performance analysis of photovoltaic system using
TRANSCRIPT
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
REFERENCE
[1] Amin, S. B., Murshed, M., & Tul Jannat, F. (2017). How can Bangladesh prepare for the new
era of Global Energy Transition?
[2] Hatziargyriou, N., Asano, H., Iravani, R., & Marnay, C. (2007). Microgrids. IEEE power and
energy magazine, 5(4), 78-94.
[3] e. a. D.L. King, “Photovoltaic array performance model,” Sandia National Laboratory , New
mexico, November, 2003.
[4] L. Ayomp, "Validated Real-time Energy Models for Small-Scale Grid-Connected PV-
Systems," no. Dublin Institute of Technology, 2010.
[5] “SOPHIA, European Research infrastructure,” [Online]. Available: http://www.sophia-ri.eu/.
[6] Google Map “Army Head Quarter’, www.google.com/ maps/search/dhaka+army+head quarter
[7] M. A. Rosen, “The role of energy efficiency in sustainable development,” Technol Soc, pp.
21– 26, 1996.
[8] McCollum, D. L., Echeverri, L. G., Busch, S., Pachauri, S., Parkinson, S., Rogelj, J., ... &
Riahi, K. (2018). Connecting the Sustainable Development Goals by their energy inter-
linkages. Environmental Research Letters, 13(3), 033006.
[9] “People got electricity in Bangladesh” http://www.daily-sun.com/post/233903/80-percent-
people-in-Bangladesh-now-get-electricity-
[10]http://bforest.portal.gov.bd/sites/default/files/files/bforest.portal.gov.bd/notices/c3379d22_ee
62_4dec_9e29_75171074d885/19.%20Power_NCS.pdf
[11] BBS (Bangladesh Bureau of Statistics) Statistical Year Book 2011. Government of
Bangladesh, Sher-e-Banglanagor, Agargaon, Dhaka, Bangladesh; 2011
[12] Bahauddin KM, Salahuddin TM. Prospect and trend of renewable energy and its technology
towards climate change mitigation and sustainable development in Bangladesh. Int J Adv Renew
Energy Res 2012;1:158.
[13] “Banngladesh Toward 100% Renewable Energy” www.dhakatribune.com/tribune-
supplements/tribune-climate/2017/08/12/
[14] Fahim Hasan, Zakir Hossain, Maria Rahman, Sazzad Ar Rahman, ‚Design and Development
of a Cost Effective Urban Residential So-lar PV
[15] Kazy Fayeen Shariar, Enaiyat Ghani Ovy, Kazi Tabassum Aziz Hoss-ainy, ‚Closed
Environment Design of Solar Collector Trough using lenss and reflectors,‛ World Renewable
Energy Congress 2011, Swe-den.
84
[16]Development of Renewable Energy Technologies by BPDB,
www.bpdb.gov.bd/bpdb/index.php?option=com_content&view=article&id=26
[17] Markvart, T. (Ed.). (2000). Solar electricity (Vol. 6). John Wiley & Sons.
[18] G. Boyle. Renewable Energy: Power for a Sustainable Future, 2nd ed. Oxford, UK: Oxford
University Press, 2004.
[19] Mr.Solar. (August 13, 2015). Photovoltaic Effect [Online]. Available:
http://www.mrsolar.com/photovoltaic-effect/
[20] Created internally by a member of the Energy Education team. Adapted from: Ecogreen
Electrical. (August 14, 2015). Solar PV Systems [Online]. Available:
http://www.ecogreenelectrical.com/solar.htm
[21] Planning and installing photovoltaic systems, "A guide for installers, architects and
engineers", Deutsche Gesellschaft für sonnenenergie (DGS LV Berlin BRB). , 2008,
www.earthscan.co.uk.
[22] "Electropaedia, Solar Power (Technology and Economics)," Woodbank Communications
Ltd, Jan 2012. [Online]. Available: http://www.mpoweruk.com/solar_power.html.
[23] "Guide To Interpreting I-V Curve Measurements of PV Array," The Solmetric ,
http://www.solmetric.com, March, 2011.
[24] S. B. Christiana Honsberg, "pvcdreom," Jan 2012. [Online]. Available:
http://pveducation.org/pvcdrom.
[25] "Solar facts and advice," Alchemie Limited Inc, Feb 2012. [Online]. Available:
http://www.solarfacts-and-advice.com
[26] D.-I. V. Quaschning, Simulation der Abschattungsverluste bei solarelektrischen Systemen,
Berlin , 1996.
[27] "Weatherbase: Historical Weather for Dhaka, Bangladesh". weatherbase.com. Retrieved
2008-12-15.
[28] Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. S., & Choi, K. (2013). Simulating land
cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote
Sensing, 5(11), 5969-5998.
[29] Meteorological Data collected from NASA “https://www.gaisma.com/en/dir/bd-country.html
[30] Dincer, I., & Rosen, M. A. (1998). A worldwide perspective on energy, environment and
sustainable development. International Journal of Energy Research, 22(15), 1305-1321.
[31] Pillai, I. R., & Banerjee, R. (2009). Renewable energy in India: Status and potential. Energy,
34(8), 970-980.
[32] Sharma, A. (2011). A comprehensive study of solar power in India and World. Renewable
and Sustainable Energy Reviews, 15(4), 1767-1776.
85
[33] Sharma, V., & Chandel, S. S. (2013). Performance analysis of a 190 kWp grid interactive
solar photovoltaic power plant in India. Energy, 55, 476-485.
[34] Anik, D.; Mahmud, A.M.B.; Arefin, N. Bangladesh prospects of solar energy in Bangladesh.
IOSR-JEEE 2013, 4, 46–57
[35] Karafil, A., Ozbay, H., Kesler, M., & Parmaksiz, H. (2015, November). Calculation of
optimum fixed tilt angle of PV panels depending on solar angles and comparison of the results
with experimental study conducted in summer in Bilecik, Turkey. In Electrical and Electronics
Engineering (ELECO), 2015 9th International Conference on (pp. 971-976). IEEE.
[36] Bai, Y. and D. Wang, Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and
defuzzifications, in Advanced Fuzzy Logic Technologies in Industrial Applications. 2006, Springer.
p. 17-36
[37] Halabi, L. M., Mekhilef, S., & Hossain, M. (2018). Performance evaluation of hybrid adaptive
neuro-fuzzy inference system models for predicting monthly global solar radiation. Applied
Energy, 213, 247-261.
[38] Mohammadi, K., Shamshirband, S., Tong, C. W., Alam, K. A., & Petković, D. (2015).
Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of
the year. Energy Conversion and Management, 93, 406-413.
[39] Kumar, A., et al., Analysis of machining characteristics in additive mixed electric discharge
machining of nickel-based super alloy Inconel 718. Materials and Manufacturing Processes, 2011.
26(8): p. 1011-1018.
[40] Zalnezhad, E. and A.A. Sarhan, A fuzzy logic predictive model for better surface roughness
of Ti–TiN coating on AL7075-T6 alloy for longer fretting fatigue life. Measurement, 2014. 49: p.
256-265.
[41] Topcu, I.B. and M. Sarıdemir, Prediction of compressive strength of concrete containing fly
ash using artificial neural networks and fuzzy logic. Computational Materials Science, 2008.
41(3): p. 305-311.
[42] MacDonald, D., Charles, D., & Fyfe, C. (1999). Neural networks which identify composite
factors. In ESANN (pp. 281-288).
[43] A. K. Yadav and S. S. Chandel. “Solar radiation prediction using Artificial Neural Network
techniques: A review,” Ren. Sust. En. Reviews, vol. 33, pp. 772-781, 2014.
86
[44] A. K. Yadav, H. Malik, and S. S. Chandel. “Selection of most relevant input parameters using
WEKA for artificial neural network based solar radiation prediction models,” Ren. Sust. En.
Reviews, vol. 31, pp. 509-519, Jan 2014.
[45] O. Kisi. “Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic
approach,” Energy, vol. 64, pp. 429-436, Oct 2013.
[46] D. I. Egeonu, H. O. Njoku, P. N. Okolo, and S. O. Enibe. “Comparative Assessment of
Temperature Based ANN and Angstrom Type Models for Predicting Global Solar Radiation,” In
Afro-European Conference for Industrial Advancement, vol. 334, pp. 109-122, 2015.
[47] O. �enkal. “Solar radiation and precipitable water modeling for Turkey using artificial neural
networks,” Meteoro. Atmos. Physics, vol. 127, pp 1-8, Aug 2015.
[48] Ayompe, L. M., Duffy, A., McCormack, S. J., & Conlon, M. (2011). Measured performance
of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy conversion and
management, 52(2), 816-825.
[49] Pietruszko, S. M., & Gradzki, M. (2003). Performance of a grid connected small PV system
in Poland. Applied energy, 74(1-2), 177-184.
[50] Kymakis, E., Kalykakis, S., & Papazoglou, T. M. (2009). Performance analysis of a grid
connected photovoltaic park on the island of Crete. Energy Conversion and Management, 50(3),
433-438.
[51] Chokmaviroj, S., Wattanapong, R., & Suchart, Y. (2006). Performance of a 500 kWP grid
connected photovoltaic system at Mae Hong Son Province, Thailand. Renewable Energy, 31(1),
19-28.
[52] Mondol, J. D., Yohanis, Y., Smyth, M., & Norton, B. (2006). Long term performance analysis
of a grid connected photovoltaic system in Northern Ireland. Energy Conversion and Management,
47(18-19), 2925-2947.
[53] Cucumo, M., De Rosa, A., Ferraro, V., Kaliakatsos, D., & Marinelli, V. (2006). Performance
analysis of a 3 kW grid-connected photovoltaic plant. Renewable Energy, 31(8), 1129-1138.
[54] Drif, M., Pérez, P. J., Aguilera, J., Almonacid, G., Gomez, P., De la Casa, J., & Aguilar, J. D.
(2007). Univer Project. A grid connected photovoltaic system of 200kWp at Jaén University.
Overview and performance analysis. Solar Energy Materials and Solar Cells, 91(8), 670-683.
[55] Okello, D., Van Dyk, E. E., & Vorster, F. J. (2015). Analysis of measured and simulated
performance data of a 3.2 kWp grid-connected PV system in Port Elizabeth, South Africa. Energy
conversion and management, 100, 10-15.
[56] Sidrach-de-Cardona, M., & Lopez, L. M. (1999). Performance analysis of a grid-connected
photovoltaic system. Energy, 24(2), 93-102.
87
[57] Wittkopf, S., Valliappan, S., Liu, L., Ang, K. S., & Cheng, S. C. J. (2012). Analytical
performance monitoring of a 142.5 kWp grid-connected rooftop BIPV system in Singapore.
Renewable Energy, 47, 9-20.
[58] Al Ali, M., & Emziane, M. (2013). Performance analysis of rooftop PV systems in Abu Dhabi.
Energy Procedia, 42, 689-697.
[59] Vasisht, M. S., Srinivasan, J., & Ramasesha, S. K. (2016). Performance of solar photovoltaic
installations: Effect of seasonal variations. Solar Energy, 131, 39-46.
[60] Pundir, K. S. S., Varshney, N., & Singh, G. K. (2016). Comparative study of performance of
grid connected solar photovoltaic power system in IIT Roorkee campus. In Paper of international
conference on innovative trends in science, engineering and management held at New Delhi, India
(pp. 422-31).
[61] Kamalapur, G. D., & Udaykumar, R. Y. (2011). Rural electrification in India and feasibility
of photovoltaic solar home systems. International Journal of Electrical Power & Energy Systems,
33(3), 594-599.
[62] Kumar, S. S., & Nagarajan, C. (2016). Performance-economic and energy loss analysis of 80
KWp grid connected roof top transformer less photovoltaic power plant. Circuits and Systems,
7(06), 662.
[63] Padmavathi, K., & Daniel, S. A. (2013). Performance analysis of a 3 MWp grid connected
solar photovoltaic power plant in India. Energy for Sustainable Development, 17(6), 615-625.
[64] Shukla, A. K., Sudhakar, K., & Baredar, P. (2016). Simulation and performance analysis of
110 kWp grid-connected photovoltaic system for residential building in India: A comparative
analysis of various PV technology. Energy Reports, 2, 82-88.
[65] Sundaram, S., & Babu, J. S. C. (2015). Performance evaluation and validation of 5 MWp grid
connected solar photovoltaic plant in South India. Energy conversion and management, 100, 429-
439.
[66] Kumar, B. S., & Sudhakar, K. (2015). Performance evaluation of 10 MW grid connected solar
photovoltaic power plant in India. Energy Reports, 1, 184-192.
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