Content uploaded by Sanka Chandrasiri
Author content
All content in this area was uploaded by Sanka Chandrasiri on Mar 27, 2019
Content may be subject to copyright.
TEMPERATURE EFFECT ON SOLAR PHOTOVOLTAIC POWER
GENERATION
M. D. S. D. Chandrasiri
University of Sri Jayewardenepura, Sri Lanka
B.Sc. (Honors) in App Sci 2016 Januay 2017
TEMPERATURE EFFECT ON SOLAR PHOTOVOLTAIC POWER
GENERATION
A dissertation
Submitted to
The Department of Physics of the
University of Sri Jayewardenepura
In partial fulfillment of the
requirement for the
Bachelor of Science (Honors) Degree in Applied Sciences
In the field of Physics
by
M. D. S. D. Chandrasiri
University of Sri Jayewardenepura, Sri Lanka
January 2017
iii
DECLARATION
The work described in this dissertation was carried out by me in collaboration with Sri Lanka
Sustainable Energy Authority and the Department of Physics, University of Sri
Jayewardenepura, Sri Lanka under the guidance of Dr. M. L. C. Attyagalle and has not been
submitted elsewhere.
……………………… ……………………………….
M. D. S. D. Chandrasiri Date of submission
……………………………………… …………………………………….
Dr. M. L. C. Attyagalle Mr. H. Wickramasinghe
Internal supervisor External supervisor
(University of Sri Jayewardenepura) (Sri Lanka Sustainable Energy Authority)
………………………………………
Dr. W. D. A. T. Wijeratne
Head/Department of Physics
University of Sri Jayewardenepura
………………………………………
Prof. Sudantha Liyanage
Dean/Faculty of Applied Sciences
University of Sri Jayewardenepura
iv
TABLE OF CONTENTS
DECLARATION ..................................................................................................................... iii
LIST OF TABLES ................................................................................................................... vi
LIST OF FIGURES ................................................................................................................ vii
ACKNOWLEDGEMENT ..................................................................................................... viii
ABBREVIATIONS ................................................................................................................. ix
ABSTRACT .............................................................................................................................. x
CHAPTER 1 ............................................................................................................................. 1
INTRODUCTION ................................................................................................................. 1
1.1.1 Background ......................................................................................................... 2
1.1.2 Description about photovoltaic modules ............................................................ 3
1.1.3 The Solemetric PV analyzer tool kit ................................................................... 5
LITERATURE REVIEW ...................................................................................................... 7
1.2.2 PV power output dependence on module/cell operating temperature ................ 9
1.2.3 PV module/cell cooling techniques .................................................................. 10
1.2.4 The heating rate of the PV module/cell ............................................................ 11
1.2.5 Annual PV potential in Sri Lanka ..................................................................... 12
CHAPTER 2 ........................................................................................................................... 13
MATERIALS AND METHODS ........................................................................................ 13
2.1 General .......................................................................................................................... 13
2.2 Experimental setup ........................................................................................................ 13
2.1 Experimental procedure ................................................................................................ 19
2.1.1. Scope limitation ..................................................................................................... 20
2.1.2. Data collecting using Solemetric PV analyzing software ..................................... 21
2.1.3. Setting a mathematical function using Matlab ...................................................... 24
CHAPTER 3 ........................................................................................................................... 27
3. RESULTS AND DISCUSSION ..................................................................................... 27
3.1 The Hambanthota area data (12/12/2016) ................................................................ 27
3.2 The Colombo 07 area data (14/12/2016).................................................................. 29
3.3 The relationship between power/irradiation and PV module temperature ............... 32
v
3.4 The relationship between power and PV module temperature when the cooling
system is active.................................................................................................................... 33
3.5 The relationship between maximum power, PV module temperature and irradiation.
35
3.6 The summarized power/irradiation vs. temperature graphs. .................................... 39
CHAPTER 4 ........................................................................................................................... 41
4.1 Conclusion ..................................................................................................................... 41
4.2 Suggestion for future works .......................................................................................... 43
4.3 References ..................................................................................................................... 46
4.4 Appendices .................................................................................................................... 47
4.4.1. Appendix 1 – The Matlab programming code for calculating minimum and
maximum values of the polynomial of power/irradiation vs. temperature ...................... 47
4.4.2. Appendix II – The programming code of the Arduino UNO cooling system. ...... 49
vi
LIST OF TABLES
Table 2.1: The electrical and physical characteristics of the Mitsubishi PV module used in the
Hambanthota solar power plant……………………………………………………………. (14)
Table 2.2: The electrical and physical characteristics of the Canadian solar PV module used in
the Colombo 07……………………………………………………………………………. (15)
Table 2.2. The experiments dates………………………………………………………….. (19)
Table 2.4: The data of figure 2.11 and figure 2.12………………………………………… (24)
vii
LIST OF FIGURES
FIGURE1.1 CHART OF SOLAR CELL TO PV SYSTEM. .................................................................... 3
FIGURE 1.1: THE SOLEMETRIC ANALYZER PV TOOL KIT ............................................................. 6
FIGURE 1.2: SCREENSHOT OF THE SOLEMETRIC PV ANALYZING SOFTWARE .............................. 6
FIGURE 1.3: PV CHARACTERISTIC CURVE .................................................................................. 7
FIGURE 1.3: ANNUAL PV POTENTIAL IN SRI LANKA ................................................................ 12
FIGURE 2.1: THE EXPERIMENTAL SETUP OF SOLMETRIC TOOLS AND PV MODULE. ................... 16
FIGURE 2.2: THE MOUNTED SOLEMETRIC PYRANOMETER ........................................................ 17
FIGURE 2.3: THE TAPED SIDE OF THE THERMOCOUPLE .............................................................. 17
FIGURE 2.4: THE CONNECTED SIDE OF THE THERMOCOUPLE ..................................................... 17
FIGURE 2.5: THE PVC WATER NOZZLE SPRAYING THE WATER DROPLETS ................................. 18
FIGURE 2.6: THE FINAL EXPERIMENTAL SETUP ......................................................................... 19
FIGURE 2.7: THE PV ANALYZING SOFTWARE “READY” BUTTON .............................................. 21
FIGURE 2.8: THE NEW PROJECT WIZARD .................................................................................. 21
FIGURE 2.9: THE PV MODULE CHARACTERISTIC WIZARD ........................................................ 22
FIGURE 2.10: THE PV ARRAY NAVIGATOR WIZARD ................................................................. 22
FIGURE 2.11: THE SCREENSHOT OF A SOFTWARE GENERATED GRAPHS .................................... 23
FIGURE 2.12: THE SCREENSHOT OF A SOFTWARE GENERATED DATA ........................................ 23
FIGURE 2.13: THE MATLAB CFTOOL SELECTING MENU ............................................................ 25
FIGURE 2.14: THE MATLAB DATA SHEET MENU ....................................................................... 25
FIGURE 3.1: IRRADIATION VS. TIME GRAPH .............................................................................. 28
FIGURE 3.2: TEMPERATURE VS. TIME GRAPH ........................................................................... 28
FIGURE 3.3: POWER VS. TIME GRAPH ........................................................................................ 29
FIGURE 3.4: IRRADIATION VS. TIME GRAPH .............................................................................. 30
FIGURE 3.5: TEMPERATURE VS. TIME GRAPH ........................................................................... 31
FIGURE 3.6: POWER VS. TIME GRAPH ........................................................................................ 31
FIGURE 3.7: THE POLYNOMIAL OF POWER/IRRADIATION VS. TEMPERATURE (COLOMBO 07) .... 32
FIGURE 3.8: THE POLYNOMIAL OF POWER/IRRADIATION VS. TEMPERATURE (HAMBANTHOTA) 33
FIGURE 3.9: THE POWER VS. TIME GRAPH (WITH COOLING SYSTEM) ........................................ 34
FIGURE 3.10: THE POWER VS. TIME GRAPH (WITH COOLING SYSTEM) ...................................... 34
FIGURE 3.11: THE SURFACE GRAPH OF X=TEMPERATURE, Y=IRRADIATION AND Z= MAXIMUM
POWER (COLOMBO) .......................................................................................................... 35
FIGURE 3.12: THE SURFACE GRAPH OF X=TEMPERATURE, Y=IRRADIATION AND Z= MAXIMUM
POWER (HAMBANTHOTA) ................................................................................................. 37
FIGURE 3.14: THE SUMMARIZED POWER/IRRADIATION VS. TEMPERATURE GRAPHS
(HAMBANTHOTA) ............................................................................................................. 40
FIGURE 3.15: THE SUMMARIZED POWER/IRRADIATION VS. TEMPERATURE GRAPHS (COLOMBO)
......................................................................................................................................... 40
FIGURE 3.16: THE FIGURE OF ARDUINO COOLING SYSTEM ....................................................... 45
viii
ACKNOWLEDGEMENT
First and foremost I would like to express my sincere gratitude to my internal supervisor, Dr.
M. L. C. Attyagalle, Lecturer, Department of Physics, for his professional, accurate and
supportive guidance given to complete research work and also I would like to thank Dr. N. G.
S. Shantha, coordinator, Department of Physics extended group, University of Sri
Jayewardenepura for giving guidance to complete my research successfully.
I would like to extend my special thanks to my external supervisor, Mr. H. Wickramasinghe,
Deputy Director General (Strategy), Sri Lanka Sustainable Energy Authority for his fullest
guidance, support and inspiration to do the research work successfully.
I wish to express my sincere gratitude to Sri Lanka Sustainable Energy Authority forgiving me
this opportunity to carry out this research project successfully. Finally I wish to thank my
family; colleagues at Sri Jayewardenepura University for their support to making this research
a successful.
ix
ABBREVIATIONS
NOCT- Nominal Operating Cell Temperature
OC – Open Circuit
PV – Photovoltaic
SLSEA – Sri Lanka Sustainable Energy Authority
SC – Short Circuit
x
ABSTRACT
The photovoltaic power generation is commonly used renewable power generation in the world
but the solar cells performance decreases with increasing of panel temperature. The solar panel
back temperature increases up to 60 oC-70oC in Sri Lanka.
The objective of this research is to identify the temperature effect on the solar photovoltaic
(PV) power generation and explore the ways to minimize the temperature effect. The
photovoltaic (PV) cells suffer efficiency drop as their operating temperature increases
especially under high insolation levels and cooling is beneficial. Commercially used two
polycrystalline solar modules (225W and 315W) are observed in this research. The observed
locations are Colombo 07 (Sri Lanka Sustainable Energy Authority) and Hambanthota (Solar
power plant). Characteristic parameters of selected photovoltaic modules are the Short-circuit
current (Isc), Open-circuit voltage (Voc) and Maximum power (Pmax). These parameters are
determined by varying the module’s temperature by spraying the water at ambient temperature
state when irradiation is constant. The above results are compared with standard rest condition
(STC) and theoretically predicted values. After observing the above system it has been
identified that, when the PV modules temperature decreases the overall efficiency of the PV
panel output power increases. From the gathered data, a suitable photovoltaic thermal system
(automated active cooling) is designed with Arduino UNO board for solar panels. From the
above thermal system solar panel efficiency can be increased up to 12% with measured power
output. The suggested active cooling system increases the solar panel efficiency. Another
advantage of using the proposed system is that clean and increase solar panel life time. These
techniques are anticipated to contribute towards wider applications of PV systems due to the
increased overall efficiency.
1
CHAPTER 1
INTRODUCTION
Photovoltaic is a method of generating electric power by converting radiant energy (especially
light) in to electricity using semiconductors that exhibit the photovoltaic effect. The solar
photovoltaic (PV) panel is the practical example for the photovoltaic power generations. The
efficiency of a solar photovoltaic (PV) panel is affected by irradiation and panel temperature.
The solar radiation contain radiant energy as well as thermal energy, but photovoltaic power
(PV) generation is only effected by the solar radiant energy (solar light). When the solar
radiation rise on the cell temperature will be rises, hence the cell materials lose their
efficiency1. The solar PV module performance is generally rated under standard test conditions
(STC). The test conditions are irradiance of 1000 W/m², solar spectrum of AM 1.5 and module
temperature at 25 °C2. When the PV module performing under irradiance, its temperature will
increase from 30 °C - 70 °C. This temperature effect courses the low efficiency performance
of the solar PV systems.
The objectives of this study was, to determine identify the temperature effect on the solar
photovoltaic (PV) power generation and minimize the temperature effect. The study was
focused on finding answers to the following questions;
1. Whether the PV module power efficiency can be improved by temperature decreasing.
2. Whether the PV module temperature can be decreased to ambient temperature.
3. What is the best cooling method and coolant for the PV module?
2
1.1.1 Background
The photovoltaic concept is discovered by Edmund Becquerel in 1839. After that photovoltaic
module technologies were evolved in the mid-1980. The photovoltaic performance module
was developed in Sandia (United States Department of Energy), it was called PVFORM3.
Commercial concentrated solar power plants were first developed in the 1980s. The 392 MW
Ivanpah installation is the largest concentrating solar power plant in the world, located in the
Mojave Desert of California. At present lots of countries are developing and using solar PV
panels to produce electricity from solar energy4.
To promote the renewable energy in the country, Sri Lanka Sustainable Energy Authority
(SLSEA), established by the Sri Lanka Sustainable Energy Authority Act No. 35 of 2007, is
engaged in renewable energy development and improving energy efficiency program. In order
to allow domestic consumers to contribute to the renewable energy development effort, the net
metering scheme was introduced in 2010 and power generation project titled 'Soorya Bala
Sangramaya' (Battle for Solar Energy) in 2016. The solar PV module usage in the Sri Lanka
became very famous and total amount of 4,196 net-metered projects are connected to national
grid at end of 2015. The cumulative capacity of these project are 28MW and total generation
in 2015 is 38.8 GWh5.
The SLSEA would establish the first ever grid connected solar energy park in Buruthakanda
area in the Hambantota, which also promises to be the first solar energy park in Asia. This is
constructed with the grants of the Government of Japan and the Government of Korea. The
total capacity of the project is 1237 kW. The Japanese contribution is Rs.114 million and the
Korean contribution is Rs.513 million. The project spans over of 20 acres of the 50 acre energy
3
park area demarcated for the purpose. The project is estimated to generate 558,600 kWh per
annum and this is capable of offsetting 860 tons of CO2.5
1.1.2 Description about photovoltaic modules
Photovoltaic modules generate electrical power from converting radiant energy to electricity
through photovoltaic effect. Most of the photovoltaic modules consist of semi-conducting
materials such as silicon based materials. When the photovoltaic module exposed to the
irradiation, semi-conducting materials generates electrical charges and conducted away by
metal carriers. A single cell generates a small amount of direct current, but the larger amount
of direct current (DC) can be produced from the string of multiple cells are connected. 1
FIGURE1.1 CHART OF SOLAR CELL TO PV SYSTEM.
Solar cell Solar module
(multiple cells)
Solar panel
(multiple
modules)
Solar array
(multiple
panels)
4
There are 3 major different types of solar panels.
Monocrystalline Silicon Solar Cells
Polycrystalline Silicon Solar Cells
Thin-Film Solar Cells (TFSC).
1.1.2.1 Polycrystalline Silicon Solar Cells
Polycrystalline solar cells are the most commonly used solar cells in the world as well as in Sri
Lanka. These cells are generally cheaper than the monocrystalline solar cells and slightly less
efficient. The average efficient of the cell is around 12%. The manufacturing process of these
cells are much simpler than the monocrystalline solar cells, they are made by pouring molten
silicon into a cast. These cells have triangular edges unlike monocrystalline solar cells,
sometimes these are known as multicrystalline cells.1 In this research two polycrystalline
silicon solar cells were used.
1.1.2.2 The characteristics of PV solar cell
Most of photovoltaic cells consist silicon based semiconductor materials, these cells are
constituted of two separate layers of semiconductors. The silicon layer which has excess to
electrons is N-type and which has excess hole is P-type. These two layers together generates a
PN junction by sandwiching together. When the sun irradiation rises on to the cell some of the
photons of the irradiation absorbed by the cell. The absorbed photons will have energy greater
than the energy gap between valence band and conduction band in the semiconductor. When
the electrons gets energy from the photons, it becomes exited and jump out and creates one
electron-hole pair. These electron-hole pairs near the p-n junction is travelled to n-type side of
5
the junction because of the electrostatic force. This is the basic principle of a solar cell and
different parameters of the solar cell will be decided the cell efficiency.1
Short circuit current (Isc) of solar cell
The short circuit current is the maximum current that a solar cell can provide without damaging
its own constriction. . It is measured by short circuiting the terminals of the cell at most
optimized condition of the cell for producing maximum output.6
Open circuit voltage (Voc) of solar cell
It is measured by measuring voltage across the terminals of the cell when no load is connected
to the cell. This voltage depends upon the techniques of manufacturing and temperature but
not fairly on the intensity of light and area of exposed surface.6
1.1.3 The Solemetric PV analyzer tool kit
The PV Analyzer is a convenient test device designed for measuring characteristic parameters
of PV modules. It measures the Irradiation, temperature, Voc, Isc and Pmax of PV modules and
compares the results to the predictions. The readings can be recorded via Wi-Fi connection
from a PV analyzer software installed computer. This tool kit is portable and consisting 3
devices and a PV analyzing windows based software.
1 Solemetric PV analyzer – measures Voc, Isc and Pmax from the PV module
2 Solemetric pyranometer- measure solar irradiation and PV module temperature
3 Solemetric Wi-Fi dongle – connects computer and Solemetric tools via Wi-Fi connection
6
FIGURE 1.1: THE SOLEMETRIC ANALYZER PV TOOL KIT
FIGURE 1.2: SCREENSHOT OF THE SOLEMETRIC PV ANALYZING SOFTWARE
2
3
1
1
2
3
7
LITERATURE REVIEW
1.2.1 PV module/cell efficiency as a function of the operating
The effect of temperature on the electrical efficiency of a PV cell/module can be given upon
the current (I), and the voltage (V), as the maximum power is given by2
In this fundamental expression, which also serves as a definition of the fill factor (FF),
subscript m refers to the maximum power point in the module’s I–V curve, while subscripts
oc and sc denote open circuit and short circuit. The fill factor (FF) is a measure of a PV module,
it can be determined by following equation.2
FIGURE 1.3: PV CHARACTERISTIC CURVE
8
Where, current and voltageat maximum power (. The theoretical value of
power () and the short-circuit current (Isc), open-circuit voltage (Voc). The PV module’s
electrical efficiency (ηc) at given PV cell temperature () and solar irradiation () is given
by,7
Where the PV module’s electrical efficiency () at reference temperature (), the
temperature coefficient (), the solar irradiation coefficient (). The traditional linear
expression for the PV module electrical efficiency represent by below equation,8
The value of temperature coefficient depends not only on the PV material but on reference
temperature. The ratio of temperature coefficient given by,9
When the PV module at high temperature, the PV module electrical efficiency drops to the
zero. The PV cell operating temperature is not always available, which can be replaced by
nominal operating cell temperature (), such expression is given by,10
9
NOCT labeled quantities are measured with no load attached, which means under open circuit
conditions. The monthly electrical output () of PV array can be determined by following
equation,11
Where glazing-cover transmittance (), plate absorptance (), n is the number of hours per day,
UL is the overall thermal loss coefficient, HT is the monthly average daily insolation on the
plane of the array, and V is a dimensionless function of such quantities as the sunset angle, the
monthly average clearness index, and the ratio of the monthly total radiation on the array to
that on a horizontal surface, in which the over-bar denotes monthly average quantities11
1.2.2 PV power output dependence on module/cell operating temperature
The predicted power output (P) of a PV module in the field can be given by,12
Where, A is the aperture area of the PV module and is the transmittance of the PV cells’
outside layers. The nonlinear multivariable regression equation for the PV electrical power as
a function of cell/module operating temperature and basic environmental variables given by,11
10
Where, j = 1–4 and m are model parameters. A correction coefficient for the output power
of a water cooled PV system given by,13
In which, the parameter takes values 1 or 3, for values of below or above 50, output
current () and voltage () respectively.
1.2.3 PV module/cell cooling techniques
The most simple, reliable and low cost method is to remove thermal energy (heat) from PV
module using natural air circulation, but other cooling methods are used to keep electrical
efficiency and the temperature at acceptable level of PV modules. The most popular method
for cooling PV modules is Hybrid Photovoltaic/Thermal (PV/T) solar system 14. These systems
consist of solar photovoltaic panels for the electric power generation with a cooling system.
The cooling agent of the cooling system is circulated around the heated PV modules for the
cooling the solar cells. Water or air can be used as the cooling agent. The thermal energy of
heat absorbed water or air (cooling agent) can be used. Akbarzadeh and Wadowski 15 found
that, from the hybrid PV/T solar system with water cooling can be increased the solar cell
power output by most 50%. Chaniotakis 14 designed a hybrid PV/T solar system where water
and air both can be used as cooling agents and he found that the water-based cooling system
increases the solar cell efficiency than air-based cooling system. A cooling method by spraying
11
water using a fan studied by Kluth 16. It was found that the solar panel with the cooling system
generates more power output than without the cooling system. It can be concluded the most
effective coolant for the PV module is water rather than air from the above literature survey.
1.2.4 The heating rate of the PV module/cell
The cooling frequency of the PV panels is determined by the heating rate of the panels. Thus,
by calculating the module temperature as a function of time, the heating rate of the PV panels
can be specified, and consequently, the cooling frequency can be specified. The module
temperature Tm is calculated using the following well known equation,16
The module temperature is based on the ambient conditions, ambient temperature, (Tamb), and
the nominal operating cell temperature (). The nominal operating cell temperature is
conducted from the work of Bharti.16 The nominal operating cell temperature is a function of
the ambient air temperature at the sunrise time Trise as follows16:
12
1.2.5 Annual PV potential in Sri Lanka
The energy rating method estimates PV potential by multiplying the total solar irradiation
during a specific period of time by a performance ratio. The annual PV electric potential in
Sri Lanka is given by figure5. The Colombo district and Hambanthota were observed in this
research where annual PV potential were relatively higher.
FIGURE 1.3: ANNUAL PV POTENTIAL IN SRI LANKA
13
CHAPTER 2
MATERIALS AND METHODS
2.1 General
The Commercially used two polycrystalline solar modules (225W and 315W) were selected
and observed with Solemetric tools. The Mitsubishi solar module (225W) was observed in the
Hambanthota solar power plant and the Canadian solar module (315W) was observed in Sri
Lanka Sustainable Energy Authority head of at Colombo 07. The temperature of the panels
were varied by spraying ambient temperature water (water cooling system) to the surface of
the panels. The characteristic parameters of selected photovoltaic modules are the Short-circuit
current (Isc), Open-circuit voltage (Voc) and Maximum power (Pmax). These parameters were
observed by varying the PV modules temperature under solar irradiation.
2.2 Experimental setup
Two experimental setups has been developed to study the temperature effect on photovoltaic
power generation in two different places in Sri Lanka. The two different places were the solar
power plant in the Hambantota district (6.2 0N, 81.04 0E) and Sri Lanka Sustainable Energy
Authority in Colombo 07 (6.9 0N, 79.9 0E) where both panels array azimuths were 00 . The
Mitsubishi PV module which was observed in the solar power plant is a PV module that
connected in the solar power plant. The Canadian solar which was observed in Colombo 07
was temporary burrowed from a private company. The Solemetric tools which were used to
observe the characteristic parameters of the panels were supplied by the SLSEA. The electrical
and physical characteristics of the two PV modules given by below tables.
14
Table 2.1: The electrical and physical characteristics of the Mitsubishi PV module
used in the Hambanthota solar power plant
Electrical characteristics
Model no PV-UJ225GA6
Power rating (Pmax) 225W
Open circuit voltage (Voc) 36.4V
Short circuit current (Isc) 8.30A
Voltage at maximum power (Vmp) 30.0V
Current at maximum power (Imp) 7.50A
Panel efficiency 13.7%
Power tolerance +/-3%
Maximum system voltage Vmax 600V
Maximum series fuse rating 15A
Nominal operating cell temperature 45oC
Temperature coefficients
Temperature coefficient of Isc 0.15%/ oC
Temperature coefficient of Voc -0.44%/ oC
Temperature coefficient of Pmp -0.41%/ oC
Physical characteristics
Cell type Polycrystalline silicon
Cell size (mm · mm) 156×156
No of cells per panel 60
Panel dimensions (mm · mm · mm) 1658×994×46
Weight 20.0kg
15
Table 2.2: The electrical and physical characteristics of the Canadian solar PV
module used in the Colombo 07
Electrical characteristics
Model no CS6X-315P
Power rating (Pmax) 315W
Open circuit voltage (Voc) 45.1 V
Short circuit current (Isc) 9.18 A
Voltage at maximum power (Vmp) 36.6 V
Current at maximum power (Imp) 8.61 A
Panel efficiency 16.42 %
Power tolerance 0 ~ + 5 W
Maximum system voltage Vmax 1000V
Maximum series fuse rating 15A
Nominal operating cell temperature 45±2 °C
Temperature coefficients
Temperature coefficient of Isc 0.053%/ oC
Temperature coefficient of Voc -0.31%/ oC
Temperature coefficient of Pmax -0.41%/ oC
Physical characteristics
Cell type Polycrystalline silicon
Cell size (mm · mm) -
No of cells per panel 72
Panel dimensions (mm · mm · mm) 1954 ˣ 982 ˣ 40
Weight 22.0kg
16
Solemetric PV analyzer and Solemetric pyranometer (with thermocouples) was connected to
the selected PV modules as Figure 2.1. The both Solmetric PV Analyzer and Solmetric
Pyranometer were connected to a computer via Wi-Fi connection. The computer was installed
with the Solemetric PV analyzing software.
FIGURE 2.1: THE EXPERIMENTAL SETUP OF SOLMETRIC TOOLS AND PV MODULE.
The Solemetric pyranometer was mounted to the PV module surface using a special mounting
equipment as figure 2.2. The top end of the Solemetric pyranometer (irradiation sensor) was
properly inspected for the shading before starting the experiment. The end of the thermocouple
was taped to back of the PV module surface with special silicon tape and the other end
connected the Solemetric pyranometer as figure 2.3 and figure 2.4. The taped end of the
thermocouple was properly inspected before the experiment.
17
FIGURE 2.2: THE MOUNTED SOLEMETRIC PYRANOMETER
FIGURE 2.3: THE TAPED SIDE OF THE THERMOCOUPLE
FIGURE 2.4: THE CONNECTED SIDE OF THE THERMOCOUPLE
18
To decreases the PV module temperature, a water nozzle was mounted upper side of the PV
module. The ambient temperature was sprayed to the PV water panel using PVC water pipe
with little holes as figure 2.5. The PVC water tube was connected a flexible water horse with
a water line.
FIGURE 2.5: THE PVC WATER NOZZLE SPRAYING THE WATER DROPLETS
The sprayed water droplets were slipped on the PV module surface covering all surface area
of the PV module to ground. The temperature of the cooling water was assumed to be at
ambient temperature. The experimental setup was completed in a good irradiation area without
shading. The experimental setup with the water cooling consist of following parts as shown
in figure 2.6,
1. PV module
2. The Solematric PV analyzer
3. The Solemertic pyranometer
4. Water Inlet
5. Water Nozzle
19
FIGURE 2.6: THE FINAL EXPERIMENTAL SETUP
2.1 Experimental procedure
The observing PV module setups has been perform to determine the characteristic parameters
of each solar panel. The experiment was conducted in December month and each experiment
setups dates were given by,
Table 2.2. The experiments dates
Experiment
Time Period
The Colombo 07 experiment setup (315W)
09/12/2016 to 10/12/2016
14/12/2016 to 15/122016
The solar power plant experiment setup (215W)
10/12/2016 to 13/12/2016
5
4
3
2
1
20
The experiment started from 11.00 a.m. till 2.00 p.m., where irradiation is the highest at the
day time. The Wi-Fi connected computer was received data (Irradiation, temperature, Voc, Isc
and Pmax) via Wi-Fi for the different PV module temperatures. The Solmetric PV analyzing
software was displayed each data and the data was recorded with 1minute time gap. The
recorded data was manually saved by getting screenshots of the computer display.
The PV module temperature was varied by spraying the ambient temperature water to the
experimental setup. The collected data was processed using Solmetric windows based
software, Pmax vs. time curves were plotted for different temperatures with constant irradiation,
each irradiation vs. time, temperature vs. time curves were plotted. The predicted values of
Pmax and PV module temperature were also plotted. To determine the efficiency of the each PV
module panel was calculated for different temperatures and
vs. temperature graphs were plotted. The plotted graphs were analyzed to find
the maximum efficiency at ambient temperature and how to maintain the temperature.
2.1.1. Scope limitation
The ambient temperature measuring equipment is not included in the PV analyzer tool kit,
hence the ambient temperature was not measured, but daily ambient temperature was
measured. The wind speed and humidity of the air was also not measured, due to the lack of
instruments. The irradiation was constantly changing due to the shading of clouds, hence
constant irradiation over time period was not recorded. When changing the height difference
between roof top and the PV module could not performed practically. The PV module
temperature could not decreased below the ambient temperature, because of the ambient
temperature coolant (water).
21
2.1.2. Data collecting using Solemetric PV analyzing software
The data collecting method from PV analyzing software needed good Wi-Fi connection
between the computer and the Solemetric tool kit, after the connecting tools via Wi-Fi the PV
analyzing software was lighted the “ready” button in green colour as figure 2.7.
FIGURE 2.7: THE PV ANALYZING SOFTWARE “READY” BUTTON
Then new project was started by File menu, selecting New Project to launch the New Project
Wizard. The Site Information screen of the New Project Wizard appears as shown below.
FIGURE 2.8: THE NEW PROJECT WIZARD
22
Then the PV module information was entered such as location (latitude and longitude) and
array azimuth (true compass heading). After that the PV module characteristics, model and
manufacturer details were selected from the software as figure 2.9. Most of commercially
available PV modules information were in the software data base.
FIGURE 2.9: THE PV MODULE CHARACTERISTIC WIZARD
After selecting the PV module characteristics, the array navigator was created. The array
navigator included one PV module did not included an inverter. Then the wire length and the
gauge entered to the Wizard as figure 2.10.
FIGURE 2.10: THE PV ARRAY NAVIGATOR WIZARD
23
As the next step the Wizard was completed by clicking finish button. After that clicking the
“Measure now” button (figure 2.7) the PV module data was measured. The measure data was
displayed as figure 2.11 and figure 2.12. The measured data was manually entered to an Excel
sheet for further analyzing, and each data displayed screenshot were taken.
FIGURE 2.11: THE SCREENSHOT OF A SOFTWARE GENERATED GRAPHS
FIGURE 2.12: THE SCREENSHOT OF A SOFTWARE GENERATED DATA
24
Table 2.4: The data of figure 2.11 and figure 2.12
Figure 2.11
Figure 2.12
Maximum power vs. voltage graph
Predicted- Pmax, Vmp, Imp, Voc, Isc, Fill factor
STC conditions current vs. voltage graph
Measured- Pmax, Vmp, Imp, Voc, Isc, Fill factor
Measured valued current vs. voltage graph
STC- Pmax, Vmp, Imp, Voc, Isc, Fill factor
Irradiation
Irradiation
Fill factor
Fill factor
Smart temperature
Smart temperature
Panel back temperature
Panel back temperature
Tilt angle
Tilt angle
2.1.3. Setting a mathematical function using Matlab
Here, it was used Matlab to plot a graph between independent and dependent variables. By
using Matlab, data were imported to feed variables to the application. After defining
independent and dependent variables it was used cftool (Curve Fitting Tool) function to plot
the graph and derive a function to the graph. Using cftool app,
Create, plot, and compare multiple fits.
Use linear or nonlinear regression, interpolation, smoothing, and custom equations.
View goodness-of-fit statistics, display confidence intervals and residuals, remove
outliers and assess fits with validation data.
25
Automatically generate code to fit and plot curves and surfaces, or export fits to the
workspace for further analysis.
After feeding data we can directly use cftool app to plot the graph. Here, it should be set
variables in cftool app.
FIGURE 2.13: THE MATLAB CFTOOL SELECTING MENU
We have to define axis after defining variables to fit a curved surface. Then we can select
which type equation to fit with the dataset.
FIGURE 2.14: THE MATLAB DATA SHEET MENU
Here,
Interpolant
Polynomial
Custom Equation
26
Lowess
In this study, it was selected a polynomial to derive best fit with the data. After selecting
polynomial, it was possible to select the degree of the polynomial to smooth the curve. In this
cftool it was possible to set a degree up to five. The graphs were plotted by the five degree of
polynomial.
27
CHAPTER 3
3. RESULTS AND DISCUSSION
The results were analyzed by using two different software (Matlab and Excel). The
Hambanthota area experimental setup data and Colombo 07 area experimental setup were
separately analyzed.
3.1 The Hambanthota area data (12/12/2016)
The experiment setup was observed on 12/12/2016 at 11.00 am to 2.00 pm without cooling the
PV module in the Hambanthota solar power plant. The irradiation vs. time, temperature vs.
time and maximum power vs. time graphs were plotted. The irradiation was varied due the
shadings of the clouds and the maximum irradiation measured was around 1000 W/m2 (figure
3.1). The smart temperature and PV module back temperature were plotted. The temperatures
were varied due to irradiation and onshore sea breeze. The smart temperature was greater than
the PV module back temperature. The maximum temperatures were measured when the
maximum irradiation was recorded (figure 3.2). The maximum predicted power and maximum
measured power vs. time graphs were plotted. The power vs. time graph shape was same as
the irradiation vs. time graph. The measured power was greater than the predicted power
(figure 3.3). The maximum power output was around 225W and the maximum PV module
temperature was around 500C.
28
FIGURE 3.1: IRRADIATION VS. TIME GRAPH
FIGURE 3.2: TEMPERATURE VS. TIME GRAPH
0
200
400
600
800
1000
1200
11:00 AM
11:07 AM
11:10 AM
11:19 AM
11:22 AM
11:24 AM
11:26 AM
11:28 AM
11:30 AM
11:32 AM
11:34 AM
11:37 AM
11:43 AM
11:46 AM
11:51 AM
11:54 AM
11:58 AM
12:02 PM
12:06 PM
12:08 PM
12:10 PM
12:14 PM
12:21 PM
12:24 PM
12:27 PM
12:33 PM
12:36 PM
1:51 PM
1:53 PM
1:55 PM
1:57 PM
IRRADIATION (W/M2)
TIME
Irradiation vs. Time
0
10
20
30
40
50
60
11:00 AM
11:07 AM
11:10 AM
11:19 AM
11:22 AM
11:24 AM
11:26 AM
11:28 AM
11:30 AM
11:32 AM
11:34 AM
11:37 AM
11:43 AM
11:46 AM
11:51 AM
11:54 AM
11:58 AM
12:02 PM
12:06 PM
12:08 PM
12:10 PM
12:14 PM
12:21 PM
12:24 PM
12:27 PM
12:33 PM
12:36 PM
1:51 PM
1:53 PM
1:55 PM
1:57 PM
TEMPERATURE(OC)
TIME
Temperature vs. Time
Panel back temperature Smart temperature
29
FIGURE 3.3: POWER VS. TIME GRAPH
3.2 The Colombo 07 area data (14/12/2016)
The experiment setup was observed on 14/12/2016 at 11.25 am to 12.45 pm without cooling
the PV module in the Sri Lanka Sustainable Energy Authority. The irradiation vs. time,
temperature vs. time and maximum power vs. time graphs were plotted. The irradiation was
varied due the shadings of the clouds and the maximum irradiation measured was around 900
W/m2 (figure 3.4) and it was lower than the Hambanthota area data. The smart temperature
and PV module back temperature were plotted. The temperatures were varied due to irradiation
and the temperature was higher than the Hambanthota area data. The maximum temperatures
were measured when the maximum irradiation was recorded (figure 3.5). The maximum
0
50
100
150
200
250
11:00 AM
11:07 AM
11:10 AM
11:19 AM
11:22 AM
11:24 AM
11:26 AM
11:28 AM
11:30 AM
11:32 AM
11:34 AM
11:37 AM
11:43 AM
11:46 AM
11:51 AM
11:54 AM
11:58 AM
12:02 PM
12:06 PM
12:08 PM
12:10 PM
12:14 PM
12:21 PM
12:24 PM
12:27 PM
12:33 PM
12:36 PM
1:51 PM
1:53 PM
1:55 PM
1:57 PM
MAXIMUM POWER (W)
TIME
Power vs. Time
Measured max power Predicted max power
30
predicted power and maximum measured power vs. time graphs were plotted. The power vs.
time graph shape was same as the irradiation vs. time graph. The measured power was greater
than the predicted power (figure 3.6). The maximum power output was around 315W and the
maximum PV module temperature was around 550C. The smart temperature was greater than
the PV module back temperature
FIGURE 3.4: IRRADIATION VS. TIME GRAPH
0
100
200
300
400
500
600
700
800
900
1000
11:25 AM
11:28 AM
11:31 AM
11:34 AM
11:50 AM
11:53 AM
11:56 AM
11:59 AM
12:02 PM
12:05 PM
12:08 PM
12:11 PM
12:14 PM
12:17 PM
12:20 PM
12:23 PM
12:26 PM
12:29 PM
12:32 PM
12:35 PM
12:38 PM
12:41 PM
12:44 PM
12:47 PM
IRRADIATION (W/M2)
TIME
Irradiation vs. Time
31
FIGURE 3.5: TEMPERATURE VS. TIME GRAPH
FIGURE 3.6: POWER VS. TIME GRAPH
0
50
100
150
200
250
300
350
POWER (W)
TIME
Power vs. Time
Measured max power Predicted max power
0
5
10
15
20
25
30
35
40
45
50
11:25 AM
11:28 AM
11:31 AM
11:34 AM
11:50 AM
11:53 AM
11:56 AM
11:59 AM
12:02 PM
12:05 PM
12:08 PM
12:11 PM
12:14 PM
12:17 PM
12:20 PM
12:23 PM
12:26 PM
12:29 PM
12:32 PM
12:35 PM
12:38 PM
12:41 PM
12:44 PM
12:47 PM
TEMPERATURE(OC)
TIME
Temperature vs. Time
Panel back temperature Smart temperature
32
3.3 The relationship between power/irradiation and PV module temperature
The power/irradiation vs. temperature graphs were plotted to determine the polynomial of the
power/irradiation vs. temperature. The maximum allowable temperature with generates the
maximum power was observed for each Hambanthota and Colombo data set (figure 3.7 and
figure 3.8). The maximum allowable temperature was determined by calculating the maximum
value of the polynomial.
FIGURE 3.7: THE POLYNOMIAL OF POWER/IRRADIATION VS. TEMPERATURE (COLOMBO 07)
The maximum value of the polynomial = 39.05400C
y = 3E-09x6-8E-07x5+ 0.0001x4- 0.0069x3+ 0.2529x2- 4.891x + 39.054
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
30 35 40 45 50 55 60 65 70
Power/Irradiation (m2)
Temperature (0C)
Power/Irradiation vs. Temperature
33
FIGURE 3.8: THE POLYNOMIAL OF POWER/IRRADIATION VS. TEMPERATURE
(HAMBANTHOTA)
The maximum value of the polynomial = 39.38000C
3.4 The relationship between power and PV module temperature when the cooling
system is active
The cooling system was activated for 10 minutes (12.08 pm to 12.18pm). The maximum power
vs. time and the temperature vs. time graphs were plotted (figure 3.9 and figure 3.10). The PV
module temperature was decreased in the PV module cooling time period and the maximum
power was increased for that time period. The PV module temperature was increased after
deactivating the cooling system and the maximum power was decreased. The PV module
efficiency was increased up to 12% with measured power output from the water cooling. The
water flow rate is around 2 liters per minute and approximately 20 liters of water were used to
cool down the PV module.
y = 4E-07x4-6E-05x3+ 0.003x2- 0.0592x + 39.39
0
0.05
0.1
0.15
0.2
0.25
0.3
30 35 40 45 50 55 60
Power/Irradiation (m2)
Temperature (0C)
Power/Irradiation vs. Temperature
34
FIGURE 3.9: THE POWER VS. TIME GRAPH (WITH COOLING SYSTEM)
FIGURE 3.10: THE POWER VS. TIME GRAPH (WITH COOLING SYSTEM)
200
210
220
230
240
250
260
270
12:08 PM
12:09 PM
12:10 PM
12:11 PM
12:12 PM
12:14 PM
12:15 PM
12:16 PM
12:17 PM
12:18 PM
12:19 PM
12:20 PM
12:21 PM
12:22 PM
12:23 PM
12:24 PM
12:25 PM
12:26 PM
12:27 PM
12:28 PM
12:29 PM
12:30 PM
12:31 PM
12:32 PM
12:33 PM
12:34 PM
12:35 PM
12:36 PM
12:37 PM
12:38 PM
POWER (W)
Power(W) vs. Time
Measured max power Predicted max power
0
10
20
30
40
50
60
70
12:08 PM
12:09 PM
12:10 PM
12:11 PM
12:12 PM
12:14 PM
12:15 PM
12:16 PM
12:17 PM
12:18 PM
12:19 PM
12:20 PM
12:21 PM
12:22 PM
12:23 PM
12:24 PM
12:25 PM
12:26 PM
12:27 PM
12:28 PM
12:29 PM
12:30 PM
12:31 PM
12:32 PM
12:33 PM
12:34 PM
12:35 PM
12:36 PM
12:37 PM
12:38 PM
TEMPERATURE (0C)
Temperature vs. Time
35
3.5 The relationship between maximum power, PV module temperature and
irradiation.
The surface graph was plotted by using temperature and irradiation as the independent variable
and maximum power as the dependent variable (figure 3.11 and figure 3.12) for Colombo and
Hambanthota data sets. The linear model polynomial of each graphs were determined using
Matlab software.
FIGURE 3.11: THE SURFACE GRAPH OF X=TEMPERATURE, Y=IRRADIATION AND Z=
MAXIMUM POWER (COLOMBO)
Linear model polynomial:
f(x,y) = p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 + p30*x^3 + p21*x^2*y
+ p12*x*y^2 + p03*y^3 + p40*x^4 + p31*x^3*y + p22*x^2*y^2
+ p13*x*y^3 + p04*y^4 + p50*x^5 + p41*x^4*y + p32*x^3*y^2
+ p23*x^2*y^3 + p14*x*y^4 + p05*y^5
Coefficients (with 95% confidence bounds):
p00 = -1386 (-1.109e+04, 8318)
p10 = 3 (-11.61, 17.61)
36
p01 = 143.6 (-926.1, 1213)
p20 = -0.01413 (-0.02813, -0.000125)
p11 = 0.08848 (-1.148, 1.325)
p02 = -7.492 (-53.88, 38.9)
p30 = -5.695e-06 (-2.763e-05, 1.624e-05)
p21 = 0.001084 (0.0001277, 0.00204)
p12 = -0.01516 (-0.05498, 0.02467)
p03 = 0.2295 (-0.771, 1.23)
p40 = 4.491e-09 (-2.019e-08, 2.917e-08)
p31 = 1.683e-08 (-6.993e-07, 7.329e-07)
p22 = -2.218e-05 (-4.888e-05, 4.524e-06)
p13 = 0.0003743 (-0.0002799, 0.001028)
p04 = -0.003545 (-0.01443, 0.007344)
p50 = 9.929e-13 (-9.844e-12, 1.183e-11)
p41 = -1.415e-10 (-4.351e-10, 1.522e-10)
p32 = 2.756e-09 (-3.584e-09, 9.096e-09)
p23 = 1.204e-07 (-9.36e-08, 3.345e-07)
p14 = -2.591e-06 (-6.881e-06, 1.698e-06)
37
p05 = 2.062e-05 (-2.835e-05, 6.959e-05)
Goodness of fit:
SSE: 2.433e+04
R-square: 0.9782
Adjusted R-square: 0.9762
RMSE: 10.42
FIGURE 3.12: THE SURFACE GRAPH OF X=TEMPERATURE, Y=IRRADIATION AND Z=
MAXIMUM POWER (HAMBANTHOTA)
Linear model polynomial:
f(x,y) = p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 + p30*x^3 + p21*x^2*y
+ p12*x*y^2 + p03*y^3 + p40*x^4 + p31*x^3*y + p22*x^2*y^2
+ p13*x*y^3 + p04*y^4 + p50*x^5 + p41*x^4*y + p32*x^3*y^2
+ p23*x^2*y^3 + p14*x*y^4 + p05*y^5
Coefficients (with 95% confidence bounds):
38
p00 = -8873 (-1.664e+04, -1104)
p10 = -24.07 (-34.61, -13.53)
p01 = 1460 (449, 2471)
p20 = -0.008604 (-0.01704, -0.0001683)
p11 = 2.731 (1.569, 3.892)
p02 = -92.77 (-146.5, -39.01)
p30 = -1.057e-05 (-1.605e-05, -5.089e-06)
p21 = 0.001173 (0.0004818, 0.001865)
p12 = -0.1209 (-0.1717, -0.07013)
p03 = 2.897 (1.428, 4.366)
p40 = -3.659e-09 (-6.826e-09, -4.926e-10)
p31 = 7.108e-07 (4.184e-07, 1.003e-06)
p22 = -4.568e-05 (-6.748e-05, -2.389e-05)
p13 = 0.002467 (0.001445, 0.003489)
p04 = -0.04488 (-0.06555, -0.02421)
p50 = 4.271e-12 (2.905e-12, 5.638e-12)
p41 = -2.019e-10 (-2.925e-10, -1.113e-10)
p32 = -3.571e-09 (-8.711e-09, 1.569e-09)
39
p23 = 4.509e-07 (2.047e-07, 6.972e-07)
p14 = -1.878e-05 (-2.672e-05, -1.084e-05)
p05 = 0.0002762 (0.0001563, 0.000396)
Goodness of fit:
SSE: 3265
R-square: 0.9958
Adjusted R-square: 0.9956
RMSE: 3.081
3.6 The summarized power/irradiation vs. temperature graphs.
The power/irradiation vs. temperature graphs were plotted for each irradiation levels as figure
3.14 and figure 3.15. The power/irradiation was decreased when the temperature was
increasing. The maximum irradiation level at Colombo data set was up to 900W/m2 and the
Hambanthota data set was up to 1000W/m2.
40
FIGURE 3.14: THE SUMMARIZED POWER/IRRADIATION VS. TEMPERATURE GRAPHS
(HAMBANTHOTA)
FIGURE 3.15: THE SUMMARIZED POWER/IRRADIATION VS. TEMPERATURE GRAPHS
(COLOMBO)
0
0.5
1
1.5
2
2.5
32
32
32
32
32.3
32.3
33
33.2
33.5
33.5
33.6
33.8
34
34
34.3
34.3
35.3
37.7
52
61.4
Power/Irradiation (m2)
Temperature (0C)
Power/Irradiation vs. Temperature
200 300 400 500 600 700 800 900
0
0.5
1
1.5
2
2.5
33.433.633.834.13838.940.240.342.6
Power/Irradiation (m2)
Temperature (0C)
Power/Irradiation vs. Temperature
1000
900
800
700
600
500
400
300
200
41
CHAPTER 4
4.1 Conclusion
The operating temperature plays a central role in the photovoltaic conversion process
and the PV modules performance decreases with increasing of panel temperature.
The irradiation levels in Hambanthota area is higher than the Colombo 7 area.
The PV module efficiency can be increased up to 12% with measured power output
from the water cooling.
From the water cooling, the PV module temperature can be decreases down to ambient
temperature.
Spraying water should be clean and chloride free, otherwise the module surface would
be damaged.
We can use collected rain water for cooling and designing a circulating system will be
save the water.
Approximately 20 liters are used to cool down a single PV module.
The onshore breeze affected to the Hambanthota area and the PV module temperature
decreased with it.
The polynomial of power/irradiation vs. temperature in Colombo 07 data is given by,
42
The maximum allowable temperature with generates the maximum power is 39.05400C
The polynomial of power/irradiation vs. temperature in Hambanthota data is given by,
The maximum allowable temperature with generates the maximum power is 39.38000C
The surface polynomial of relationship between maximum power and temperature in
Colombo 07 data is given by,
The surface polynomial of relationship between maximum power and temperature in
Hambanthota data is given by,
43
4.2 Suggestion for future works
From the gathered data, a suitable photovoltaic thermal system (automated active cooling) is
designed with Arduino UNO board for solar panels. From the above thermal system solar panel
efficiency can be increased up to 12% with measured power output. The suggested active
cooling system increases the solar panel efficiency not only that but it also clean and increase
solar panel life time. These techniques are anticipated to contribute towards wider applications
of PV systems due to the increased overall efficiency.
The cooling system
The suggested cooling system automatically activates, when the PV solar module temperature
is above 450C. The cooling system will automatically deactivate when the PV module
temperature below 350C. The power supply for the cooling system should provide separately.
The apparatus for the cooling system given below,
Arduino UNO R3 board
3/8’ NM /SE connector
44
SRD-05VDC-SL-C5V relay
DHT11 temperature and humidity sensor
Bread board
Connecting wires
Water pump
Water nozzles and tubes
Water tank
Water drain pipe
The water tank, water nozzles and water pump should choose according to the PV module
array. Approximately 20 liters are used to cool down a one PV module with 2liter per minute
water flow rate. The water pump wattage should less than 10% of the PV module array
nominal power, otherwise it is not power consume. The water tubes, drain pipe, water nozzle
and water tank should connect to circulate the water. The water nozzle mount top of the PV
module to drain water droplet on PV module surface. For the water coolant we can use rain
water. The Arduino circuit should connect as figure 4.1. The programming part of the circuit
is in the appendix II.
45
FIGURE 3.16: THE FIGURE OF ARDUINO COOLING SYSTEM
46
4.3 References
1. Hecktheuer, L. A.; Krenzinger, A.; Prieb, C. W. M., Methodology for photovoltaic
modules characterization and shading effects analysis. Journal of the Brazilian Society of
Mechanical Sciences 2002, 24 (1), 26-32.
2. Markvart, T., Solar electricity. John Wiley & Sons: 2000; Vol. 6.
3. Green, M.; Emery, K.; King, D., W Warta, S. Igari. Prog Photovolt 2003, 11, 347.
4. Solar Research. http://www.nrel.gov/solar
5. Renewable Energy.
http://www.energy.gov.lk/sub_pgs/energy_renewable_solar_potential.html
6. Skoplaki, E.; Palyvos, J., On the temperature dependence of photovoltaic module
electrical performance: A review of efficiency/power correlations. Solar energy 2009, 83 (5),
614-624.
7. Zondag, H., Flat-plate PV-Thermal collectors and systems: A review. Renewable and
Sustainable Energy Reviews 2008, 12 (4), 891-959.
8. Evans, D., Simplified method for predicting photovoltaic array output. Solar energy
1981, 27 (6), 555-560.
9. Hart, G.; Raghuraman, P. Simulation of thermal aspects of residential photovoltaic
systems; Massachusetts Inst. of Tech., Lexington (USA). Lincoln Lab.: 1982.
10. Evans, D.; Florschuetz, L., Terrestrial concentrating photovoltaic power system
studies. Solar Energy 1978, 20 (1), 37-43.
11. Rosell, J.; Ibanez, M., Modelling power output in photovoltaic modules for outdoor
operating conditions. Energy Conversion and Management 2006, 47 (15), 2424-2430.
12. Jie, J.; Hua, Y.; Wei, H.; Gang, P.; Jianping, L.; Bin, J., Modeling of a novel Trombe
wall with PV cells. Building and Environment 2007, 42 (3), 1544-1552.
13. Tomita, Y.; Furushima, K.; Ochi, K.; Ishizu, K.; Tanaka, A.; Ozawa, M.; Hidaka, M.;
Chikama, K., Organic nanoparticle (hyperbranched polymer)-dispersed photopolymers for
volume holographic storage. Applied physics letters 2006, 88 (7), 071103.
14. Chaniotakis, E., Modelling and analysis of water cooled photovoltaics. Msc
Engineering Systems and the Environment 2001.
15. Akbarzadeh, A.; Wadowski, T., Heat pipe-based cooling systems for photovoltaic
cells under concentrated solar radiation. Applied Thermal Engineering 1996, 16 (1), 81-87.
16. Moharram, K.; Abd-Elhady, M.; Kandil, H.; El-Sherif, H., Enhancing the
performance of photovoltaic panels by water cooling. Ain Shams Engineering Journal 2013,
4 (4), 869-877.
47
4.4 Appendices
4.4.1. Appendix 1 – The Matlab programming code for calculating minimum and
maximum values of the polynomial of power/irradiation vs. temperature
The Colombo data
syms x
>> c = sym2poly(3e-09*x^6 - 8e-07*x^5 + 0.0001*x^4 + 0.2529*x^2 - 4.891*x + 39.054)
c =
0.0000 -0.0000 0.0001 0 0.2529 -4.8910 39.0540
>> minmax(sym2poly(3e-09*x^6 - 8e-07*x^5 + 0.0001*x^4 + 0.2529*x^2 - 4.891*x +
39.054))
ans =
-4.8910 39.0540
>>
48
The Hambanthota data
c = sym2poly(4e-07*x^4 - 6e-5*x^3 + 0.003*x^2 - 0.0592*x + 39.38)
c =
0.0000 -0.0001 0.0030 -0.0592 39.3800
>> minmax(sym2poly(4e-07*x^4 - 6e-5*x^3 + 0.003*x^2 - 0.0592*x + 39.38))
ans =
-0.0592 39.3800
49
4.4.2. Appendix II – The programming code of the Arduino UNO cooling system.
#include <dht.h>
dht DHT;
#define DHT11_PIN 7
int pinOut = 8;
void setup(){
Serial.begin(9600);
pinMode(8, OUTPUT);
}
void loop()
{
int chk = DHT.read11(DHT11_PIN);
Serial.print("Temperature = ");
Serial.println(DHT.temperature);
if (DHT. Temperature >= 45){
digitalWrite(pinOut, HIGH);
}
if (DHT. Temperature <= 35) {
digitalWrite(pinOut, LOW);
}
delay(500);
}
s
