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2008 Design, Installation, and Solar Energy Efficiency Assessment Using a Dual#Axis Tracker by Kaifan Kyle Wang
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FLORIDA STATE UNIVERSITY FAMU‐FSU COLLEGE OF ENGINEERING
DESIGN, INSTALLATION, AND SOLAR ENERGY
EFFICIENCY ASSESSMENT USING A DUAL‐AXIS TRACKER By
KAIFAN KYLE WANG
A Thesis submitted to the Department of Industrial Engineering in partial fulfillment of the requirements for the degree of Master of Science
Degree Awarded: Fall Semester, 2008
Copyright©2008 Kyle Wang All Rights Reserved
The members of the Committee approve the Thesis of KaiFan Kyle Wang defended on November 07, 2008.
______Yaw A. Owusu Professor Directing Thesis
______Samuel A. Awoniyi Committee Member
______Egwu E. Kalu Committee Member
Approved:
______Chuck Zhang, Chair, Department of Industrial Engineering
______ChinJen Chen, College Engineering
The Office of Graduate Studies has verified and approved the above named committee members.
ii ACKNOWLEDGEMENTS
Acknowledgement to the sponsor: Research Center for Cutting Edge
Technologies (RECCET) the laboratory where the research took place. Special thanks to the United States Department of Education in Washington, D.C through the Title III Program for the financial support.
Thanks to my major professor, Dr. Yaw A. Owusu, whose guidance and encouragement helped me with the thesis. Thanks to the other committee members: Dr. Samuel A. Awoniyi and Dr. Egwu E. Kalu whose technical advice and support for making this accomplishment possible. My appreciation also goes to the Dr. Hans Chapman and Mr. Ron Cutwright for their steady assistance throughout the whole project. Appreciation to the schoolmates: Thomas Anthony,
Yaw Nyanteh, Wooden Shanon, Russel Ford and Guillermo Maduro for their assistances for dual‐axis tracker installation and data collection
Last but not least, my profound gratitude goes to my parents, who inspired me to study abroad and to pursue the master degree. Their encouragements have made it possible for me to complete this portion of my education in life.
iii ABSTRACT
Environmental and economic problems caused by over‐dependence on fossil
fuels have increased the demand and request for green energy produced by
alternative renewable sources. Producing electricity by using photovoltaic cells
(also called solar cells) is a fast growing industry. There are two main ways to
make photovoltaic cells more efficient. One method is to improve the materials
design and the other is to optimize the output by installing the solar panels on a
tracking base that follows the sun. This research employed the latter method.
The main purpose of the thesis was to design and assemble of a dual‐axis solar
tracker with a view to assess the improvement in solar conversion efficiency. A
comparative analysis was performed using three systems, i.e., Dual‐Axis
Tracking, Single‐Axis Tracking and Stationary Modules. ‘’Design Expert 6.0”
statistical software was used to process the design of experiment and to
determine the effects of four chosen factors (Tracking or No Tracking, Type of
Modules, Time of the Day, and Weather Condition).
The results showed that the use of the Dual‐Axis Tracking System produced
18% gain of power output, compared with a Single‐Axis Tracking System. The
gain of output power with the Dual‐Axis Tracking System was much higher
(53%) when compared with a stationary system inclined at 30˚ to the horizontal.
A benefit‐cost analysis performed on the three systems showed that the unit cost
of energy produced by the Dual‐Axis Tracker is $0.53, which is reasonable,
considering the state of the technology and the potential added benefit of any
future amortization when employed on a large scale.
iv TABLE OF CONTENTS
List of Tables vii List of Figures viii
Chapter 1: Introduction to Thesis Research 1.0 Introduction 1 1.1 Problem Description 2 1.2 Research Objective 2 1.3 Project Rationale and Benefits 3
Chapter 2: Literature Review 2.0 Introduction to Literature Review 4 2.1 History of Photovoltaic 4 2.2 P‐N Junction 5 2.3 Physics of a Solar Cell 8 2.4 Efficiency of a Solar Cell 9 2.5 Meteorological Factors that Affect Solar Energy Conversion 10 2.5.1 Cloud Cover 11 2.5.2 Turbidity 11 2.5.3 Total Ozone 11 2.5.4 Precipital Water Vapor 12 2.6 Spectral Response of Silicon Solar Cells 12 2.7 Temperature Influence on the Efficiency 15 2.8 Solar Tracking Systems 16 2.9 Crystalline Silicon Based Photovoltaic Cell 19 2.10 Indoor Assessment Procedure(Standard Testing Condition) and Outdoor Testing of Photovoltaic Modules 20 2.11 Mean Time Before Failure (MTBF) of the PV Modules 21 2.12 Photovoltaic System 22
Chapter 3: Methodology 3.0 Introduction 24 3.1 AZ‐125 Dual‐Axis Solar Tracker Installation and System Design 24 3.1.1 Concrete Foundation 24 3.1.2 Gear Motor and Sensor 25
v 3.1.3 System Design 27 3.2 Preliminary Data Collection 29 3.3 Design of Experiments (Choice of Factors and Data Collection) 29 3.3.1 Fish Bone Diagram of Power Output (Voltage and Current) 30 3.3.2 The Controllable Factors 30 3.3.3 The Nuisance Factors 31 3.3.4 Response Variable 32 3.3.5 Data Collection 32
Chapter 4: Data Analysis and Results 4.0 Introduction 36 4.1 Preliminary Data Analysis 36 4.2 The Actual Experiment and Data Analysis 41 4.2.1 General Regression Model of Power Output variables 45 4.2.2 Model Adequacy Checking 46 4.2.3 Model Validation 49 4.3 Performance Improvement Analysis 51 4.4 Cost Analysis of Dual‐Axis Tracker 56
Chapter 5: Concluding Remarks and Future Work 5.1 Summary and Conclusions 58 5.2 Recommendations and Future Work 59
References 60 Biographical Sketch 62
vi LIST OF TABLES
1 Spectrum Response Ranges and Energy Gaps for Various PV Materials 13
2 Comparison of Mono‐crystalline and Poly‐crystalline Silicon 20
3 Indoor Assessment Procedures Conducted on Modules Evaluated 21
4 Choice of Factors and Levels for Design of Experiment 31
5 Data Collected on a Clear Sunny Day 39
6 Data Collected on a Partially Cloudy Day 40
7 Design Matrix and Observed Values of the Responses 41
8 Analysis of Variance Table (ANOVA) for Experiment 44
9 Summary Statistics of Analyzed Experiment 45
10 Sample of Generated Prediction Run 50
Power Output Values and Percentage Difference of 3 Systems Using 11 52 Mono‐crystalline Modules 12 Power Output Values and Percentage Difference of 3 Systems Using 53 Poly‐crystalline Modules 13 Installation Cost Analysis of AZ‐125 Dual‐Axis Tracker 56
14 Comparison of Dual‐Axis Tracker, Single‐Axis Tracker and Stationary 57
vii LIST OF FIGURES
1 Solar cell, Photovoltaic Modules and Photovoltaic Array 1
2 Simplified Diagram of P‐N Junction 6
3 Graph of P‐N Junction of Voltage and Current 7
4 Movement of Electrons in P‐N Junction 7
5 I‐V Characteristic Curve of Solar Cells 9
6 Diagram Shows the Spectrum Wavelength, Frequency and Photo Energy 13
7 Sun’s Apparent Motion in Different Season 17
8 Movement of Passive Tracker and its Structure Scheme 18
9 Dual‐Axis Solar Tracker Combines Two Motions 19
10 Completed PV System 23
11 Schematic Representation of the Dual‐Axis Solar Tracker Foundation 25
12 Diagram Shows the Capability of Movement of AZ‐125 Solar Tracker 26
13 Simplified Schematic Diagram of Light Sensor 27
14 The Schematic Diagram of AZ‐125 System Design 28 15 Fish Bone Chart of Factors Affecting the Power Output 30
16 Two Stationary Modules Inclined at an Angle of Thirty Degrees 33
17 The Single‐Axis Tracker Used for Data Collection 34
18 The Dual‐Axis Tracker Used for Data Collection 35
19 The Power Curves of Three Systems on a Clear Sunny Day 37
20 The Power Curves of Three Systems on a Partially Cloudy Day 38
21 Half Normal Probability Plot of Effects 43
22 Normal Probability Plot of Residuals for Experiment 46
23 Plot of Residuals versus Predicted Response for Experiment 47
24 Plot of Residuals versus Run Number for Experiment 48
25 Outlier T Plot for Experiment 48
viii 26 Interaction Graph of CD for Experiment 49
27 Diagram of Power Output Curves for 3 Systems Using Mono‐crystalline 54 Modules 28 Diagram of Power Output Curves for 3 Systems Using Poly‐crystalline 55 Modules
ix CHAPTER 1
INTRODUCTION TO THESIS RESEARCH
1.0 Introduction
Industrial and domestic reliance on the use of fossil fuel is today facing
challenges in demand and environmental consideration. Faced with a possibility of scarce oil resources and increasing concern about its harmful byproducts, such as toxic pollution, global climate change and acid rain, awareness of using renewable energy is growing.
There are many kinds of renewable energy sources like solar, hydrogen fuel cell, wind, biomass and geothermal. Solar energy technology is one of the promising sources of future energy supplies because it is clean and remarkably abundant. Solar energy can be converted into electricity through the solar cells.
Individual cells (Figure 1(a)) are assembled to make photovoltaic modules
(Figure 1(b)). Several modules can be linked in photovoltaic arrays (Figure 1(c)).
(a) (b) (c)
Figure 1.1: (a) A solar cell, the smallest unit to convert solar energy into electricity. (b) A photovoltaic module is a packaged interconnected assembly of solar cells. (c) A photovoltaic array is a linked assembly of PV modules.
1 1.1 Problem Description
Photovoltaic energy involves the conversion of sunlight into electricity. The
efficiency of converting radiant solar energy into electrical energy is the critical
point that influences the choice of solar energy as a form of alternative energy.
There are two ways to improve the PV technology performance. One is to use
different materials or add other dopants to manufacture the PV modules. The
other one is to use a tracker as the device for orienting a solar PV module toward
the sun.
This thesis focuses on using two types of solar tracking systems, single and
dual‐axis tracker as well as a stationary system to compare the output of mono‐
crystalline and polycrystalline silicon PV modules. In order to achieve the
highest conversion efficiency, the sun light has to impinge the module surface
perpendicularly. The earth not only has one year rotational motion around the
sun, but also a daily motion around its own axes. Passive tracker only meets the
requirement of the change in day and night but not the seasons. It is for this reason that a dual‐axis tracker is employed in this work. Other important factors
to be considered in the experiment design are; Material Type (mono‐crystalline
and poly‐crystalline), Time of the Day (10:00AM and 02:00PM), and Weather
Condition (Fair and Cloudy).
1.2 Research Objective
[Anthony, 2006] showed that a single‐axis solar tracker can improve the
efficiency of mono‐crystalline solar modules by 24% at 00 to the horizontal and
by 37% for those at 400 to the horizontal. This work involves the use of an AZ‐
2 125 Azimuth Dual‐Axis Solar Tracker as a means of further increasing the energy conversion efficiency compared with the single‐axis tracker and stationary module.
The four main objectives are shown as follows:
1. To design and install an AZ‐125 Dual‐Axis Solar Tracker for monitoring the
increase in the energy conversion efficiency.
2. To measure the output (voltage and current) of photovoltaic modules using
multi‐meter.
3. To assess the improvement in solar energy conversion efficiency and
demonstrate at least 15% increase compared with single‐axis tracker.
4. To perform a cost analysis of the dual‐axis tracker in comparison to the
single‐axis tracker and stationary modules, thereby validating the benefit of
dual‐axis tracking.
1.3 Project Rationale and Research Benefits
The reason for employing the dual‐axis tracker is that the sun s position in the sky varies both with the seasons (elevation) and time of day as the sun moves across the sky. Although the passive (single‐axis) tracking can increase the efficiency over stationary modules, its main limitation is the inability to perfect align the modules toward the sun’s direct path. This research focuses on designing a solar tracker system to maximize the conversion of solar irradiance to electrical energy output. It is envisioned that this research will also make solar technology more competitive and affordable in remote areas without grid access.
3 CHAPTER 2
LITERATURE REVIEW
2.0 Introduction to Literature Review
This section of the thesis contains a review of the literature related to History of Photovoltaic, Physics of Solar Cell (voltage and current characteristics),
Efficiency Assessment of Solar Cell, P‐N Junction of Semiconductor Silicon. It also covers Meteorological Factors which Affect the Solar Energy Conversion
Performance (Cloud Cover, Turbidity, Total Ozone, and Precipital Water Vapor),
Spectral Response of a Silicon Solar Cell, Temperature Influence on the Efficiency.
Other sections discuss Solar Tracking Systems (passive and active), Crystalline Silicon
Based Photovoltaic Cell and Indoor Assessment Procedure (Standard Test
Conditions) and Outdoor Test of Photovoltaic Modules, Mean Time Before
Failure (MTBF) of the Photovoltaic Modules and Completed Photovoltaic System.
2.1 History of Photovoltaic
Ancient Greeks and Romans had already known how to take advantage to the sun. The Greeks used the sun to light and heat indoor space. The Romans advanced the art of harnessing solar energy by covering south facing building openings with glass or mica to hold in the heat of the winter sun. These helped
Greeks and Romans to offset the need of woods. The word “Photovoltaic”, originates from two terms. The term photo is a word from the Greek phos,
which means light. Volt is named for Alessandro Volta, the pioneer of
electricity study who was known especially for the development of the first electric cell in 1800. Photovoltaic means the conversion of solar light into electricity. The
4 next paragraph highlights some memorial milestones in the history of photovoltaic developing.
Around 1838, Edmund Becquerel, a French physicist discovered the photovoltaic effect,
i.e., exposing certain materials to light produced small amounts of electric current.
Latter, in 1883, the first photovoltaic solar cells made from selenium wafers were created
by Charles Fritts, an American inventor. In 1954, Bell Laboratories observed the light
sensitivity of silicon which led to the first practical solar modules discovery with conversion
efficiency around 4.5%. The New York Times reported the discovery as the
beginning of a new era, leading eventually to the realization of harnessing the
almost limitless energy of the sun for the uses of civilization.” The energy crisis
and oil embargos of the 1970’s made many nations aware of their dependency on
controlled non‐renewable energy sources and this fueled exploration of
alternative energy sources. This included further research into renewable
sources such as solar power. When it comes to 21st century, the application of
photovoltaic takes off due to the carbon emissions reductions.
2.2 P‐N Junction
The P‐N junction is the most important of a standard silicon (Si) solar cell.
Approximate 2×1016//cm3 acceptor atoms (such as boron) or approximate 1019/cm3
donor atoms (such as phosphorous) are substituted for the silicon atoms while
making solar cell. The p‐type silicon is the silicon with one less valence electron.
On the other hand, the n‐type is the silicon with an extra valence electron (See
Figure 2.1).
5
Figure 2.1: Simplified diagram of P‐N junction. The free electrons are represented by “‐”sign in the N‐type layer. The holes in the P‐type crystal are shown as “+” sign.
If a piece of P‐type silicon is placed in contact with a piece of N‐type silicon, the extra electrons in the N region will seek to lose energy by filling the holes in the P region. This leaves an empty zone, or depletion region. And this action makes the imbalance. Electrons move from the N‐side to the p‐side. This causes the N‐side to be a positive side carrying positive charge and P‐side as the negative side carrying negative charge. Now, if the voltage is applied to the outside ends of PN crystal, the negative voltage applied to the N‐type end and positive voltage to the P‐type end. This action shrinks the depletion region and be called forward bias. The electrical current now flows through the junction in the forward direction, but not in the reverse direction. This is the basic nature of an ordinary semiconductor diode. On the contrary, the positive voltage is applied to the N‐type material and the negative to the P‐type end, it makes all available current carriers are attracted away from the junction and a bigger depletion region. This is so called reverse bias.
In the Figure 2.2, a P‐N junction is rectified, the current is allow to flow only one way and increase rapidly as the voltage increases when applying the forward bias. But if we apply the reverse bias, the current will not increase until reach the breakdown voltage.
6
Figure 2.2: Graph of P‐N junction of voltage and current.
When it comes to a solar cell, sunlight strikes a cell and creates the charge carriers (electrons), the electric field make the electrons to flow from N‐side to P‐ side. This electric field established across the P‐N junction creates a diode that promotes current to flow in only one direction across the junction (See Figure 2.3).
Figure 2.3: A simplified diagram of P‐N Junction and movement of electrons
[Energy Information Administration, 1998].
7 2.3 Physics of a Solar Cell (Voltage and current Characteristics of Solar Cell)
There are two important quantities to characterize a solar cell, open circuit voltage (VOC) and short circuit current (ISC). The former is the difference of electrical potential between two terminals of a device when there is no external load connected (infinite load resistance). The latter is the current when the
terminals are connected to each other (zero load resistance). When the load is
connected to the solar cell, the current decreases but the voltage starts to build at
terminal. The resulting current is the effect of short circuit current and dark
current. Dark current here is defined as the constant response exhibited by the
solar cell of radiation during periods when it is not exposed to light and its
density is given by the formulae bellow [Markvart, 2000]:
qV / K T JDARK (V) = J0 (e B ‐1) 2.1
where,
J0 is a constant, q is the electron charge, V is the voltage between the
terminals, KB is the band gap and T is the absolute temperature.
From Equation 2.1, the resulting current is given in Equation (2.2):
qV / K T J = Jsc ‐ J0 (e B ‐1) 2.2
The open circuit voltage, Voc. is expressed as:
K BT J sc Voc= ln ( +1) 2.3 q J o
8 2.4 Efficiency of a Solar Cell
In solar energy technology, the efficiency of a solar cell is the most important consideration. Define solar energy conversion efficiency, it is the percentage of power converted (from absorbed light to electrical energy) and collected, when a solar cell is connected to an electrical circuit. Figure 2.4 shows the current voltage
(I‐V) characteristic curve of a solar cell made of semiconductor. Before the m stage, the power density (Pd) increases linearly while the voltage increases.
Because of rising of the temperature, circuit parameter called the resistance changes. The big drop of power density happens after the m stage. The law of efficiency is expressed in Equation 2.4 and 2.5 at maximum power stage.
m
Figure 2.4: Current‐voltage (I‐V) characteristic curve of solar cells made of semiconductor [Green, 1982].
9
JmVm η= 2.4 Ps
and
Ps=EAc 2.5
where,
Jm is the current density, Vm is the voltage at m point, E is the input light
irradiance (in W/m²) under standard test conditions (STC) and Ac is the
surface area of the solar cell (in m²).
The Fill Factor (FF) is another way to describe the solar cell efficiency. Fill
Factor is defined as the ratio of the actual maximum obtainable power to the
theoretical power. It is given in Equation 2.6 [Green, 1982]:
JmVm FF= 2.6 JscVoc
and efficiency of solar cell can be rewritten as :
JscVocFF η= 2.7 Ps
2.5 Meteorological Factors That Affect Solar Energy Conversion
Sunlight is composed of photons and other particles of solar energy. As solar
irradiance passes through an ideal (clean and dry) atmosphere, it gets attenuated
by permanent atmospheric constituents, whose content is nearly invariable. They
may be reflected, or absorbed by solar cell. There are several environmental
10 factors that have significant influence on the solar energy conversion
performance. The major ones are: Cloud Cover, Turbidity, Total Ozone, and
Precipital Water Vapor [Case, 2007].
2.5.1 Cloud cover
The presence of cloud adversely affects solar energy conversion efficiency.
Clouds can block a significant portion of the sun’s incoming radiation from
reaching the earth’s surface. The amount of incoming solar energy reflected into the space is related to the thickness of cloud cover. The thicker the cloud cover, the greater the decrease in efficiency. The cloud thickness is of more significant than the altitude of the cloud. Thickness here refers to how much light the cloud can intercept, rather than a specific physical thickness. During warm weather, cumulus clouds often grow quite large during the afternoon which influences the power output of solar modules [NASA, 1997].
2.5.2 Turbidity
In the solar energy conversion calculation, the turbidity factor is always
involved. The turbidity attributes to the quantity of aerosols (particles from 0.1 to
1+ microns diameter) that are generally invisible to the naked eye in the
atmosphere. Turbidity Factor is between o and 1. If the sky is perfectly clear, the
value of turbidity is 0. On the contrast, in an opaque condition, the value is 1.
2.5.3 Total Ozone
Total ozone is the total amount of ozone present in a column of the earth’s
atmosphere, often expressed in Dobson units. And here One DU is 2.69×1016
ozone molecules per square centimeter. Ozone absorbs incoming solar UV
11 radiation from 30 to 50 km above the surface of earth. It captures light with
wavelengths shorter than 310 nm that are atmospherically important.
2.5.4 Precipital Water Vapor
Unlike humidity represents the wetness of the atmosphere at sea level,
precipital water vapor means the total atmospheric water vapor contained in a
vertical column of unit cross‐sectional area extending between any two specified
levels, commonly expressed in terms of the height to which that water substance
would stand if completely condensed and collected in a vessel of the same unit
cross section. Since it influences total intensity and spectral distribution of
sunlight, precipital water vapor is regarded as one of the atmospheric variations that relates to the efficiency of solar cells.
2.6 Spectral Response of Silicon Solar Cells
The sun acts as a perfect emitter and releases the most energy as visible light
(wavelengths from about 400‐800 nm). Each wavelength corresponds to the
specific frequency and the energy released. In Figure 2.5, the shorter the
wavelength, the higher the frequency and energy it has (which is expressed in
electron‐volts, or eV). In the visible solar spectrum, the violet light is at high‐
energy compared to the red light which is at low‐energy. Solar cells respond differently to the different wavelengths, or colors, of light. Crystalline silicon is now the most commonly used material for manufacturing the modules. Silicon has the ability to be effective in the entire visible spectrum plus some part of infrared spectrum (Table 2.1). However for most long‐wavelength infrared spectrum, the energy is too low to produce current flow. Higher‐energy radiation
(ultra‐violet) can produce current flow, but much of this energy is likewise not usable since most of it transforms into heat but not produce electricity.
12 Table 2.1: Spectrum Response Ranges and Energy Gaps for Various PV Materials [Gottschalg, 2003].
Photovoltaic Material Spectrum Response Energy gap
Range
Amorphous Silicon (a‐Si) 300‐780nm 1.17eV
Cadmium Telluride (CdTe) 300‐900nm 1.45eV
Crystalline Silicon (c‐Si) 300‐1100nm 1.12eV
Copper Indium Gallium Diselenide (CIGS) 300‐1360nm 1.05eV
Figure 2.5: The diagram shows the spectrum wavelength, frequency and photon energy.
Spectral response [Singh, 2003] means the probability the absorbed photon will
yield a carrier to the photogenerated current of the cell. The spectral response (SR
λ) and internal quantum efficiency (Qi) of a solar cell are expressed as:
Jsc, λ SRλ= 2.8 Pi
13 and Jsc Qi= 2.9 qnph 1( − Rλ)
where,
Jsc,λ is the short‐circuit current density of radiation of wavelength λ. Pi is
the intensity of the incident monochromatic radiation, nph is the incident
photon flux.
The main purpose of measuring spectral response of a cell is to determine the following three cell parameters:
(i) Short‐circuit current density, Jsc, for a given spectrum
(ii) Minority carriers diffusion length, Lb, of the base region
(iii) The apparent dead Layer, Xd, of the cell
For the conventional single‐junction solar cell, its conversion ability is around the value between the incident solar spectrum and the spectral absorption of the material. As band gap (Eg) is main the criterion. The term band gap refers to the energy difference between the top of the valence band and the bottom of the conduction band; electrons are able to jump from one band to another. In order for an electron to jump from a valence band to a conduction band, it requires a specific amount of energy for the transition. Photons with energy (Eph) less than
Eg will not be absorbed. For photons with energy exceeding the band gap, Eph>Eg, only energy equal to the band cap can be absorbed. The amount of energy, Eph‐Eg, transfers to the heat. In modern times, people use nanotechnology, employing planar plastic converters with semiconductor nanocrystals in it, to improve the spectral response of solar cell [Vab Sark, 2004]. For example CdSe Quantum
14 Dot(QD’s) have been dispersed in the highly transparent plastics, the QD’s improve the solar cell efficiency by means of absorption and re‐emission in the wavelengths where the spectral response of the solar cell is low to wavelengths where the spectral response is high (emission wavelength can be tuned by QD’s size). The multi‐crystalline solar cell showed an increase in short‐circuit current by 10%.
2.7 Temperature Influence on the Efficiency
Previous studies suggest that the most important factor influencing efficiency of PV modules is temperature inside the P‐N junctions of semiconducting solar cells. Generally speaking, the higher operation temperature, the worse the module performance. Modules loose up to 7% of their power when operating at temperatures of the order of 40 °C [Gxasheka, 2004]. Although there is an increase in current with temperature, the overall effect of increased temperature is a decrease in power due to the larger decrease in the voltage. The following equations illustrate poor efficiency when cell temperature increases. First, the increase in temperature results in a decrease in the semiconductor band gap
(Equation 2.10) and results in a decrease in the density of short‐circuit current
[Olchowik, 2006].
βT 2 E(T) = Eg(0) ‐ 2.10 T + γ
where,
Eg(0) is the energy band gap at T = 0, and are the coefficients for a
15 specific semiconductor.
As mentioned in Section 2.4, Fill Factor (FF) describes the solar cell efficiency. In
Equation 2.11, with the increase in temperature, the fill factor decreases.
∂FF q ∂U U ∂FF Eg o)( + 3kT ∂FF = ( oc ‐ oc ) = 2.11 ∂T kT ∂T T ∂U kT 2 ∂U
and qU U= oc 2.12 kT
where,
Uoc is the temperature dependence of the open circuit voltage.
2.8 Solar Tracking Systems
The sun s position in the sky varies both with the seasons (elevation) and time of day as the sun moves across the sky. It is important to know the background of Sun‐Earth geometrical relation. There are two motions should be considered.
(i) Seasons result from the yearly revolution of the Earth around the Sun
(elliptical trajectory). It is responsible for the altitude variation of the sun
on the celestial sphere during one year (Figure 2.6).
(ii) The earth rotates, spinning on its axis, thereby resulting in day and night.
This is accounted for by the east‐west daily path of the sun.
16
Figure 2.6: Sun s apparent motion as seen from the northern hemisphere.
A solar tracking system is the device for orienting solar PV modules toward the sun. At all times that the sun is visible in the sky. It shows better effectiveness compared to modules in fixed positions. There are many types of solar trackers.
The following are two most commonly used trackers, passive single‐axis tracker and active dual‐axis tracker [Comsit, 2007].
(i) The dual‐axis tracker is more complicated than the single‐axis tracker. The
components of AZ‐125 Dual‐Axis Azimuth Solar Tracker include a gear, a
motor and a sensor. It combines two motions (Figure 2.7) thereby
providing higher accuracy when tracking. Instead of continuous motion,
the AZ‐125 dual‐axis tracker is moved in discrete steps. So it is a very low
power consumption device(less than 10 watt‐hours per day). A more
comprehensive description of the AZ‐125 will be introduced in Chapter 3.
(ii) The dual‐axis tracker is more complicated than the single‐axis tracker. The
17 components of AZ‐125 Dual‐Axis Azimuth Solar Tracker include a gear, a
motor and a sensor. It combines two motions (Figure 2.7) thereby
providing higher accuracy when tracking. Instead of continuous motion,
the AZ‐125 dual‐axis tracker is moved in discrete steps. So it is a very low
power consumption device(less than 10 watt‐hours per day). A more
comprehensive description of the AZ‐125 will be introduced in Chapter 3.
Figure 2.6: Movement of passive tracker and its structure scheme.
18
Figure 2.7: Dual‐axis solar tracker combines two motions while tracking.
2.9 Crystalline Silicon Based Photovoltaic Cell
Crystalline silicon remains the major material in the photovoltaic marketplace
with 90% of the market, despite the development of a variety of thin film
technologies [Markvart, 2000]. The advantages of crystalline silicon are its good efficiency, stability, material abundance and low toxicity. Most silicon cells have been fabricated using thin wafers cut from large cylindrical mono‐crystalline ingots prepared by the exacting Czochralski (CZ) crystal growth process and doped to about one part per million with boron during ingot growth. Multi‐ crystalline wafers are sliced from ingots prepared by a simpler casting technique, which produces large‐grained poly‐crystalline ingots. These boron‐doped starting wafers generally have phosphorus diffused at high temperatures a fraction of a micron into the surface to form the P‐N junction required. Contacts
19 to both the N‐ and P‐type side of the junction are then made by including metal pastes [Martin, 2001]. Mono‐crystalline silicon, having a single and continuous crystal lattice structure with practically zero defects or impurities shows the best conversion efficiency‐ around 15%. Polycrystalline, with the good mobility and can be orders of magnitude larger, has the efficiency around 12%. The following table 2.2 compares the two types of silicon material.
Table 2.2: Comparison of Mono‐crystalline and Poly‐crystalline Silicon [Case, 2007].
Mono‐crystalline silicon Poly‐crystalline silicon
Cell thickness (μm) 200‐400 70‐140
Module strength Strong Very strong
Outdoor performance Good Good
Production cost Expensive Average
Cell efficiency(%) 15‐20 10‐14
2.10 Indoor Assessment Procedure (Standard Test Condition) and Outdoor
Testing of Photovoltaic Modules
Performance of photovoltaic (PV) modules is evaluated under the standard test condition (STC). It is defined by the American Society for Testing Materials
(ASTM). It includes:
(i) Incident solar irradiance: 1 kW/m2
(ii) Solar spectrum distribution: AM1.5G
(iii) Module temperature: 25 °C
20 In this standard test condition for the modules’ performance, a set of baseline
data can be used for the future reference. The assessment procedure we used in this study is summarized in Table 2.3.
Standard Test Condition rarely meets actual outdoor conditions and the environmental conditions greatly affect the output energy of PV modules.
Influenced by all the meteorological factors, the modules exposed to the outdoor environment did not perform as expected. For example, the effect of temperature on PV module performance is often neglected, but studies have shown that it cannot be ignored. Modules lose up to 7% of their power when operating at temperatures of the order of 40 °C.
Table 2.3: Indoor Assessment Procedures Conducted on Modules Evaluated [Gxasheka, 2004].
Assessment Objective
Visual inspection Inspect module imperfections
STC I–V curves Test electrical performance
Temperature dependence Determine coefficients
Irradiance measurements Evaluate cell shunting characteristics and
performance at low irradiance
2.11 Mean Time Before Failure (MTBF) of the PV Modules
The phenomenon of degradation relates to the lifetime of PV modules.
Repeated accelerated lifetime tests have been undertaken in an effort to estimate the lifetime. The visual defects happened in first twenty years include: (i) Sealant infiltration detected on 76% of modules, usually observed along upper and lower module edges
21 (ii) Cracked cells, on 15% of panels (iii) Bad seal of the junction box on the back sheet with the consequent risk of detachment when opening, so leading to a loss in insulation resistance. (iv) Oxidation of the terminal connections leading to higher electrical resistance. Severe oxidation can, in worst cases, cause detachment when wiring.
Experiments (test electrical characteristics) had been done to accurately estimate
PV modules degradation. Results show that, after about twenty years, 59% of the modules exhibited a variation of less than ‐10% of the stated nominal power, 35% of modules exhibited a variation of between ‐10% and ‐20%, and only for the 6% of modules showed a variation loss greater than ‐20% [Realini, 2001]. Regarding the determination of the Mean Time Before Failure of the PV modules, accelerated lifetime tests help assume, that the modules could continue to provide useful electrical power for another 10‐15 years. The total life time of PV modules can last 30 to 35 years.
2.12 Photovoltaic System
The photovoltaic systems with battery storage are especially suitable in areas where utility power is unavailable or utility line extensions would be too expensive. The batteries used in PV systems are similar to car batteries, but they re built somewhat differently to allow more of their stored energy to be used each day (deep cycling). The ability to store PV‐generated electrical energy makes the PV system a reliable source of electric power both day and night, rain or shine. In Figure 2.8, a simple electrical device called a charge controller is
22 between the PV array and battery system. It keeps the batteries charged properly and helps prolong their life by protecting them from overcharging or from being
completely drained. In order to operate ac appliances, an inverter (between the
batteries and the load) should be employed. Although a small amount of energy
is lost in converting dc electricity to ac, an inverter makes PV‐generated
electricity behave like utility power.
Figure 2.8: Completed PV system includes PV array, charge controller, battery system, inverter and loads.
23 CHAPTER 3
METHODOLOGY
3.0 Introduction
In this chapter, the details of AZ‐125 Dual‐Axis Solar Tracker installation and
system design are presented. The focus of this thesis is to install the dual‐axis
tracker to assess its improvement in terms solar efficiency. “Design Expert 6.0”
software was used in the design of experiments and subsequent analysis of
variance. Four factors (i.e.Tracking or no tracking, Type of module, Time of day
and Weather condition) were chosen and investigated their influence on the response (current and voltage).
3.1 AZ‐125 Dual‐Axis Solar Tracker Installation and System Design
3.1.1 Concrete foundation
Digging an appropriate sized hole and providing a stable foundation to the tracker pipe is critical to the installation project. While concrete is often one of the very base foundations of any structure, its stability relies on proper site preparation. A rule of thumb is to have one of third of the pipe mounted into the concrete foundation. The dimension of foundation is shown in Figure 3.1. The 6”
ID, Schedule 40 Steel Pipe had been set in the center with two 3 foot “anti‐ rotation” re‐bars go through it.
24 (a) (b)
Figure 3.1: (a) Schematic representation of the side view of the dual‐axis solar tracker on its concrete foundation showing dimensions in inches. (b) A solid diagram of dual‐axis solar tracker on its concrete foundation.
3.1.2 Gear motor and sensor The gear motor was installed onto the top of the pipe, making sure that the
motor points to the “true north” while the location is at northern hemisphere.
The sun’s trajectory path and movable ability of solar tracker were both taken into consideration. Solar modules are usually set up to be in full direct sunshine at the middle of the day facing South in the Northern Hemisphere, or North in the Southern Hemisphere. True North is the direction marked in the sky by the
North Celestial Pole, which is different from Magnetic North, the direction the compass needle points to. For the AZ‐125 solar tracker, it is able to have 270 degrees of azimuth rotation and 5 to 75 degrees of elevation. See Figure 3.2.
25
Figure 3.2: The AZ‐125 solar tracker shows the capability of 5 to 75 degrees of a vertical‐axis and 270 degrees of a horizontal‐axis tracking arc.
This light‐sensing AZ‐125 solar tracker is fitted with photosensors, the
photodiodes that require equal levels of light while sensing. The following simplified
schematic diagram (Figure 3.3) of a vertical‐axis solar tracker is fitted to
photovoltaic modules. Its pair of sensors point to the East and West of the
location of the Sun. The light detected by the Eastward‐facing sensor is at a lower
intensity compared to that detected by the Westward‐facing sensor. Therefore, the module must be turned westwards (by the motor controlled by the solar tracker circuit) until the levels of light detected by both the East and the West sensors are equal. At that point the module will be directly facing the sun and generated electricity optimally.
26
Figure 3.3: Simplified schematic diagram of a vertical‐axis solar tracker fitted to a photovoltaic module. The west sensor detects higher intensity of sun light than east sensor.
3.1.3 System Design
The controller is the device connected to the sensors and motor. There are two options to power the controller: The first is to employ the battery banks to provide 270 degrees of azimuth tracking. The other is to power from the array to provide only 180 limited tracking arc. The AZ‐125 tracker controller input power range is 23 to 50 volts DC and less than 5 amps. 2‐12 VDC series batteries are chosen to be the power resource of the controller to provide 24 volt and get maximum tracking arc.
Four new crystalline silicon modules were mounted on the tracker to charge batteries. The specifications show each module to have 17.4 rated voltage and
5.02 rated current. In order to charge the 24 volt battery bank, consecutive voltage was provided to make the total voltage exceed 24 volt. Two pairs of
27 photovoltaic modules were placed in parallel, each pair connected first in series.
The combination of series and parallel modules provide double voltage and
current to support the whole system. Between the PV arrays and battery bank,
there is a voltage regulator. A voltage regulator converts varying input voltage
and produces a constant regulated output voltage. The voltage regulator can
guarantee longer battery life and improved system performance. Figure 3.4 is a
schematic drawing of the set up for the AZ‐125 dual‐axis solar tracker.
Figure 3.4: The schematic diagram of AZ‐125 system design including battery bank, voltage regulator, 4 PV modules and tracker controller.
28 3.2 Preliminary Data Collection
After the dual‐axis solar tracker was successfully installed, a preliminary
experiment to test the operation of the tracker was performed. The data were
collected using the mono‐crystalline and poly‐crystalline modules mounted on
the dual‐axis tracker every hour from 9AM to 5PM for five consecutive days. The
voltage and current output were measured by the multi‐meter. Results of the
preliminary testing are showed in Section 4.
3.3 Design of Experiments (Choice of Factors and Data Collection)
Data collection and analysis are essential to the thesis. In this research, the
relied data was collected first and followed by statistical analysis using statistical tools and software. There are two main parts of the experiments. First, Design of
Experiments is the discipline to consider interested factors which are going to have effect on the output performance of the PV modules. The data was collected from mono‐crystalline and poly‐crystalline modules mounted on the AZ‐125 dual‐axis tracker and at an angle of thirty degrees. Design‐Expert 6.o software was used for generating an experimental design matrix and subsequent analysis.
The second part of experiments was to perform a comparative analysis of the energy conversion efficiencies of the dual‐axis and single‐axis trackers compared to the stationary one. Both mono‐crystalline and poly‐crystalline modules were used for the test. Data was collected every one hour from 8AM (0800 hrs) to 7PM
(1900 hrs) for a period of 30 days. Then Excel software is employed to analyze the difference and improvement.
29 3.3.1 Fish Bone Diagram
Factors related to the performance of the Dual-Axis Solar Tracker are presented in the Fish Bone Diagram below (Figure 3.5).
Method Human Resource Material Tracking or Non tracking Time of Day Poly‐crystalline Operator Mono‐crystalline Output Power (vols &s) Temperature Wind Speed Current Sensor Actuator Water Vapor Gear Weather Station B Pressure Motor Cloud Cover Supporting Structure Voltage Sensor
Environmenta Machine Measuremen
Figure 3.5: Fish bone chart of factors affecting the power output of solar modules.
30 3.3.2 The Controllable Factors
With a designed experiment being chosen as the procedure leading to the
analysis, the four main factors were selected and are listed in Table 3.1. The first
factor is using dual‐axis tracking or not. The second factor was material type, i.e.,
mono‐crystalline and poly‐crystalline modules. Next, emphasis was placed on different time of day, 10AM and 2PM. Last but not least, different weather
conditions, fair and cloudy were chosen.
Table 3.1: Choice of Factors and Levels for Design of Experiment.
Choice of Factors and Levels
Factors Levels
1 Tracking or Non Tracking Yes, No
2 Time of Day 10AM, 2PM
3 Type of Module Mono‐crystalline , Poly‐crystalline
4 Weather Condition Fair, Cloudy
3.3.3 The Nuisance factors
The nuisance factors are those that may influence the experimental response
but in which we are not directly interested. Nuisance factors were considered as uncontrollable ones. The experiments in this work were conducted outdoors. All uncontrollable factors known to have effects on electrical output were assumed constant.
31 Cloud cover reduces the solar irradiance to reach the PV modules and restrict
the power output. In the case of turbidity, the lower the quantity of aerosols in the air, the better for the solar panels efficiency. Precipital water vapor influences the total intensity and spectral distribution of sunlight. It is also one of the atmospheric variations that is related to the efficiency of solar cells. Temperature negatively affects the efficiency of PV conversion.
3.3.4 Response variable
Power output (in watts) is a measure of the rate of energy performance. The data collected in the experiments were measured voltage and current. Power output (in watts) is a function of voltage and current.
3.3.5 Data Collection
The data were collected using a configuration as shown in Figures 3.6 (a), (b) and (c) respectively. For the purpose of this project, only voltage and current
were collected by multi‐meter. The information of weather conditions were got
from the website ”The Weather Channel”.
32
Figure 3.6 (a): The two types of PV modules used in this research in the “stationary” position. The mono‐crystalline module is at the right and polycrystalline at the left of the picture.
33
Figure 3.6 (b): Single‐axis solar tracker mounted with mono‐crystalline PV module on the rack. The tracker is located behind the Centennial Building, Innovation Park, Tallahassee, FL.
34
Figure 3.6 (c): The dual‐axis solar tracker used for thesis research. The tracker is located behind Portable 410, FAMU‐FSU College of Engineering.
35 CHAPTER 4
DATA ANALYSIS AND RESULTS
4.0 Introduction
In this chapter, the preliminary data show the different power output on two different weather conditions and demonstrate the dual‐axis tracker performs better power conversion efficiency. Design of Experiment is used to prove the
significance of four chosen factors. And performance assessment and analysis of cost for three systems (i.e. Stationary, Single‐Axis Tracker and Dual‐Axis Tracker) show the benefit of employing the Dual‐Axis Tracker.
4.1 Preliminary Data Analysis
Preliminary data was collected before the start of main experiment. Table 4.1 and 4.2 show the variation of power output (Watts) for 3 systems, i.e., Dual‐Axis
Tracker, Single‐Axis Tracker and Stationary Modules using two types of modules
on two different weather conditions. Figure 4.1 and 4.2 show the combination of
the power curves of Table 4.1 and 4.2.
In the Figure 4.1, three power curves increase from the morning to the
afternoon. The peak power output exist at 13:00. The three power curves show a
general bell shape. However, in Figure 4.2, the power curve showed a significant
dip around 16:00. The preliminary experiment was conducted outdoors. As a
result, a number of environmental conditions were taken into consideration. The
cloud cover or the rising of the operation temperature will all decrease the PV
module energy conversion efficiency. At September 6th, there was a scatter
36 thunder storm in the afternoon and could case the decrease of power output. In summary, the dual‐axis tracker shows better energy conversion efficiency most times during the day. This finding shows that the dual‐axis tracker works properly and orients the PV modules perpendicularly to the sun.
Preliminary Data: Time of the Day vs Power Output (Watts) on a Clear Sunny Day 50.00 45.00
40.00 Stationary (W atts)
35.00
30.00 Single‐ 25.00 Axis Output 20.00 Tracker
15.00 Dual‐Axis Tracker Power 10.00
5.00
0.00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00
Time of the Day
Figure 4.1: The power curves for 3 systems using mono‐crystalline modules on a clear Sunny Day.
37
Preliminary Data: Time of the Day vs Power Output (Watts) on Partially Cloudy Day 45.00 40.00 35.00 30.00 Stationary 25.00 Output
20.00 Single‐Axis 15.00 Tracker Power 10.00 Dual‐Axis 5.00 Tracker 0.00
0 0 00 0 00 0 0 0 8:0 9:0 :00 :00 :00 10 11:00 12: 13 14:0 15: 16:0 17:0 18 19:0 Time of the Day
Figure 4.2: The power curves of 3 systems using poly‐crystalline modules on a partially cloudy day.
38
Table 4.1: The Variation of Power Output (Watts) for 3 Systems Using Mono‐crystalline modules on a Clear Sunny Day.
Mono‐crystalline PV Modules (2008/09/13) Stationary Module Single‐Axis Tracker Dual‐Axis Tracker Time Voltage (V) Current (A) Power (W) Voltage (V) Current (A) Power (W) Voltage (V) Current (A) Power (W) 8:00 6.33 0.03 0.19 6.88 0.04 0.28 7.04 0.07 0.49 9:00 8.24 0.82 6.76 10.39 1.64 17.04 14.40 1.83 26.35 10:00 14.60 1.73 25.26 17.90 1.88 33.65 19.00 2.12 40.28 11:00 16.60 1.89 31.37 18.49 1.98 36.61 19.66 2.15 42.27 12:00 16.70 1.85 30.90 18.40 1.95 35.88 19.88 2.19 43.54 13:00 17.26 1.87 32.28 18.45 2.04 37.64 19.93 2.32 46.24 14:00 17.24 1.62 27.93 18.26 1.96 35.79 19.27 2.03 39.12 15:00 17.33 1.67 28.94 18.03 1.96 35.34 18.44 1.98 36.51 16:00 14.85 1.25 18.56 17.30 1.87 32.35 18.33 1.94 35.56 17:00 6.14 0.85 5.22 13.42 1.33 17.85 17.40 1.89 32.89 18:00 6.13 0.55 3.37 6.35 0.68 4.32 9.33 1.29 12.04 19:00 6.05 0.01 0.06 6.31 0.01 0.06 6.89 0.02 0.14
39 Table 4.2: The Variation of Power Output (Watts) for 3 Systems Using Poly‐crystalline Modules on a Partially Cloudy Day.
Poly‐Crystalline PV module (2008/09/06) Stationary Module Single‐Axis Tracker Dual‐Axis Tracker Time Voltage (V) Current (A) Power (W) Voltage (V) Current (A) Power (W) Voltage (V) Current (A) Power (W) 8:00 5.17 0.01 0.05 5.29 0.01 0.05 5.88 0.01 0.06 9:00 5.39 0.60 3.23 6.87 1.14 7.83 11.42 1.52 17.36 10:00 12.82 1.64 21.02 15.21 1.85 28.14 17.63 1.90 33.50 11:00 15.34 1.70 26.08 15.92 1.90 30.25 18.43 1.99 36.68 12:00 15.80 1.79 28.28 17.43 1.95 33.99 18.89 2.00 37.78 13:00 17.26 1.97 34.00 18.45 2.04 37.64 19.93 2.03 40.46 14:00 17.00 1.42 24.14 17.66 1.83 32.32 19.80 1.96 38.81 15:00 16.03 1.64 26.29 18.23 1.65 30.08 18.96 1.78 33.75 16:00 8.35 0.60 5.01 8.37 0.60 5.02 8.97 0.65 5.83 17:00 6.03 0.97 5.85 11.85 1.18 13.98 14.90 1.68 25.03 18:00 5.77 0.55 3.17 6.07 0.64 3.88 9.12 1.07 9.76 19:00 4.05 0.01 0.04 5.38 0.01 0.05 6.14 0.02 0.12
40 4.2 The Actual Experiment and Data Analysis
A 24 full factorial design with one replicate was chosen in order to adequately represent the conditions pertinent to the experiment. Although the center points assist in an independent estimate of pure error as well as a check for curvature, most of the factors used in this thesis were qualitative but not quantitative factors which served as the basis for the exclusion of the center points. As a result, this is a resolution IV design with no center points. It gives a total of 16 experimental runs. Design Expert statistical software was used for both the experimental design and data analysis. The design matrix and observed values of the responses are shown in Table 4.3.
Table 4.3: Design Matrix and Observed Values of the Responses.
41 To perform a statistical analysis, the first step is to estimate the effects of factors as well as their interactions. If necessary, these factors should be adjusted to improve the response. The next step is to use Analysis of Variance (ANOVA) to determine which factors and interactions are significant to the model. The final
step is to use graphical analysis to check the model adequacy. The concluding
observations from the ANOVA could be adopted [Montgomery, 2005].
The half normal plot of effects estimates in Figure 4.3 serves as a guide in
determining “tentatively” the significant and insignificant terms in a design
model. The effects that are insignificant are normally distributed with mean of
zero and constant variance, σ2, and tend to fall along a straight line on this plot.
Significant terms, on the other hand, have a nonzero means and thus do not lie
along the straight line. Generally, the far the plot of factor away from the straight
line, the more significant the factor is. Therefore, form Figure 4.2, it was found
that five terms are selected. They are all single factors Tracking (A), Module Type
(B), Time of the Day (C) and Weather Condition (D). The interaction of Time and
Weather Condition (CD) was also found to be significant.
42
Figure 4.3: Half normal probability plot of effects estimates for experiment.
Results obtained from the Analysis of Variance (ANOVA) in Table 4.4 reveal that
A,B, D and CD terms all have P‐values that are less than 0.05, indicating that they are significant terms in the design model which agree with results from the half normal plot of effects. Because of hierarchy corrected automatically, factor C was involved into the half normal plot of effects but shows insignificance in ANOVA table.
43 Table 4.4: Analysis of Variance Table (ANOVA) for Experiment.
In Table 4.5, the R‐Squared value of 0.9464 indicates that the factors in the design model accounts for about 94.6% of the variability in the response, which is Power
Output. The Predicted R‐Squared value appears to be in reasonable agreement
with the Adjusted R‐Squared value. The high Adequate Precision value signals
low noise in the model. Therefore, the normality, constant variance and
independence assumptions are investigated using Residual Plots.
44 Table 4.5: Summary Statistics of Analyzed Experiment.
4.2.1 General Regression Model
The general regression model of Power Output variables is shown in
Equation 4.1.
y = β 0 + β x11 + β 2 x2 + β 3 x3 + β 4 x4 + β 34 x3 x4 4.1
where,
x1, x2, x3 and x4 are coded variables representing factors A, B, C and D
respectively and y is the power output.
Substituting Effects Estimates gives a general regression model in Equation 4.2.
y = 27.68 + .3 57x1 + .4 67x2 + .1 06x3 + .4 43x4 + .1 89x3 x4 4 .2
45 4.2.2 Model Adequacy Checking
Before adopting the concluding observations from ANOVA, the adequacy of model should be checked. The model is assumed to be normally and independently distributed with mean zero and constant variance [ε~NID (0, σ2)].
Normality Assumption
The normal plot of residuals is a graphical representation of the assumption that the data are normally distributed. The Fat Pencil Test is adopted. This test is to imagine that placing a fat pencil along the straight line can cover all the data points. In Figure 4.4, it shows all the points lying close to a straight line. This implies the normality assumption holds.
Figure 4.4: Normal probability plot of residuals for experiment.
46 Constant Variance Assumption
Plot of residuals versus predicted response is used to check the validity of the constant variance assumption. Figure 4.5 shows no definite pattern and most fell within the 1.5 of the standard deviation. This implies that the constant variance assumption was not violated.
Figure 4.5: Plot of residuals versus predicted response for experiment.
Independence Assumption
Plot of Residuals Versus Run Number is sued to check the validity of the
independence assumption. Figure 4.6 shows no pattern indicating that there is no
problem of auto‐correlation. The independence assumption is satisfied.
47
Figure 4.6: Plot of residuals versus run number for experiment.
Outlier T‐Plot
The Outlier Plot is used to check whether during the data collection phase
there were unusual data recorded. Figure 4.7 shows no existence of any outlier in the data collected for the design model.
Figure 4.7: Outlier T plot for experiment.
48 Model Graphs
The model graphs interpret the significant interaction between Time of Day
and Weather Condition. Figure 4.8 reveals that, better power output at 10:00 than
14:00 in the Fair Weather Condition. On the contrary, better power output takes
place at 14:00 but not at 10:00 in the Cloudy Weather Condition.
Figure 4.8: Interaction graph of CD for experiment.
4.2.3 Model Validation
Model validation seeks to determine the best input factor settings that will achieve optimum Power Output. The predicted values are updated as the levels are changed. The 95% CI (confidence interval) is the range in which you can
49 expect the process average to fall into 95% of the time. The 95% PI (prediction interval) is the range in which you can expect any individual value to fall into
95% of the time. The prediction interval will be larger (a wider spread) than the confidence interval since you can expect more scatter in individual values than in averages. From the validation experiment shown in Table 4.6, the actual response fell within both the confidence and predicted intervals. This is an indication that the model Equation 4.2 is good for estimating the power output of either PV module.
Table 4.6: Sample of Generated Prediction Run.
50 4.3 Performance Improvement Analysis
Final experiment was setup to determine the percentage difference in power output between mono‐crystalline and poly‐crystalline modules in stationary position (30 degrees to the horizontal), on single‐axis tracker and dual‐axis
tracker. The data were shown in Table 4.6 and 4.7. From these data, EXCEL software was used to generate the Histogram of the data (Figure 4.8 and 4.9).
Table 4.7 and Figure 4.9 shows the calculations and difference of power output based on the comparisons of three systems, stationary, single‐axis tracker and
dual‐axis tracker using mono‐crystalline modules. Even with minor fluctuations
in the power output due to climatic changes, the average power output was
greater in favor of the dual‐axis tracker than sing‐axis tracker and stationary one.
Regardless the time periods of linear increasing and decreasing of power output,
the monotonic power output region, the period of time chosen was between
10:00 and 17:00. For using the mono‐crystalline modules, the average increase by
employing dual‐axis tracker was calculated to be 20.79% and 54.88% over the
single‐axis tracker and stationary one respectively. As well as using the poly‐
crystalline modules, the average increase by employing dual‐axis tracker was
calculated to be 18.27% and 52.78% over the single‐axis tracker and stationary
one (Table 4.8 and Figure 4.10).
51
Table 4.7: Power Output Values of 3 Systems using Mono-crystalline Modules and Percentage Difference Between Dual-Axis Tracker and Other 2 Systems.
Mono‐crystalline PV Modules (2008/10/11) Pw of Stationary Pw of Single‐Axis Pw of Dual‐Axis % difference % difference Time (Watts) Tracker (Watts) Tracker (Watts) between St & DA between SA & DA 8:00 0.06 0.06 0.06 3.74% 1.75% 9:00 3.87 11.24 16.48 326.02% 46.60% 10:00 20.38 30.90 38.15 87.23% 23.48% 11:00 30.53 35.09 41.43 35.73% 18.06% 12:00 30.90 35.88 42.51 37.60% 18.48% 13:00 34.00 37.64 45.44 33.64% 20.73% 14:00 21.66 27.02 28.23 30.34% 4.48% 15:00 28.94 29.39 32.82 13.41% 11.69% 16:00 17.94 30.62 34.98 95.00% 14.23% 17:00 5.22 16.51 30.01 475.05% 81.82% 18:00 3.37 4.13 11.29 234.85% 173.51% 19:00 0.06 0.06 0.13 108.93% 101.27%
Average Percent Difference Between 10:00 and 17:00 54.88% 20.79%
52
Table 4.8: Power Output Values of 3 Systems using Poly-crystalline Modules and Percentage Difference Between Dual-Axis Tracker and Other 2 Systems.
Poly‐crystalline PV Modules (2008/10/18) Pw of Stationary Pw of Single‐Axis Pw of Dual‐Axis % difference % difference Time (Watts) Tracker (Watts) Tracker (Watts) between St & DA between St & DA 8:00 0.05 0.05 0.05 3.69% 2.10% 9:00 2.71 6.62 11.65 329.13% 75.83% 10:00 17.89 21.60 26.63 48.87% 23.28% 11:00 22.01 30.01 35.99 63.52% 19.92% 12:00 26.69 34.10 37.51 40.55% 9.99% 13:00 33.22 37.04 40.42 21.68% 9.11% 14:00 18.82 20.96 24.12 28.22% 15.07% 15:00 26.29 30.08 33.75 28.38% 12.20% 16:00 12.90 22.80 27.32 111.72% 19.85% 17:00 4.37 12.90 22.03 404.28% 70.73% 18:00 2.84 3.69 9.48 233.44% 156.92% 19:00 0.04 0.05 0.12 202.22% 131.82% Average Percent Difference 52.78% 18.27% between 10:00 and 17:00
53
Performacne Assessment of Mono‐crystalline
50.00 Modules in 3 Systems
45.00
40.00
35.00 (Watts) 30.00 Stationary
25.00 Output
Single‐Axis 20.00 Tracker 15.00
Power Dual‐Axis 10.00 Tracker 5.00
0.00
0 0 0 0 0 0 0 0 8:00 9:00 :00 : : : :00 :00 : 10:00 11 12 13 14 15:00 16:00 17 18 19 Time of the Day
Figure 4.9: Diagram of power output curves for 3 systems using mono‐crystalline modules. Dual‐axis tracker’s power output performance was calculated to be 19.86% over the single‐axis tracker and 53.69% over the stationary module respectively.
54
Performance Assessment of Poly‐crystalline Modules in 3 Systems 45.00
40.00
35.00
30.00
(W atts) Stationary
25.00
20.00 Single‐Axis Output Tracker 15.00
Power Dual‐Axis 10.00 Tracker
5.00
0.00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 Time of the Day
Figure 4.10: Diagram of power output curves for 3 systems using poly-crystalline modules. Dual‐axis tracker’s power output performance was calculated to be 17.46% over the single‐axis tracker and and 53.08% over the stationary module respectively.
55 4.4 Cost Analysis of Dual‐Axis Tracker
Table 4.9 gives a detailed cost analysis of the installation of AZ‐125 Dual‐Axis
Tracker. In the cost analysis, a total of sixteen hours (four hours per day) of building the concrete foundation and assembling the components of the dual‐ axis tracker were used to calculate the labor fee. The labor fee used was the on‐
campus rate for research assistants. In performing a comparative analysis, the rack size is related to how maximum number of modules could be mounted on
trackers. The total amount of energy generated by each mono‐crystalline PV
module within a day is assumed equal to the region under the power curve in
Figure 4.9. The modules’ power degradation (0.5% per year) should be concerned
[Realini, 2001]. The first time annual maintenance will be taken place in the sixth
year after the installation of tracking system. In Table 4.10, the dual‐axis tracker
shows the cheapest unit cost of energy.
Table 4.9: Installation Cost Analysis of AZ‐125 Dual‐Axis Tracker.
Item Component Units Unit Cost ($) Total Cost ($) 1 AZ‐125 Dual‐Axis Tracker 1 3385.00 3385.00 2 Schedule 40 Steel Pipe (99”) 1 150.00 150.00 3 Concrete Foundation 1 150.00 150.00 3 12 Volt Deep Cycle Battery 2 57.44 114.88 4 #10 Stranded wire (70’) 2 29.40 58.8 5 5 Amps Fuse 1 3.87 3.87 6 30 Amps Voltage Regulator 1 140.00 140.00 7 Package of Wire Terminal 1 5.79 5.79 8 Copper Grounding Rod 1 50.50 50.50 9 Labor Hours (16 hrs @ $8/hr) 16 8.00 128 Total $ 4186.84
56 Table 4.10: Comparison of Dual‐Axis Tracker, Single‐Axis Tracker and Stationary Modules.
Performance Indicator Dual‐Axis Single‐Axis Stationary Tracker Tracker Modules Set‐up Cost ($) $4186.84 $3250.84 $639.47 Max # of Mono‐crystalline Modules 9 9 9 Mounted Life Time of Modules 20 20 20 (years; ‐10% of nominal power loss within 20 years) Cost of Mono‐crystalline Modules $7200 $7200 $7200 ($800/each) Installation Fee Cash Back ‐$2846.71 ‐$2612.71 ‐$1959.87 Rewarded (Federal Law) Total Installation Cost($) $8540.13 $7838.13 $5879.61 Technician Maintenance Fee $600 $600 $0 ($/year) Cost of Replacement of Batteries $650.99 $650.99 $650.99 (Every 3 years) Total Cost within 20 Years($) $9791.12 $9089.12 $6530.6 Power (kw.h) Generated (per day) 2.80 2.24 1.77 (power consumption: less than 10 wh/day) Total Power (kw.h) Generated 18480.24 14792.19 11688.47 within 20 Years Cost of Electricity ( $/kw.h) 0.53 0.61 0.56
57 CHAPTER 5
CONCLUDING REMARKS AND FUTURE WORK
5.1 Summary and Conclusions Analysis of data in this work showed that the factors that are significant to the power output are tracking system, type of module, and weather condition. This research also confirmed that the dual‐axis tracking system oriented PV modules
perpendicularly to the sun and had a tremendous performance improvement.
The results reveled that the employment of the Dual‐Axis Tracking System
produced 18% gain of power output, compared with a Single‐Axis Tracking
System. The gain of output power with the Dual‐Axis Tracking System was
much higher (53%) when compared with a stationary system inclined at 30˚ to
the horizontal. A benefit‐cost analysis performed on the three systems showed
that the unit cost of energy produced by the Dual‐Axis Tracker is $0.53, which
was the cheapest unit cost of energy in the three systems.
From the Figures 4.9 and 4.10, time of the day versus power output, a
phenomenon was observed which showed a decrease in power output between
14.00 and 16.00 Hrs. This phenomenon was also been found in previous
researcher [Anthony 2006]. This behavior may be due to meteorological factors,
such as temperature and cloud cover The further tests are needed to find the
exact factors that decrease the power generation.
58 5.2 Recommendations and Future Work
(i) Dual‐Axis tracker which takes advantage of two degree of freedom while
tracking can contribute more accurate data and show the different power
output since the change of the seasons. Perform long‐term experiments to
test the effect of seasons to the power output.
(ii) Investigate the effect of temperature with particular attention to the
unusual decrease in power output observed between 14.00 and 16.00 Hrs.
Pay attentions to the influence of temperature.
59 REFERENCES
Tomas Markvart, Solar Electricity, 2nd ed., 2000.
Green, M. A., Solar Cells: Operating Principles, Technology, and Systems Application, University of New South Wales. 1982.
Michael A. O. Case, Affordable Robust Methodology FOR Testing Quality And Consistency In Energy Production Capacity Of Silicon Photovoltaic Modules, 2007.
P.K. Singh*, Ravi Kumar, P.N. Vinod, B.C. Chakravarty, S.N. Singh, Effect of spatial variation of incident radiation on spectral response of a large area silicon solar cell and the cell parameters determined from it, 2003.
W.G.J.M. van Sark, C. De Mello Donega, C. Harkisoen, R. Kinderman, J.A.M. van Roosmalen, R.E.I. Schropp, E.H. Lysen, Improvement of spectral response of solar cells by deployment of spectral converters conaining semiconductor nanocrystals, 2004.
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M.Comsit, I. Visa, Design of the linkages type tracking mechanisms of the solar energy conversion systems by using Multi Body Systems Method, 2007. A.R. Gxasheka, E.E. van Dyk, , and E.L. Meyer, Evaluation of performance parameters of PV modules deployed outdoors, 2004.
Markvart, Tomas., Solar Electricity, 2000.
Martin A Green, Progress in Photovoltaics: Research and Applications, 2001.
60 A. Realini, E. Burà, N. Cereghetti, D. Chianese, S. Rezzonico, STUDY OF A 20‐ YEAR OLD PV PLANT, 2001 http://www.pvpower.com/historyofphotovolatics.html http://www.pvresources.com/en/solarcells.php http://www.play‐hookey.com/semiconductors/pn_junction.html http://org.ntnu.no/solarcells/pages/Chap.2.php
61 BIOGRAPHICAL SKETCH
The author, Kai‐Fan Wang, was born in Taitung, Taiwan in 1980. He is the second son and second child of Tsai‐Chiung Chiang and Chen‐Kun Wang. Kai‐
Fan had his primary, secondary, and tertiary technical education in Taiwan.
After graduating with a B.S. in Industrial Engineering from Tunghai University, he served in the military as the corporal for two years.
In 2006, Kai‐Fan enrolled at the Florida State University to pursue his Master of Science in Industrial Engineering. He worded with the Undergraduate
Research Center for Cutting Edge Technologies (URCCET) as a Graduate
Research Assistant. His main research interests were in Quality Control,
Renewable Energy and Nanotechnology. His master’s thesis research was
Renewable Energy. In December 2008, under the direction of his major professor
Yaw A. Owusu, Kai‐Fan was awarded the Master of Science degree in Industrial
Engineering.
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