Anik deb et al. [34] investigated the prospects of solar energy in Bangladesh. The identified

the possible implementations of solar technologies like photovoltaic cells (PV) and solar thermal

energy (STE). They discussed optimum capacity, efficiency, storage facility and cost per unit

power of the PV cells. Some social, economic and environmental constraints regarding the

implementation of solar technology are highlighted and some possible solutions were discussed.

31

CHAPTER 3

Heuristic Model

3.1 Performance characterization

Normally, the starting point in the system performance analysis of the PV systems is its

rated d.c power at standard test conditions (STC) i.e. irradiance of 1000 W/m2, AMI=1.5 and cell

temperature of 25 oC. Next step is to determine actual ac power produced once system is put into

the field conditions. The following performance characterization which is given by

Eac,stc=Edc,stc*inverter conversion efficiency (3.1)

Where Edc,stc is the name plate power rating of the system at STC and conversion efficiency

includes the effect of the inverter efficiency. Eac,stc is the energy in AC.

Total Yield

The total yield of the system is defined as net or total energy produced by the plant in KWh in

a given time e.g. a day, a month or a year.

Specific Yield

The specific yield of the PV system is defined as the ratio of the net or total energy output to

the name plate dc power rating of the system specified at standard test conditions (STC).

Sy =Eout

Eplate (3.2)

Where Sy is the specific yield, Eout is the net or total energy output (KWh) and Eplate is the name

plate dc power rating (KWp).

Specific yield normalizes the plant output to the system size and can be used to compare

performance of the plants of different power rating. In other words, specific yield indicates the

number of hour plant operated at its rated capacity.

32

Performance ratio

The plant performance ratio (PR) is one of the mostly used performance indicator and

commonly known as plant quality factor which can be effectively used to compare plants installed

at different locations. The PR is defined as the ratio of actual and theoretical or nominal plant

output.

PR =Eactual

Enominal (3.3)

Where Eactual is the actual plant output and Enominal is the calculated nominal plant output which can

be calculated by following relation

Enominal = Ir ∗ ηpv (3.4)

Where Ir is the incidence solar irradiation at the modules surface of the PV plant (KWh) and η𝑝𝑣

is the efficiency of the PV modules.

PR can be determined on daily, monthly or yearly basis. It indicates the proportion of the energy

available for export to the grid after deducting thermal losses and conduction losses.

The performance ratio tells the plant owners that how energy efficient and reliable their plant

is. The determination and monitoring of PR at regular intervals can lead towards the possible faults

and other issues in case abnormal deviation is observed in the PR value. There are number of

factors which influence the PR such as temperature, irradiance, soiling of module or sensors,

module and inverter efficiency, solar technology, and recording period.

3.2 Solar angles

The position of the sun at different periods is determined by the solar angles. Moreover,

solar angles are used to track the movement of the sun in a day. The rotation of the sun varies

depending on the latitude and longitude of the location. Therefore, the solar angles will be different

for the locations at different latitude and longitude during the same period. So, the solar angles

must be known to determine the position of the sun.

33

Latitude: The latitude angle (Ø) is the angle forming according to the equator center. The

north of the equator is positive and the south of the equator is negative and it varies between -90˚

≤ Ø ≤ 90˚.

Longitude: specifies the east-west position of a point on the Earth's surface. It is an angular

measurement, usually expressed in degrees and denoted by the Greek letter lambda (λ). Longitudes

traditionally have been written using "E" or "W" instead of "+" or "−" to indicate this polarity.

Declination angle: Declination angle (δ) is the angle between the sun lights and equator

plane. Declination angle occur due to 23.45 degrees angle between earth’s rotational angle and the

orbital plane. It is positive at north and varies between -23.45 ˚ ≤ δ ≤ 23.45 ˚. Declination angle is

calculated by the following equation and shown in Figure3.1.

δ = 23.45 sin [360 ∗(284+𝑛)

365] (3.5)

Where n represents the day of the year and 1st January is accepted as the start. In Table 2.9

represents declination angles which are calculated by the above formula.

Figure 3.1 Declination angle [28].

34

Table 3.1 Declination angles

Month n δ (degree)

Jan 15 -21.2695

Feb 45 -13.6198

Mar 75 -2.41773

Apr 105 9.414893

May 135 18.79192

Jun 165 23.26761

Jul 195 21.67462

Aug 225 14.42842

Sep 255 3.418991

Oct 285 -8.48219

Nov 315 -18.171

Dec 345 -23.1205

Hour angle: Hour angle (ω) is the angle between the longitude of sun lights and the

longitude of the location. The angle before noon and after noon are taken as (-) and (+)

respectively. This angle is 0 at noon. The hour angle is defined as the difference between noon and

the desired time of the day. This angle is calculated by multiplying this difference by 15 fixed

number. This fixed number is the angle of 1 hour rotation of the earth around the Sun. An

expression to calculate the hour angle from solar time is

ω = 15(ts − 12) (3.6)

And the mean sunshine hour angle for the month (𝜔𝑠) were calculated using the following

formula

𝜔𝑠 = 𝑐𝑜𝑠−1(−𝑡𝑎𝑛∅ ∗ 𝑡𝑎𝑛𝛿) (3.7)

Table 3.2 Hour angles

Hour(ts) 6 7 8 9 10 11 12 13 14 15 16 17 18

ω -90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90

35

Zenith angle: Zenith angle (θz) is the angle between the line to the sun and the vertical axis. Zenith

angle is 90º during sunrise and sunset whereas it is 0 ̊ at noon. Zenith angle is calculated depending

on the other angles.

𝛾𝑠 = 𝑐𝑜𝑠−1[(sin(α) . sin(∅) −sin (𝛿)

cos(𝛼).cos (∅) (3.8)

Figure 3.2 The Basic solar angle [35]

Elevation angle: Solar elevation angle (α) is the angle between the line to the sun and the

horizontal plane. This angle is the complement of the zenith angle 90˚. Elevation angle is

calculated by the following equation.

θ = cos−1[(cos(δ) . cos(∅). cos(ω) + sin(δ) . sin(∅)] (3.9)

36

Azimuth angle: Solar azimuth angle (γs) is the angle between the north or south position of the

sun and the direct solar radiation. This angle is assumed to be (-) from south to east and to be (+)

from south to west. (γs) is 180˚ at noon. Azimuth angle is calculated by the following equation.

β = │∅ − δ│ (3.10)

Surface azimuth angle (γ) is the angle between the projection of the normal to the surface on a

horizontal plane and the line due south. This angle is 0 in south, negative in east (-) and it is positive

(+) towards west. It varies between -180˚ and 180˚.

Incidence angle: Incidence angle (θ) is the angle between the radiation falling on the surface

directly and the normal of that surface. If incidence angle is steep to the sun lights, it is (θ=0˚). On

the other-hand if this angle is parallel to the sun lights, it is (θ=90 ˚).

cos𝜃𝑧 = (cos(𝛿) . cos(∅). cos(ω) + sin(𝛿) . sin(∅) (3.11)

Tilt angles: Tilt angle (β) is the angle between the panels and the horizontal plane. This angle is

south oriented in the Northern Hemisphere and north oriented in the Southern Hemisphere. Tilt

angle varies between 0º ≤ β ≤ 180 ˚. When a plane is rotated about horizontal east-west axis with

a single daily adjustment, the tilt and incidence angle is shown in Figure 3.3. the tilt angle of the

surface will be fixed for each day and is calculated by the following equation.

tanβ = tanθz│cosγs│ (3.12)

On the other hand, when the plane is rotated about a horizontal east-west axis with continuous

adjustment, the tilt angle of the surface will be calculated by the following equation.

tanβ = tanθz│cos(γ − γs)│ (3.13)

For the rotation of a plane about a horizontal north-south axis with continuous adjustment, the tilt

angle of the surface will be calculated by the following equation

α = 90 − θz (3.14)

37

Figure 3.3 Incidence and tilt angle [35].

The estimated value of 𝛿 and 𝜔𝑠 are used to calculated Rb which is the ratio of the average daily

direct radiation on a tilted surface to that on a horizontal surface

𝑅𝑏 =cos(∅−𝛽)∗𝑐𝑜𝑠𝛿∗𝑠𝑖𝑛𝜔𝑠

′+𝜋

180∗𝜔𝑠

′∗sin(∅−𝛽)∗𝑠𝑖𝑛𝛿

𝑐𝑜𝑠∅∗𝑐𝑜𝑠𝛿∗𝑠𝑖𝑛𝜔𝑠′+ (

𝜋

180)∗𝜔𝑠

′∗𝑠𝑖𝑛∅∗𝑠𝑖𝑛𝛿 (3.15)

Where,

𝜔𝑠 = (𝑎𝑐𝑜𝑠(−𝑡𝑎𝑛∅ ∗ 𝑡𝑎𝑛𝛿)

𝑎𝑐𝑜𝑠 − tan (∅ − 𝛽) ∗ 𝑡𝑎𝑛𝛿) (3.16)

38

CHAPTER 4

Expected Output of Existing Solar Panel

4.1 Theoretical efficiency of solar panel

The theoretical efficiency is measured under laboratory conditions and represents the maximum

achievable efficiency of the PV module. Actual efficiency depends on the output voltage, current,

junction temperature, light intensity.

A solar panel is first tested in the factory. As the panel comes of the production line, a

worker or a robot places the on a flash table and hooks up the positive and negative leads to a

measuring device. The panel is then flashed with fake sunlight. This testing condition is called

Standard Test Conditions (STC). The calibrated light source precisely 1000 W/m2 fall on the solar

panel surface at 25°C. The performance of the module under STC is shown in Table 4.1. The

electrical characteristics for STC are shown in Table 4.2

Table 4.1 STC performance of the solar panel

Model Module Description

BOSCH 250 c-Si M 60 250W Mono-crystaline

39

Table 4.2 PV module and inverter specification

PV module (STC) Inverter

Parameters Specification Parameters Specification

Type of module Mono-crystalline Model BOSCH 250

Pmax 250 W Input(DC)

Iam 8.25 A Nominal Power, W 12250

Vam 30.31V Voltage range, V 1000

Isc 8.82 A Nominal current, A 22/11

Voc 37.9 Maximum current, A 33/12.5b

Temperature Coefficient of Pmax 0.44%/°C Output (AC)

Nominal operating cell

Temperature (NOCT) 48.4 °C Voltage range, V 160-280

Module area, m2 (1620.16*966.24) Nominal current, A 19.2

No. of modules Nominal frequency, Hz 50

Efficiency 13% Efficiency, % 98/97.7

Weight/module, kg 21 Weight, kg 64

4.2 Expected performance of the solar panels after sun simulator

test

A solar simulator or sun simulator is a device which has similar intensity and spectral

composition to the nature of sunlight. It is widely used as a controllable indoor test facility offering

laboratory conditions for solar cells. A solar simulator usually consists of three major components:

(i) light source(s) and associated power supply; (ii) any optics and filters used to modify the output

beam to meet the requirements; (iii) necessary controls to operate the simulator. Xenon lamps or

40

other artificial light sources are usually chosen as the light source of a standard solar simulator.

However, there are differences between artificial light source(s) and nature sun light, both in

intensity and spectral composition, which only with the help of optics and filters can be modified

to meet the nature sun light. Furthermore, as the outdoor condition is time dependent, it is

necessary to define a standard test condition for the solar simulator. The testing lab of sun simulator

is shown in Figure 4.1 and software interfaces are shown in Figure 4.2 and 4.3. Performance

measured from the sun simulator is represented in Table 4.3. This experiment was carried out by

Ava Renewable Energy Ltd. at Kashimpur, Gazipur.

Figure 4.1 Sun simulator testing lab

41

Figure 4.2 Interface of the sun simulator

Figure 4.3 Output interface of the sun simulator.

42

Table 4.3 Test data of sun simulator

Serial No Isc Voc Pm Ipm Vpm Eff.(%) F.F Rs(ohm) PWR(KW)

M60

EV30117 8.165 37.661 194.39 6.463 30.077 13.129 0.632 1.379 194.390

M60

EV30118 7.983 37.677 189.68 6.208 30.556 12.811 0.631 1.42 189.680

M60

EV30119 8.626 37.614 189.62 6.170 30.735 12.808 0.618 1.437 189.620

M60

EV30120 7.914 37.613 189.28 6.158 30.740 12.785 0.635 1.453 192.390

M60

EV30121 8.171 37.630 194.37 6.461 30.084 13.128 0.632 1.371 194.370

Mean 8.172 37.639 191.45 6.292 30.438 12.9322 .629 1.412 192.09

43

CHAPTER 5

Study of Hour Angles and Tilt Angles

5.1 Effects of hour angles and tilt angle

5.1.1 Effects of hour angles

The experimental investigation as shown in Figure 5.1 of average energy generation by

varying hour angle is shown in Figure 5.2. The microprocessor collected data in every 30 seconds

later in a day. It was observed that there is strong correlation with the hour angle as described in

Table 5.1 and energy generation. This experiment was carried out at Bangladesh Army

Headquarters, Dhaka in December, 2017.

Figure 5.1 Experimental investigation of solar panel

44

Figure 5.2 Average energy generated by varying hour angle of sun.

Table 5.1: Average energy generated by varying hour angle

Time Energy generation (WATT)

12PM 16.9

2PM 16.58

4PM 16.10

8AM 15.78

10AM 16.45

12PM 16.76

4PM 14.40

45

Table 5.2: Energy generation per square meter collected by panels at different tilt angles

Month Tilt

Angle

Energy

generation

(KWh/m2/day)

Energy

Generation for

fixed Angle

(22o)

(KWh/m2/day)

Energy

Variation

Theoretical

Rb

Actual

Rb

January 45.09 0.932 0.786 0.146 1.957504 1.185751

February 37.44 0.962 0.912 0.05 2.891275 1.054825

March 26.23 1.031 1.221 -0.19 15.39757 0.84439

April 14.4 1.138 0.795 0.343 3.987754 1.431447

May 5.02 1.49 0.84 0.65 2.083101 1.77381

June 0.55 1.125 0.985 0.14 1.739718 0.822335

July 2.14 1.39 1.34 0.05 1.843204 0.881343

August 9.39 1.159 0.9685 0.1905 2.650646 1.196696

September 20.4 1.21 1.114 0.096 10.87956 0.885099

October 32.3 1.059 0.859 0.2 4.505505 1.232829

November 41.99 1.043 0.924 0.119 2.237621 1.128788

December 46.94 1.05 0.981 0.069 1.827667 1.070336

It was observed that 12.56% energy increase when angle was changed for each month. Figure 5.2

shows a comparison graph of energy generation with changing angle and energy generation for

fixed angle. Figure 5.3 Shows the ratio of the average daily direct radiation on a tilted surface to

that on a horizontal surface (theoretical Rb) and the ratio of energy generation on a tilted surface

to that on a fixed angle (22o). A solar array covering approximately 0.1632m2 has been used to

calculate the amount of energy collected by the solar collectors with the different tilt angle.

46

Figure 5.3 Energy generation for fixed angles and changing angles

Figure 5.4 Comparison graph of theoretical Rb and actual Rb

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ener

gy g

ener

atio

n (

KW

h)

Energy generation for changing angle Energy generation for fixed angle

0

2

4

6

8

10

12

14

16

18

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Theoritical Rb Actual Rb

47

5.1.2 Estimated tilt angle

The tilt angles shown in Table 5.1 are calculated theoretically from the formula discussed

in mathematical model chapter (chapter 3). From the Table 5.1, it was observed that in June tilt

angle is maximum and average maximum tilt angle in June is 45.41 degree.

Table 5.3 Tilt angles for each month of a year

Month Plants Chattogram Jashore Cumilla

Dhaka

Head

Quetrter

Dhaka

Armed Savar Bogura Rangpur

Ø δ 22.41 23.17 23.47 23.81 23.8158 23.91 24.76 25.76

Jan -21.26 43.68 44.44 44.74 45.09 45.09 45.19 46.03 47.03

Feb -13.62 36.03 36.79 37.09 37.44 37.44 37.54 38.38 39.38

Mar -2.42 24.83 25.59 25.89 26.23 26.23 26.34 27.18 28.18

Apr 9.42 13.00 13.76 14.06 14.40 14.40 14.50 15.35 16.35

May 18.79 3.62 4.38 4.68 5.02 5.02 5.13 5.97 6.97

Jun 23.27 0.86 0.10 0.20 0.55 0.55 0.65 1.49 2.50

Jul 21.67 0.74 1.50 1.80 2.14 2.14 2.25 3.09 4.09

Aug 14.43 7.98 8.74 9.04 9.39 9.39 9.49 10.33 11.34

Sep 3.42 18.99 19.75 20.05 20.40 20.40 20.50 21.34 22.35

Oct -8.48 30.89 31.65 31.95 32.30 32.30 32.40 33.24 34.25

Nov -18.17 40.58 41.34 41.64 41.99 41.99 42.09 42.93 43.94

Dec -23.12 45.53 46.29 46.59 46.94 46.94 47.04 47.88 48.89

If the tilt angle will be fixed for a year, the variance will be highest as shown in Table 5.3.

Thus it is not feasible to take a fixed angle over a year for producing maximum power.

48

Table 5.4 Tilt angle for a year of each plant

Solar Plants β (Degree)

Mean Std. Deviation Variance

Chattogram 20.85293138 16.69460898 278.7099689

Jashore 21.49613523 16.84472311 283.7446966

Cumilla 21.78118323 16.86441607 284.4085293

Dhaka Head

Quetrter 22.12596623 16.86441607 284.4085293

Dhaka Armed 22.12596623 16.86441607 284.4085293

Savar 22.23007223 16.86441607 284.4085293

Bogura 23.07088723 16.86441607 284.4085293

Rangpur 24.07508723 16.86441607 284.4085293

Table 5.5 Tilt angle for 2 times a year for each plant

Solar Plants

β (Degree)

Oct-Mar Apr-Aug

Mean Std.

Deviation Variance Mean

Std.

Deviation Variance

Chattogram 36.92437533 7.957189744 63.31686863 7.530409 7.313568531 53.48828465

Jashore 37.68452533 7.957189744 63.31686863 8.037175667 7.611302553 57.93192655

Cumilla 37.98442533 7.957189744 63.31686863 8.304896333 7.65188854 58.55139823

Dhaka Head

Quetrter 38.32920833 7.957189744 63.31686863 8.649679333 7.65188854 58.55139823

Dhaka Armed 38.32920833 7.957189744 63.31686863 8.649679333 7.65188854 58.55139823

Savar 38.43331433 7.957189744 63.31686863 8.753785333 7.65188854 58.55139823

Bogura 39.27412933 7.957189744 63.31686863 9.594600333 7.65188854 58.55139823

Rangpur 40.27832933 7.957189744 63.31686863 10.59880033 7.65188854 58.55139823

If the tilt angle will be changed 2 times a year, the following Table 5.4 represents the mean

angle for a yea. The tilt angle will remain fixed in October to March and another tilt angle will

remain fixed in April to August over a year for each plant.

49

Table 5.6 Tilt angle for 4 times a year

Solar Plants

β (Degree)

Nov-Jan Feb-Apr May-Jul Aug-Oct

Mean Std.

Dev Variance Mean

Std.

Deviation Variance Mean

Std.

Deviation Variance Mean

Std.

Deviation Variance

Chattogram 43.2 2.04 6.25 24.6 11.52 132.6 1.7 1.63 2.66 19.2 11.46 131.2

Jeshore 44.0 2.04 6.25 25.3 11.52 132.6 1.9 2.18 4.77 20 11.46 131.2

Cumilla 44.3 2.04 6.25 25.6 11.52 132.6 2.2 2.27 5.15 20.3 11.46 131.2

Dhaka Head 44.6 2.04 6.25 26.02 11.52 132.6 2.5 2.27 5.15 20.6 11.46 131.2

Dhaka

Armed 44.6 2.04 6.25 26.0 11.52 132.6 2.5 2.27 5.15 20.6 11.46 131.2

Savar 44.7 2.04 6.25 26.13 11.52 132.6 2.6 2.27 5.15 20.8 11.46 131.2

Bogura 45.6 2.04 6.25 26.9 11.52 132.6 3.52 2.27 5.15 21.6 11.46 131.2

Rangpur 46.6 2.04 6.25 27.9 11.52 132.6 4.5 2.27 5.15 22.6 11.46 131.2

If the tilt angle will be changed 4 times a year the following angles shown in Table 5.5

should be considered. The angle will be changed in November to January, February to April, May

to July, August to October for the 1st time, 2nd time, 3rd time and 4th time over a year respectively.

It was observed that varying tilt angle for every month is for costly and time consuming

and systems remain idle. But a fix tilt angle shown in Table 5.3 for every plant produces less power

due to variation of solar angle and variation of irradiation and Table 5.3 shows that variance is

highest. If tilt angles is changed 2 times a year variance is reduced comparing a fixed angle shown

in Table 5.4. But for maximum power collection and lower cost it can be concluded that tilt angle

can be changed 4 times a year as shown in Table 5.5.

50

CHAPTER 6

Fuzzy Logic and ANN Analysis for Irradiation

6.1 Fuzzy logic background

Many traditional processes or methods, such as numerical methods, the Taguchi method,

and finite element analysis, are used to predict the irradiation at different parameters. Traditional

methods commonly use the trial-and-error approach, which is time-consuming when numerous

experiments are required. Therefore, a suitable methodical approach for determining the solar

irradiation at different parameters, covering all the parameters, is required [36]. When an

appropriate mathematical model or information is unavailable, soft computing techniques are the

most useful. Soft computing techniques also differ from traditional computing techniques. The

fuzzy logic technique is a prominent soft computing method that plays a vital role in determining

the relation matrix between input and output parameters. The significance of fuzzy logic increases

with the system complication. Fuzzy logic defines intermediate values between conventional

threshold values because it is a multi-value type of logic. Generally, mathematical equations

generate crisp values if the mathematical model is known. However, in the case of non-linear

dynamic systems, the model is difficult or impossible to predict. In this circumstance, presenting

the system or process as a series of if-then rules is an effective approach, and this is done with a

fuzzy logic model. Fuzzy logic processes a given input value into an output value through a rule

base. Therefore, mathematical equations are unnecessary, and fuzzy logic is very important. Fuzzy

interface is the method of mapping a given input value to an output value using fuzzy logic, and

decisions are made from this mapping. In this study, for prediction irradiation fuzzy logic is

selected due to the non-linearity of the input and output parameters. Many researchers have used

fuzzy interface for prediction irradiation [37-38].

6.1.1 Fuzzy logic

Fuzzy logic is an uninterrupted interpretation system from true to false conditions. The first

practical application of fuzzy logic in the engineering field to control an automatic steam engine

was applied by Dr. E. H. Mamdani in 1974 [39]. The fuzzy logic computation is based on “degrees

of truth” rather than Boolean logic and is applied in modern computers. Fuzzy logic can compute

51

for partial truth as opposed to the distinct true-false condition in binary logic, and it can handle the

numeric values and linguistic knowledge simultaneously. In engineering practices, fuzzy logic

exploits the continuous subset membership conversion to modify crimped numeric problems in

fuzzy linguistic areas. Fuzzy logic uses customary languages to define numeric variables and

mathematical functions. Unlike mathematical expressions, fuzzy logic offers to use knowledge

and experience based on practice rather than theory form. By sustaining the physical interface and

effects of all variables, fuzzy logic simulates complex and non-linear systems.

The relationship between the input parameters (temperature, wind speed, and humidity)

and the output parameter (tilt angle and irradiation) was considered in constructing rules. The fuzzy

linguistic characteristics and fuzzy expression for the input and output parameters are shown in

Table 6.1. Each input parameter has three membership functions: low (L), medium (M), and high

(H). The two output parameters have 7 membership functions: very very low (VVL), very low

(VL), low (L), medium (M), high (H), very high (H), and very very (VVH). The membership

functions were selected based on the knowledge of authors. The MATLAB software was utilized

for the linguistic variables with associated membership functions.

52

Table 6.1 Fuzzy linguistic variables and parameters

Parameter Linguistic variable Range

Temperature (oC) Low (L), medium (M), high (H) 18.6–27.7 (oC )

Wind speed Very low (L), low (l), medium (M),

high (H)

2.07–3.11 (m/s)

Humidity Very low (L), low (l), medium (M),

high (H)

55–80 (%)

Tilt angle very very low (VVL), very low

(VL), Low (L), medium (M), high

(H), very high (VH), very very high

(VVH)

2.14–47 (degree)

Irradiation very very low (VVL), very low

(VL), Low (L), medium (M), high

(H), very high (VH), very very high

(VVH)

3.95–5.16

(KWH/m2/day)

6.1.2 Fuzzy logic rules

In this study, the fuzzy rule base containing a set of IF-THEN statements for 12 rules with

three inputs, namely temperature, wind speed, humidity and with two multi-response output, tilt

angle and irradiation are considered. Twelve rules were identified on the available data shown in

Table (6.2), and the constructed rules are presented in Table (6.3).

The fuzzy output is generated for the fuzzy logic rules by following the maximum minimum

compositional process [40].

53

6.1.3 Defuzzification

The fuzzy output from the fuzzy interface system requires a defuzzification process. Numeric

data are found from the fuzzy set data by the defuzzification process. Researchers utilize several

methods of defuzzification, including center of sum (COS), mean of max, largest area, weight

average, and centroid. In this study, a defuzzification process, called the centroid of area (COA)

technique [41], is used to obtain the numeric value from the fuzzy interface system. Given its

extensive utilization and acceptance, the COA defuzzification technique was used to obtain more

correct results [40].

The defuzzied value defined by 𝑋∗ is obtained using the COA method.

𝑋∗ =∑ 𝑥𝑖.𝜇(𝑥𝑖)𝑛

𝑖=1

∑ 𝜇(𝑥𝑖)𝑛𝑖=1

(6.1)

where 𝑥𝑖 represents the samples, 𝜇(𝑥𝑖) is the membership function, and n denotes the number of

elements.

Table 6.2: Meteorological data for fuzzy logic

Inputs Outputs

Months Temperature

(oc)

Wind speed

(m/sec)

Humidity

(%)

Tilt angle

(Degree)

Irradiation

(KWh/m2/day

January 18.61 2.59 69 45.09 4.29

February 21.81 2.85 58 37.44 5.18

March 25.69 3.00 55 26.23 5.96

April 26.61 3.11 65 14.40 5.83

May 27.11 3.05 73 5.02 5.28

June 27.67 2.88 79 4.06 4.49

July 27.42 2.59 80 2.14 4.09

August 27.44 2.34 80 9.39 4.20

September 26.67 2.21 79 20.40 3.95

October 24.95 2.07 74 32.30 4.43

November 21.81 2.30 68 41.99 4.37

December 19.22 2.40 71 46.94 4.07

54

Table 6.3 Rules used for fuzzy logic model

Rule

number

If statements for input parameters THEN statements for output

response

Temperature Wind speed Humidity Tilt angle Irradiation

1 L M L VVH L

2 L H VL H H

3 M H VL M VVH

4 M H L L VH

5 H H M VL H

6 H H H VL M

7 H M H VVL VL

8 H L H L L

9 H L H M VVL

10 M VL M H M

11 L L L VH M

12 L L M VVH VL

55

Figure 6.1 Input variable Gaussian membership functions for temperature

Figure 6.2 Input variable Gaussian membership functions for wind speed

Figure 6.3 Input variable Gaussian membership functions for humidity

56

Figure 6.4 Output triangular membership function for A Tilt angle and B Irradiation.

A

B

57

Figure 6.5 Fuzzy rules interface

58

Figure 6.6 Surface plot of predicted tilt angle (degree) values by fuzzy logic in relation to parameters

change: A temperature and wind speed, B temperature and humidity and C Humidity and wind speed

A

B

C

59

Figure 6.7 Surface plot of predicted irradiation (degree) values by fuzzy logic in relation to parameters

change: A temperature and wind speed, B temperature and humidity and C Humidity and wind speed

A

B

C

60

In this study, root mean square error (RMSE) and fraction of variance (R2) were used for predicting

the fuzzy logic model performance with the measured values. The rate of error (ei) and the accuracy

of the fuzzy logic model (A) were determined by the following formula.

RMSE=√∑ (𝐹𝑖−𝑀𝑖)2

𝑖

𝑁 (6.2)

R2 = 1-∑ (𝐹𝑖−𝑀𝑖)2

𝑖

∑ (𝐹𝑖)2𝑖

(6.3)

ei =(|𝑀−𝐹|)

𝑀× 100% (6.4)

A=1

𝑁∑ (1 −𝑖

(|𝑀−𝐹|)

𝑀) × 100% (6.5)

where F is the predicted fuzzy value and M is the measued experimental value, and N is the number

of experiment.

Table 6.4 Fuzzy logic model error and accuracy for tilt angles

Months Tilt angle (o) Predicted (fuzzy)

tilt angle Error (%) Accuracy (%)

January 45.09 45.3 0.465735 99.53426

February 37.44 26.7 28.6859 71.3141

March 26.23 23 12.31414 87.68586

April 14.40 12.4 13.88889 86.11111

May 5.02 5.26 4.780876 95.21912

June 4.06 4.69 15.51724 84.48276

Jully 2.14 2.75 28.50467 71.49533

August 9.39 11.65 24.06816 75.93184

September 20.40 18.5 9.313725 90.68627

October 32.30 31.2 3.405573 96.59443

November 41.99 38.3 8.787807 91.21219

December 46.94 45.3 3.493822 96.50618

61

Table 6.5 Fuzzy logic model error and accuracy for irradiation

Months Irradiation

(KWh/m2/day)

Predicted (fuzzy)

irradiation Error (%) Accuracy (%)

January 4.29 4.3 0.2331 99.7669

February 5.18 5.53 6.756757 93.24324

March 5.96 5.93 0.503356 99.49664

April 5.83 5.79 0.686106 99.31389

May 5.28 5.24 0.757576 99.24242

June 4.49 4.62 2.895323 97.10468

Jully 4.09 4.13 0.977995 99.022

August 4.20 4.17 0.714286 99.28571

September 3.95 4.17 5.56962 94.43038

October 4.43 4.62 4.288939 95.71106

November 4.37 4.62 5.720824 94.27918

December 4.07 4.12 1.228501 98.7715

Table 6.6 Performance of the fuzzy logic model

Output RMSE R2 A (%)

Tilt angle 3.6157 0.981489 87.23

Irradiation 0.15695 0.998937 97.47

62

6.2 Artificial Neural Network

ANN is a connectionist system based on the neural structure of the human brain (biological neural

networks), which processes data among a number of neurons. The basic unit of ANN is neurons,

which are connected to one another with a weight factor that determines the strength of the

connections. ANN can be trained for a specific function by adjusting the value of these weight

factors of the neurons.

One of the most widely used neural networks is the multilayered perception (MLP) neural

network. This neural network has been used by a number of researchers [42]. A backpropagation

algorithm is used to train this multilayered feedforward network. Backpropagation identifies the

network error with respect to the network weight and biases of the process. Figure 6.8 shows a

schematic of the MLP network. The MLP model maps a set of input data onto a set of appropriate

output data. The MPL has multiple layers, such as input, hidden, and output layers of nodes, in a

directed graph, with each layer connected to the next layer. MLP utilizes backpropagation, a

supervised learning method, to train the network.

Figure 6.8 Typical structure of an MLP network.

63

In this study, an MLP neural network was utilized to predict the solar irradiation for Dhaka

region in relation to the input parameters, such as air temperature, relative humidity, atmospheric

pressure, wind speed, earth temperature.

Numerous studies on ANN model has been made in recent years. These studies have given

a prediction on monthly solar irradiation. As ANN is of nonlinear behavior and no need for primary

assumption to develop data relationships, it becomes a necessary tool for estimating solar

irradiation. ANN models are developed to model various solar radiation variables in different

locations. Yadav et al. [43] gave a review on artificial neural network models to identify best

suitable method for solar radiation prediction. Yadav et al. [44] designed a neural network model

to determine most relevant input parameters for prediction of solar radiation. Ozgur kisi [45] tried

fuzzy genetic approach to estimate solar radiation and gave a compare with ANN approach.

Egeonu et al.[46] developed a temperature based ANN model trained with the Levenberg

Marquardt algorithm to provide a forecast on solar radiation in Nigeria. ùenkal [47] applied

artificial neural network approach for giving a model and prediction of mean perceptible water

and solar radiation in specific location in Turkey based on meteorological data.

In this present paper, a feed forward back propagation neural network with one hidden layer to

estimate the monthly solar radiation for Dhaka city in Bangladesh. Here the input layer consists of

five units and hidden layer with 15 neurons and output layer with 1 neuron to estimate solar

irradiation.70% of the total data was used for training, 15% for testing and 15% for validating the

developed model.

64

Table 6.7 Meteorological Data for ANN

Month Air

temperature

Relative

humidity

Atmospheric

pressure

Wind

speed

Earth

temperature

Daily solar

radiation -

horizontal

°C % kPa m/s °C kWh/m2/d

January 19.7 53.8% 100.9 1.9 21.5 4.36

February 23.0 49.2% 100.7 2.1 25.6 4.92

March 26.4 52.4% 100.4 2.2 29.3 5.59

April 27.1 69.5% 100.2 2.5 29.1 5.76

May 27.6 78.0% 99.9 2.5 29.2 5.30

June 27.9 84.5% 99.5 2.4 28.7 4.53

July 27.7 86.3% 99.6 2.2 28.1 4.23

August 27.6 85.7% 99.7 1.9 28.1 4.29

September 27.0 84.7% 100.0 1.7 27.5 4.02

October 25.5 80.1% 100.4 1.5 26.0 4.32

November 22.5 72.8% 100.8 1.6 22.9 4.28

December 20.2 61.0% 101.0 1.7 21.1 4.21

65

Table 6.8 ANN model error and accuracy for irradiation

Months

Actual

Irradiation

(KWh/m2/day)

Predicted (ANN)

Irradiation

(KWh/m2/day)

Error (%) Accuracy (%)

January 4.36 4.3616 0.036697 99.9633

February 4.92 4.8206 2.020325 97.97967

March 5.59 5.5901 0.001789 99.99821

April 5.76 5.6227 2.383681 97.61632

May 5.30 5.3005 0.009434 99.99057

June 4.53 4.5319 0.041943 99.95806

Jully 4.23 4.234 0.094563 99.90544

August 4.29 4.29 0 100

September 4.02 4.2992 6.945274 93.05473

October 4.32 4.3201 0.002315 99.99769

November 4.28 4.28 0 100

December 4.21 4.3373 3.023753 96.97625

Table 6.9 Performance of the ANN model

Output RMSE R2 A (%)

Irradiation 0.318129 0.999535 98.78669

66

Figure 6.9: Performance regression plot for predictive model

Figure 6.10 Comparison graph of ANN and Fuzzy logic with actual irradiation

67

CHAPTER 7

Results and Discussion

7.1 Performance analysis of 80 KWp PV solar plant

The performance results of the 80KWp PV system power plant discussed in this section.

In the mathematical model section, different performance indicators were discussed. Table 7.1

shows the monthly energy generation and different performance indicators results. Figure 7.1 and

Figure 7.2 show the monthly generation and specific yield of 80KWp plant in two years and it can

be observed that maximum generation was found in April due to maximum irradiation and

minimum in December, 2016 due to winter session and less irradiation. In 2016, average energy

generation and specific yield were found 6140.76 KWh and 77.76 respectively. In 2017, average

generation and specific yield were found 4354.51 KWh and 54.43. These results show that in 2016

the plant utilized 77.76% capacity of the plant and in 2017, the plant utilized 54.43% capacity of

the plant.

68

Table 7.1 Monthly energy generation and performance indicators of 80KWp plant

Month Generation(KWh) Specific Yield (SY) Performance Ratio

Jan 16 5184.60 64.81 0.60

Feb 16 5965.70 74.57 0.62

Mar 16 7488.70 93.61 0.63

Apr 16 7718.60 97.48 0.68

May 16 6952.00 87.90 0.66

Jun 16 6860.00 85.75 0.79

Jul 16 5734.90 71.69 0.70

Aug 16 6505.40 81.32 0.77

Sep 16 4978.20 62.23 0.65

Oct 16 6009.00 75.11 0.68

Nov 16 5918.41 73.98 0.70

Dec 16 4374.09 54.68 0.54

Jan 17 4017.91 50.22 0.47

Feb 17 4052.50 50.66 0.43

Mar 17 6055.90 75.70 0.51

Apr 17 6039.40 75.49 0.54

May 17 6720.90 84.01 0.64

Jun 17 4195.20 52.44 0.48

Jul 17 3539.10 44.24 0.43

Aug 17 3390.09 42.38 0.40

Sep 17 3149.00 39.36 0.41

Oct 17 3544.00 44.30 0.40

Nov 17 4000.61 50.01 0.47

Dec 17 3549.50 44.37 0.44

69

Figure 7.1 Monthly generation of 80KWp solar plant.

Figure 7.2 Monthly specific yield of 80KWp plant

Comparing the year 2016 and 2017 it can be observed that in 2016 energy generation is

higher than 2017 because of 1 year depreciation. The specified module efficiency of 13%

mentioned in the PV solar panel manual has been used in the calculation of nominal plant output

to determine the performance ratio.

Figure 7.3 shows the monthly performance ratio of the 80 KWp plant. It can be observed

that in June and August, 2016 performance ratios are highest and plant was utilized 79% nominal

0

20

40

60

80

100

120

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SY-2016 SY-2017

Sp

ecif

ic Y

ield

(S

Y)

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

8000.00

9000.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Generation(KWh)-2016 Generation (kWh)-2017

Gen

erat

ion (

KW

h)

70

capacity in June and 77% nominal capacity in August 2016. Again it can be observed that in 2017

performance ratio is less than 2016. In 2016 average performance ratio was found 0.67 and in 2017

average performance ratio was found 0.47. These results show that average 67% irradiation was utilized in

2016 and 47% irradiation was utilized in 2017.

Figure 7.3 Monthly performance ratio (PR) of the 80KWp plant.

7.2 Performance analysis of 30KWp PV solar plant

This section describes the evaluated performance of 30 KWp roof-top PV system. Table

7.2 shows the monthly energy generation and different performance parameters results. In 2016,

average generation of energy and specific yield were found 2333.48KWh and 77.78 respectively.

In 2017, average generation of energy and specific yield were found 2114.44KWh and 70.48

respectively. These values interpreted that in 2016 the plant utilized 77.8 % of plant capacity and

in 2017 the plant utilized 70.48% of plant capacity. Comparing 2016 and 2017, in 2016 the PV

system plant showed good performance than 2016. Figure 7.4 and Figure 7.5 represent the

monthly energy generation and specific yield of this plant. It was observed that in April 2016 and

May 2017 energy generation and specific yield are maximum due to good solar irradiance and

environment.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

J AN FEB MAR AP R MAY J UN J UL AUG SEP OCT NOV DEC

PR-2016 PR-2017

Per

form

ance

rat

io (

PR

)

71

Table 7.2 Monthly energy generation and performance indicators results of 30KWp plant

Month Generation (KWh) Specific yield Performance Ratio

Jan 16 1915.80 63.86 0.59

Feb 16 2224.00 74.13 0.61

Mar 16 2807.60 93.59 0.62

Apr 16 2884.50 97.15 0.68

May 16 2592.40 87.41 0.65

Jun 16 2554.30 85.14 0.78

Jul 16 2137.10 71.20 0.69

Aug 16 2407.70 80.22 0.76

Sep 16 1845.50 61.52 0.64

Oct 16 2183.40 72.78 0.65

Nov 16 2292.40 77.41 0.72

Dec 16 2159.10 71.97 0.70

Jan 17 2013.70 67.12 0.62

Feb 17 1543.30 51.44 0.44

Mar 17 1907.10 63.57 0.42

Apr 17 2570.70 85.69 0.60

May 17 2862.60 95.42 0.72

Jun 17 2102.10 70.07 0.64

Jul 17 1952.10 65.07 0.63

Aug 17 2050.90 68.36 0.65

Sep 17 1884.30 62.81 0.65

Oct 17 2097.30 69.88 0.63

Nov 17 2354.50 78.48 0.74

Dec 17 2035.70 67.86 0.66

72

Figure 7.4 Monthly energy generation of 30KWp PV system.

Figure 7.5 Monthly specific yield of 30KWp PV system.

Figure 7.6 shows the performance ratio of 30 KWp PV system plant in 2016 and 2017. Comparing

in 2016 and 2017, it can be observed that in 2016, the plant exhibited good performance and utilized

maximum solar irradiance. In June 2016 the plant utilized maximum solar irradiance. The average

performance ratio in 2016 was found 0.67 which showed that only 67% nominal capacity utilized the plant.

And again in 2017 the average performance ratio was found 0.62. This result showed that the plant utilized

62% nominal capacity of the plant.

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

3500.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Generation (KWh)-2016 Generation (KWh)-2017

Gen

erat

ion

(KW

h)

0.00

20.00

40.00

60.00

80.00

100.00

120.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SY-2106 SY-2017

Sp

ecif

ic Y

ield

73

Figure 7.6 Performance ratio of 30KWp PV system.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PR-2016 PR-2017

Per

form

ance

rat

io

74

7.3 Performance analysis of 6 plants in 2016

This section discussed about performance of 6 plants and each of the plant has 20KWp

capacity. Table 7.3 shows the monthly energy generation (KWh) and Table 7.4 shows average

energy generation (KWh) of six plants in 2016. Figure 7.7 shows the column charts of 6 plants.

Table 7.3 Monthly energy generation of 6 plants in 2016

Month Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Jan 737.50 1241.90 238.60 263.20 765.70 917.30

Feb 722.00 1643.60 535.40 362.60 1650.50 1139.20

Mar 532.40 1181.60 643.30 694.30 2044.00 1329.65

Apr 509.80 807.36 373.30 877.40 1278.90 1342.11

May 612.10 1114.74 343.50 1022.60 1964.00 1267.94

Jun 638.00 1033.60 312.90 412.70 1231.50 1143.30

Jul 555.70 785.10 275.20 174.40 187.80 970.10

Aug 433.00 831.60 337.70 345.90 1043.90 1202.40

Sep 419.70 837.60 268.40 200.00 233.90 951.10

Oct 605.60 1164.50 361.10 285.00 330.00 606.00

Nov 585.10 1158.00 349.70 414.10 540.00 355.40

Dec 365.40 1247.70 288.60 1010.30 390.90 333.50

Table 7.4 Average energy generation of 6 plants in 2016

Plants Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Average 559.69 1087.11 360.56 505.21 971.67 963.08

75

Figure 7.7 Monthly energy generation (KWh) of 6 plants in 2016.

It was observed from Table 7.4 that average generation in 2016 of the plant Bogura

Cantonment is maximum (971.657KWh) and the plant of the Chattogram Cantonment is minimum

(360.55KWh). It was found that in March average energy generation is maximum 1070.85 KWh

of six plants and in September average generation is minimum 484.95 KWh. Figure 7.8 shows the

specific yield of 6 plants. It was observed that maximum fluctuation was found in the Savar

cantonment plant. The generated average energy and specific yield are 559.69KWh and 671.01

respectively.

Figure 7.8 Monthly specific yield of six plants in 2016

0.00

500.00

1000.00

1500.00

2000.00

2500.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur CantonmentG

ener

atio

n (

KW

h)

0

20

40

60

80

100

120

J AN F E B M AR AP R M AY J U N J U L AU G S E P O C T N O V D E C

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur Cantonment

Sp

ecif

ic Y

ield

76

Table 7.5 Monthly performance ratio of six plants in 2016

Month Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Jan 0.32 0.55 0.10 0.12 0.34 0.44

Feb 0.28 0.63 0.21 0.14 0.64 0.48

Mar 0.17 0.37 0.24 0.22 0.65 0.45

Apr 0.17 0.26 0.13 0.30 0.41 0.46

May 0.22 0.38 0.12 0.37 0.65 0.44

Jun 0.28 0.43 0.14 0.18 0.52 0.50

Jul 0.26 0.36 0.12 0.08 0.08 0.47

Aug 0.20 0.37 0.14 0.15 0.46 0.56

Sep 0.21 0.41 0.12 0.09 0.11 0.47

Oct 0.26 0.47 0.15 0.12 0.14 0.25

Nov 0.26 0.49 0.16 0.19 0.24 0.16

Dec 0.17 0.57 0.13 0.47 0.18 0.16

Table 7.6 Average performance ratio of six plants in 2016

Plants Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Average 0.23 0.44 0.15 0.20 0.37 0.40

Table 7.5 and Table 7.6 show the monthly performance ratio and average performance

ratio of six plants respectively. Figure 7.9 shows monthly performance ratio of six plant in 2016.

It was observed that average performance ratio of the Bogura Cantonment is highest it means this

plant utilized maximum nominal capacity or irradiance. The plant of the Chattogram Cantonment

shows minimum utilization (15%) of nominal capacity. It can be seen the Savar Cantonment plant

exhibits maximum fluctuation of performance ratio. Maximum average utilization of all plant is

in February (40%).

77

Figure 7.9 Monthly performance ratio of six plant in 2016.

7.4 Performance analysis of 6 plants in 2017

Table 7.7 Monthly energy generation of 6 plants in 2017

Month Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Jan 587.60 1348.40 279.60 1370.10 385.60 290.36

Feb 615.66 1034.60 367.70 1403.20 783.80 233.40

Mar 720.60 948.10 382.10 1621.40 1915.10 624.80

Apr 767.24 1277.44 265.70 1323.00 1857.90 591.10

May 859.50 857.56 250.10 1734.00 2071.80 321.99

Jun 391.60 162.10 215.30 1454.00 1549.70 459.33

Jul 690.90 320.00 160.80 1260.90 1087.90 481.95

Aug 855.70 365.20 107.30 1287.80 1413.50 481.95

Sep 562.10 328.00 130.36 1127.60 1597.70 450.98

Oct 553.14 904.00 152.39 1345.00 1383.40 578.80

Nov 759.50 451.40 184.06 1587.00 1565.80 608.10

Dec 689.66 410.66 192.26 1273.00 1454.40 429.90

Table 7.8 Average energy generation (KWh) of 6 plants in 2017

Plants Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Average 671.02 700.54 223.89 1398.92 1422.13 462.72

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

J A N F E B M A R A P R M A Y J U N J U L A U G S E P O C T N O V D E C

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur Cantonment

Per

form

ance

Rat

io

78

Figure 7.10 Monthly energy generation (KWh) of 6 plants in 2017

Table 7.7 and Table 7.8 represent the monthly energy generation as shown in Figure 7.10

and average energy generation in 2017 of six plants respectively. It can be seen that Jashore

Cantonment plant shows maximum average energy generation 1422.13 KWh and the Chattogram

Cantonment plant shows minimum average energy generation in 2017. Both the year 2016 and

2017, the Chattogram Cantonment plant exhibited minimum energy generation due to less solar

irradiance comparing to others plants. Figure 7.11 shows the specific yield of six plants in 2017.

Again it can be seen that Savar Cantonment exhibits maximum fluctuation of energy generation.

Figure 7.11 Monthly specific yield of six plants in 2017

0.00

500.00

1000.00

1500.00

2000.00

2500.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur Cantonment

Gen

erat

ion

(K

Wh

)

0

20

40

60

80

100

120

J A N F E B M A R A P R M A Y J U N J U L A U G S E P O C T N O V D E C

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur Cantonment

Sp

ecif

ic Y

ield

79

Table 7.9 and Table 7.10 show the monthly performance ratio and average performance ratio of

six plants respectively. It was observed that average performance ratio of Rangpur Cantonment is

highest it means this plant utilized maximum nominal capacity or irradiance. Again the plant

Chattogram Cantonment shows minimum utilization (15%) of nominal capacity. Also again it can

be seen the Savar Cantonment plant exhibits maximum fluctuation of performance ratio.

Maximum average utilization of all plant is in November (38%). Comparing 2016 and 2017,

overall performance of these six plants is good in 2017 (33% PR). Figure 7.12 shows the monthly

performance ratio (PR) of the six plants in 2017.

Table 7.9 Monthly performance ratio of six plants in 2017

Month Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Jan 0.26 0.60 0.12 0.60 0.17 0.14

Feb 0.25 0.41 0.15 0.57 0.32 0.10

Mar 0.23 0.29 0.14 0.52 0.61 0.21

Apr 0.26 0.41 0.09 0.45 0.59 0.20

May 0.31 0.29 0.09 0.63 0.68 0.11

Jun 0.17 0.07 0.10 0.64 0.65 0.20

Jul 0.32 0.15 0.07 0.55 0.48 0.24

Aug 0.39 0.16 0.05 0.55 0.62 0.22

Sep 0.28 0.16 0.06 0.52 0.74 0.22

Oct 0.24 0.37 0.06 0.57 0.57 0.23

Nov 0.34 0.19 0.08 0.72 0.70 0.27

Dec 0.32 0.19 0.09 0.59 0.67 0.21

Table 7.10 Average performance ratio of six plants in 2017

Plants Savar

Cantonment

Bogura

Cantonment

Chattogram

Cantonment

Cumilla

Cantonment

Jashore

Cantonment

Rangpur

Cantonment

Average 0.23 0.44 0.15 0.20 0.37 0.40

It was observed that in 2017, Cumilla cantonment exhibited highest performance (58%) comparing to others

plant.

80

Figure 7.12 Monthly performance ratio of six plant in 2017

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

J A N F E B M A R A P R M A Y J U N J U L A U G S E P O C T N O V D E C

Savar Cantonment Bogura Cantonmemt Chattogram Cantonment

Cumilla Cantonment Jashore Cantonment Rangpur Cantonment

Per

form

ance

Rat

io (

PR

)

81

Table 7.11 Performance summary of some selected grid connected PV systems.

Reference Location PV-

Type

System

Size

(Kwp)

Final Yeild

(kwh/kwp/day)

PV module

efficiency(%)

Inverter

Efficiency

Performance

ratio

[48] Dublin,

Ireland

Mc-Si - 2.4 14.9 89.20 81.5

[49] Poland a-si - - 6.0 93.00 80.00

[50] Crete,

Greece

P-Si - 1.96-5.07 15.0 - 67.36

[51] Thailand - - 2.91-3.98 12.0 92.16 70.00

[52] Dublin,

Ireland

- 3.056 1.69 7.60 75.00 0.6-0.62

[53] Italy P-Si - 3.40 7.95 98.0 -

[54] Spain Isofoton

I-106

2.5 1.60 5.71 87.03 49

[55] South

Africa

P-Si 3.2 4.90 13.72 88.10 64.30

[56] Spain - 2.0 3.80 8.50 88.00 64.00

[57] Singapore P-Si - 3.12 11.80 - 81.00

[58] Abu

Dhabi

a-Si/P-

Si

142.5 - - 94.80 -

[59] India P-Si 20 4.1 13.71 - 85.00

[60] India P-Si 1816 - 8.76 - 63.58

[61] India - 50 - - - 55-89

[62] India Mc-Si 80 4.45 15.53 - 83.2

[63] India 3MWp 3.75 - - 0.70

[64] India c-Si/a-

Si

110 2.67-3.36 - 93.5-97.5 71.6-79.5

[65] India 5MWp 4.81 6.08 88.2 -

[66] India P-Si 10MWp 1.96-5.07 13.2 97.0 86.12

Present

Study

AHQ,

Dhaka

c-Si 80 2.53 13 97.7 66

Present

Study

AFD,

Dhaka

c-Si 30 2.59 13 97.7 67

82

CHAPTER 8

Conclusion and Recommendation

Photovoltaic energy is considered as one of the most promising renewable energy technologies.

The grid connected seven plants of PV system installed on the roof top constituent of various

locations of Bangladesh Army was monitored in the year of 2016 and 2017. The performance of

the PV system was compared with that of other heuristic models as well as grid connected PV

systems installed across the globe as shown in Table 7.11. The salient findings from the study are

summarized below.

i. Experimental investigation shows that changing the angles of the solar panels can lead up

to 12.56 percent increase in energy than a fixed angle as shown in Table 5.1.

ii. The accuracy of fuzzy logic model was obtained 97.47% as described in Table 6.6 and the

accuracy of ANN model was obtained 98.78% with the actual irradiation as shown in Table

6.9.

iii. This study found that performance of ANN model is better than the fuzzy logic model for

solar irradiation prediction.

iv. This predictive model of fuzzy logic and ANN will be helpful for researchers and engineers

for design and planning of solar plants.

v. To derive maximum efficiency, design of the subject Photovoltaic system needs to be

reviewed.

vi. From the analysis, it is observed that the tilt angle of the solar panels needs to be changed

4 times a year variance that produces maximum power. Should it be difficult then the angle

of the solar panels to be changed minimum twice a year to increase energy efficiency.

Future research investigations can be conducted on mapping of solar potential over Bangladesh

along with the fuzzy logic and ANN model.

Looking into the overall performance of the of the installed roof top solar PV systems, It is found

to be a feasible solution for power supply in Bangladesh. Such PV system can be installed in off-

grid remote locations.

83

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88

ANNEXURE

A-1

Sample Calculation:

Since each of the PV cell has 250 KW capacity and 32 PV cells were used.

Total capacity=250*32=8000KW

Specific Yield (SY):

From the equation 33(2) as described in chapter 3

Sy =Eout

Eplate

For January 2016,

Sy =5184.60

8000=64.81

Similarly, other months were calculated as shown in Table 7.1

Performance Ratio:

From the equation (3) as described in chapter 3

PR =Eactual

Enominal

and from equation (4)

Enominal = Ir ∗ ηpv

where,

89

Ir=4.29 KWh/m2/day (for January as shown in Table 2.2)

Since, in 80KWp plant 320 number of PV cells were used and each

of the cell has 1.5509 m2 area. So total incidence irradiation is

Ir =4.29*31*320*1.55=66003.84 KWh

and ηpv=13% as shown in Table 4.3

Enominal = 66003.84 ∗ 0.13 = 8580.499 K

So,

Performance ratio, PR =5184.64

8580.499= 0.60

Similarly, the performance ratios of other months were calculated as shown in Table 7.1