solar windmill final

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DESIGN AND ANALYSIS OF SOLAR WINDMILL
REPORT FOR ENG 573 ENERGY SYSTEMS PROJECT
Submitted in partial fulfillment of requirements for the Master of Engineering Degree with a
Concentration in Energy Systems in the Graduate College of the University of Illinois at
Urbana-Champaign
ABSTRACT
The wind and the solar systems combined can meet the global energy demand and are often used
separately. The aim of this report is to exploit the potential of the combined system in two
configurations. The first configuration utilizes the potential of putting the photovoltaic (PV)
panels on the tower structure of the wind turbine, where these are exposed to the incoming solar
radiation to produce electricity that is combined with the wind energy to give much more
continuous supply. In the second configuration, the wind turbine blades are covered with PV
(thin film) and these are exposed to solar radiation. This unutilized energy, if harnessed can be
used for supplying excitation voltage to the generator or for battery storage in wind turbines. In
this work, the concept of hybrid PV systems is analyzed through experimental study. Moreover,
MATLAB (Matrix Laboratory) analysis demonstrated the feasibility of putting PV thin film on
blades of a wind turbine and also the support structure. Further, a relation is derived for the
power output of the solar PV mounted turbine in terms of the rotational speed under different
irradiance levels.

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Table of Contents
1. INTRODUCTION ..................................................................................................................... 5
2. LITERATURE REVIEW........................................................................................................... 6
3. SOLAR ENERGY - OVERVIEW............................................................................................... 8
3.1 Solar Panels....................................................................................................................................................................8
3.1.1 Silicon Solar Cells ...............................................................................................................................................9
3.1.1.1 Monocrystalline Silicon Solar Cells.........................................................................................9
3.1.1.2 Polycrystalline Solar Cells........................................................................................................9
3.1.2 Thin Film Solar Cells ......................................................................................................................................9
3.1.2.1 Amorphous Silicon Solar Cells ...............................................................10
3.1.2.2 Cadmium Telluride Solar Cells ................................................................10
3.1.2.3 Copper Indium Gallium Selenide Solar Cells ...........................................11
4. WIND ENERGY-OVERVIEW ............................................................................................... 11
4.1 Horizontal Axis Wind Turbine.......................................................................................................................12
4.2 Vertical Axis Wind Turbine ............................................................................................................................12
5. MATLAB AND STATISTICAL ANALYSIS - PRELIMINARIES ..........................................13
5.1 Matlab Introduction ..............................................................................................................................................13
5.2 Terminology - Statistical Analysis.................................................................................................................13
6. SYSTEM DESCRIPTION AND SELECTION ISSUES ........................................................... 16
6.1 Rationale for HybridSystem ............................................................................................................................17
6.2 Use of Thin Film .......................................................................................................................................................18
6.3 Effect of Light onThin Film................................................................................................................................18
7. EXPERIMENTAL PROCEDURE........................................................................................19
7.1 Set Up..............................................................................................................................................................................19
7.2 Testing ................................................................................................................................20
7.2.1 STC Testing......................................................................................................................................................20
7.2.2 Outdoor Testing ............................................................................................................................................20
7.3 Analysis...............................................................................................................................21
7.3.1 Matlab .........................................................................................................................21
7.3.2 PVPM....................................................................................................................................................................21
7.3.3 Experimental Detail.......................................................................................................................................21
8. RESULT ...................................................................................................................................22
8.1 PVPM Data Values ............................................................................................................22
8.2 Curve Fitting .....................................................................................................................33
9. CONCLUSION ........................................................................................................................ 37

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REFERENCES...........................................................................................................................38
APPENDIX A- Technical Data of Panel ................................................................................41
APPENDIX B- Matlab Codes...................................................................................................41

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List of Figures and Tables
Figure 1 Power Vs Time for Static condition.....................................................................34
Figure 2 Power Vs Time for Dynamic condition at 170 RPM...........................................35
Table 1 Irradiance and Power data- Static condition........................................................23
Table 2 Irradiance and Power data- Speed 1....................................................................24
Table 5 Derived Values with optimized tilt - Sunpower X21-345...................................27
Table 6 Derived Values with optimized tilt - Sharp NU-U240F2....................................28
Table 7 Derived Values with optimized tilt - Solastica Thin film....................................29
Table 8 Derived Values with Fixed Tilt – Premium.........................................................30
Table 9 Derived Values with Fixed Tilt – Standard.........................................................31
Table 10 Derived Values with Fixed Tilt - Thin film.........................................................32

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1. INTRODUCTION
Renewable energy, a term coined recently, but its significance has never been more prominent.
The negative impact of conventional energy sources on environment shifted the focus of the
modern world towards renewable energy. The world is facing an unprecedented risk of global
warming, resulting in glacial melting, rising sea level, extinction of flora and fauna species.
These issues are mainly due to the high carbon content in the atmosphere released by thousands
of power plants, releasing greenhouse gases and intoxicating the environment. The possible
solution to these problems lies in reducing the greenhouse gas emission, but due to ever
increasing population, driven by the desire to improve the standard of living the task does not
seem that simple. This situation is forcing to find new solutions to the problem. The one alternate
is in the form of solar and wind power generation system, giving clean, green and reliable
electricity for both on-grid and off-grid applications. The share of renewable energy in total
power is ever increasing; almost 13.2% in 2012 and expected to increase further (International
Energy Agency 2015).
The most promising sources of all the renewable energies are solar and wind. These technologies
with time have matured to the level making them more compatible with the conventional
sources. The solar and the wind together hold the promise for future power generation. In
general, the earth receives abundant sunshine year round. In particular, India receives more than
300 sunny days in a year (Bennett, Coleman and Company 2015). Major developing countries
around the world are viewing solar energy as a possible solution to last mile energy connectivity,
particularly for off-grid. The wind, on the other hand, is a manifestation of solar energy and
flows in a pattern around the earth and can be commercialized for MW power generation. The
ease of production of wind turbines in sizes ranging from kW to MW scale has been increasing

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their applications in both on-grid and off-grid applications. The wind and solar combined is
expected to change the global energy scenario, in the sense that it is going to contribute the
maximum to the renewable energy segment, providing not only pollution-free but safe and easily
exploitable natural energy. The idea of a solar windmill is not new and has been patented
(Kashyap 2006). In most of the studies, the effect of the wind on a solar panel is considered, in
which the mechanical impact of wind gusts on PV panel and how it affects the voltage and
current are investigated. In the past, the researchers explored the idea of a solar-wind hybrid
system through some applications. However, in this project, the experimental analysis is also
carried out under standard test conditions (STC) and outdoor testing conditions. A relation
between current and voltage regarding irradiance and rotational speed is also derived.
2. LITERATURE REVIEW
An extensive literature review was carried out to identify research gaps for solar wind hybrid
systems. A brief of each paper is furnished here:
Khare et al. (2016) presented a study of different aspects of the hybrid renewable energy system.
The authors discussed mainly optimum sizing, modeling and control issues. Further, the
application of evolutionary algorithms in hybrid renewable energy was introduced.
Sinha and Chandel (2015) studied the prospects of photovoltaic micro wind based hybrid
systems for selected locations of the Himachal Pradesh in India. The National aeronautics and
space administration (NASA) data, artificial neural network predicted and ground measured data
were used in the analysis. The study indicated that state has a better prospect of power generation
from hybrid systems with significant solar and minor wind components. The suggested
methodology can be used for the prediction of the photovoltaic and wind power generation

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potential of any region worldwide.
Mahesh and Sandhu (2015) presented a review on hybrid PV/wind energy systems with battery
storage. The discussion was focused on design, analysis and integration of such systems into the
power network.
Bouzelata et al. (2016) suggested the integration of wind energy conversion system and
photovoltaic power generator and its connection to the grid line via parallel active power filter.
The proposed wind energy conversion system is based on a doubly fed induction generator with
the directly grid-connected stator and rotor through a back-to-back AC-DC-AC pulse-width
modulation converter. Furthermore, the authors studied hybrid system response under various
wind speed and various values of the nonlinear load to prove the performance of the proposed
approach.
Sinha and Chandel (2015a) presented a review of trends in optimization techniques used for the
design and development of solar photovoltaic wind based hybrid energy systems. The pattern
showed that the new generation artificial intelligence algorithms were mostly used during last
decade as these require less commutation time and have better accuracy, convergence in
comparison to traditional methods. Further, the study suggested hybridization of two or more
algorithms, which may overcome the limitations of a single algorithm.
Gonzalez et al. 2015 identified hybrid renewable energy systems as an efficient mechanism to
generate electrical power. The work was focused on the optimal sizing of hybrid grid-connected
photovoltaic wind power systems from real hourly wind and solar irradiation data and electricity
demand from a certain location. The proposed methodology was capable of finding the sizing
that leads to a minimum life cycle cost of the system while matching the electricity supply with
the local demand.
Ahmed et al. (2016) presented a review of the different hybrid PV wind renewable energy hybrid

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systems used for electrical power generations. Various criteria for sizing the various system
components of hybrid renewable energy power plant at the most preferable logistical
environmental and economic considerations had been discussed. Also, the authors discussed
some of the optimization approaches, which were used to compare the energy production cost,
and performance of different hybrid system configurations using simulation techniques. The next
two sections provide an overview of solar and wind energy.
3. SOLAR ENERGY - OVERVIEW
The solar energy is the fastest growing energy segment of all the renewable sources due to ever
decreasing a cost of production, ease of installation and improvement in efficiency of panels,
thereby making it compatible with other conventional sources of energy. The fuel for this is the
sun or solar radiation, which is a free and unlimited source. Solar panels are used to harness the
solar energy.
3.1 Solar Panels
Solar PV panels are simple p-n junction diode capable of producing electricity. The PV panel is
exposed to the incoming packets of energy called photons, which transfer their energy to free
electrons in ‘n’ layer of the panel. Thus, the electrons are released from the valence band of ‘n’
layer and flow through the circuit generating electricity before recombining with the ‘p’ layer.
The expression for power is (International Standard 2005): P= Irradiation*area of
panel*efficiency
There are different types of cell technologies available to harness solar energy. These are
discussed in the following subsections.
3.1.1 Silicon Solar Cells
Silicon Solar Cells are the most common types of cells and constitute the major share of the PV

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market. The majority of silicon-based solar cells (about 95 percent) are crystalline silicon. These
are of two types - monocrystalline and polycrystalline.
3.1.1.1 Monocrystalline Silicon Solar Cells
Monocrystalline solar cells are identified by their color. These are made from the pure silicon.
The alignment of the molecules determines the efficiency of a panel; more pure the alignment
more efficient is the panel. Their efficiency level is about 20 percent (Alternative Energy 2015).
Monocrystalline solar cells are made out of "silicon ingots," a cylindrically shaped design that
helps in optimizing the performance. Therefore, these panels have rounded edges rather than
square, like other types of solar cells.
3.1.1.2 Polycrystalline Solar Cells
Polycrystalline solar cells are the multi-silicon cells. These were the first solar cells ever
introduced to the industry, in 1981. For polycrystalline cells, the silicon is melted and poured
into a square mold, hence their square shape. Since silicon waste is minimized during the
manufacturing process, these are more economical than monocrystalline. However,
polycrystalline is less efficient than monocrystalline.
3.1.2 Thin Film Solar Cells
Thin film solar cells are characterized by the manner in which various materials are layered on
top of one another to create a series of thin films. The thin film solar cells registered growth rates
of approximately 60 percent during 2002 to 2007 (Alternative energy 2015). By 2011, the share
of thin film solar cell industry is about 5 percent of all cells in the market (Alternative Energy
2015). While many variations of thin film products exist, these typically achieve efficiencies of
7-13 percent (Alternative Energy 2015). Due to the flexible nature of the thin film, there are

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many new application areas for this technology. Moreover, high heat and shading have less of a
negative impact on thin film technologies. For these reasons, the thin film market continues to
grow. There are some limitations of thin film technologies; such as higher cost, larger space
requirement, and shorter shelf life. Mass production of thin film solar cells is easier than
crystalline-based models. Therefore, the cost of the mass production of thin film solar cells is
lower. Further, thin film technologies require a lot of space. Due to this requirement, this
technology is more suitable for residential applications than for commercial spaces. Moreover,
thin film cells have a shorter life than their crystalline counterparts. Therefore, manufacturers
offer a shorter warranty. Thin film technology uses various photovoltaic substances, including
amorphous silicon, cadmium telluride, and copper indium and gallium selenide. These materials
are suitable for different types of solar applications.
3.1.2.1 Amorphous Silicon Solar Cells
Thin film solar cells made out of amorphous silicon are traditionally used for smaller-scale
applications, including things like pocket calculators, travel lights, and camping gear employed
in remote locations. A new process called "stacking" that involves creating multiple layers of
amorphous silicon cells have resulted in higher efficiency (up to 8 percent) (Alternative Energy
2015) for these technologies. However, it is still expensive.
3.1.2.2 Cadmium Telluride Solar Cells
Cadmium Telluride is only thin-film materials that have been cost-competitive with crystalline
silicon models. In fact, in recent years, some cadmium models have surpassed them regarding
their cost-effectiveness. The efficiency of Cadmium Telluride models is in the range of 9-11
percent (Alternative Energy 2015).
3.1.2.3 Copper Indium Gallium Selenide Solar Cells

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Copper Indium Gallium Selenide cells have efficiency levels from 10-12 percent (Alternative
Energy 2015), which is somewhat comparable to crystalline technologies. However, these cells
are still in the nascent stages of research and have not been commercially deployed on a large
scale.
4. WIND ENERGY-OVERVIEW
Wind systems harness energy from the wind by converting the kinetic energy of the wind into
rotational energy. The expression for power is (Ragheb 2015):
P=0.5*density of air*coefficient of performance*area of turbine*wind speed
There are two types of wind turbine systems namely, horizontal axis wind turbine and vertical
axis wind turbine.
4.1 Horizontal Axis Wind Turbine
Horizontal axis wind turbines are most commonly used wind turbines for commercial purpose.
These are often designed on MW scale and are increasingly being installed and connected to the
grid. In these types of turbines, the flow of wind is perpendicular to the blades. The generator
and the gearbox are located in the nacelle of the turbine. There is a need for yaw mechanism to
orient the flow of wind perpendicular to the blade. Horizontal axis wind turbine is a self-starting
machine and more efficient than vertical axis turbines.
4.2 Vertical Axis Wind Turbine
Vertical axis wind turbines usually found applications in small KW scale and not exploited on a
commercial scale. In these turbines, the flow of wind does not determine the rotation of blades
and can catch the wind in all directions. These require no yaw motion to orient itself to the wind.
The gearbox and the generator are usually located in the base and require some form of starting

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mechanism. It produces less noise and exerts lower stress on blades.
5. MATLAB AND STATISTICAL ANALYSIS - PRELIMINARIES
5.1 Matlab Introduction
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-
generation programming language. It is developed by Math Works. MATLAB allows matrix
manipulations, plotting of functions and data, implementation of algorithms, the creation of user
interfaces, and interfacing with programs written in other languages, including C, C++, Java,
FORTRAN and Python. MATLAB is also used for the feature extraction and emotion detection.
For this project, all the codes are developed using MATLAB, as it is easier to use and provides a
base for signal processing. Further, the MATLAB is used to derive both surface and contour
plots. Also, the mathematical relations are obtained for current and voltage on irradiance and
rotational speed.
5.2 Terminology - Statistical Analysis
In this sub-section terminology used in the analysis is described.
Residual plot- This is used to identify the errors between the fitted surface and data that help in
removal of outliers.
Contour plot- This is used to examine a contour map of the surface. A contour plot makes it
easier to see points that have the same height.
Table of Fits- This shows all fits in the current session. After using the graphical method to
evaluate the goodness of fit, the goodness-of- fit statistics is examined through the table of fits.
The goodness-of-fit statistics helps to determine how well the surface fits the data. The following
guidelines help to use the statistics to determine the best fit:
SSE is the sum of squares due to the error of the fit. A value closer to zero indicates a fit that is

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more useful for prediction.
R-square is the square of the correlation between the response values and the predicted response
values. A value closer to 1 indicates that the model accounts for a greater proportion of variance.
Adj R-sq is the adjusted R-square. A value closer to 1 indicates a better fit.
RMSE is the root mean squared error or standard error. A value closer to 0 indicates a fit that is
more useful for prediction.
Goodness-of- fit- After using graphical methods to evaluate the goodness of fit, the goodness-
of- fit statistics should be examined. Curve fitting toolbox supports this goodness-of- fit
statistics for parametric models and provides the following list of statistics.
The sum of squares due to error (SSE)
R-square
Adjusted R-square
Root mean squared error (RMSE)
For the current fit, these statistics are displayed in the results list box in the fit editor. For all fits
in the current curve- fitting session, the goodness-of- fit statistics can be compared with the table
of fits.
Sum of squares -This statistic measures the total deviation of the response values from the fit to
the response values. It is also called the summed square of residuals and is usually labeled as
SSE. A value closer to 0 indicates that the model has a smaller random error component and that
the fit will be more useful for prediction.
R-square- This statistic measures how successful the fit is in explaining the variation of the data.
Put another way, R-square is the square of the correlation between the response values and the
predicted response values. It is also called the square of the multiple correlation coefficients and

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the coefficient of multiple determinations. R-square is defined as the ratio of the sum of squares
of the regression (SSR) to the total sum of squares (SST). SST is also called the sum of squares
about the mean and is defined as where SST = SSR + SSE. R-square can take on any value
between 0 and 1, with a value closer to 1 indicating that the model accounts for a greater
proportion of variance. For example, an R-square value of 0.8234 means that the fit explains
82.34 percent of the total variation in the data about the average.
If the number of fitted coefficients is increased in the model, R-square will increase although the
t may not improve in a practical sense. To avoid this situation, use the degrees of freedom
adjusted R-square statistic described below.
It is also possible to get a negative R-square for equations that do not contain a constant term.
Because R-square is defined as the proportion of variance explained by the fit, if the fit is worse
than just fitting a horizontal line then R-square is negative. In this case, R-square cannot be
interpreted as the square of correlation. Such situations indicate that a constant term should be
added to the model.
Degrees of freedom adjusted R-square- This statistic uses the R-square statistic defined above
and adjusts it based on the residual degrees of freedom. The residual degrees of freedom is
defined as the number of response values n minus the number of fitted coefficients m estimated
from the response values, ‘v’ indicates the number of independent pieces of information
involving the ‘n’ data points that are required to calculate the sum of squares. If parameters are
bounded and one or more of the estimates are at their bounds, then those estimates are regarded
as fixed. The number of such parameters increases the degree of freedom.
The adjusted R-square statistic is the best indicator of the fit quality when two models that are
nested are compared, i.e., a series of models each of which adds additional coefficients to the

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previous model. The adjusted R-square statistic can take on any value less than or equal to 1,
with a value closer to 1 indicating a better t. Negative values can occur when the model contains
terms that do not help to predict the response.
Root mean squared error- This statistic is also known as the fit standard error and the standard
error of the regression. It is an estimate of the standard deviation of the random component in the
data. MSE value closer to 0 indicates a fit that is more useful for prediction.
6. SYSTEM DESCRIPTION AND SELECTION ISSUES
A solar-wind hybrid system is considered for this project. The PV systems are dependent on
solar radiation for producing the power. Their behavior is similar to a p-n junction diode. When
these are exposed to packets of solar energy called photons, an atom is freed from n layer of the
diode and flows through p side, before recombining. This flow of electrons generates electricity.
The PV panels are temperature sensitive, i.e. their output falls with rising temperature and thus
requires some form of cooling either air or water. Wind turbines, on the other hand, require wind
energy to rotate and therefore, mounting the panels on a wind turbine requires no artificial
cooling of the panel, as cooling is done by the wind flow generated by the wind turbine. The
combined system optimizes efficiency and performance of the panel.
The turbine is Suzlon S 88 2.1 MW turbine, which has a hub height of 100 meter and a blade
diameter of 88 m. The PV panels could be mounted on the tower structure from 0-44 meter to
avoid shading effects due to the blades. The project involves a use of three different types of
panels and under varying tilt angles the power output of the panels is compared to the blade and
the tower structure of the turbine. The performance of the panels varies in both conditions due to
the difference in the orientation to the sun. The panels are based on different technologies, and

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each panel gives varying output. The panels are of different efficiencies and while plotting the
power output the efficiency levels of each panel are adjusted to neglect the effect of different
efficiencies.
The first part of the experiment compares the performance of the panel with an optimized tilt for
the panel mounted on the tower structure keeping the fixed tilt for the panel mounted on the
blade structure. The power output of the panels varies with the different types of panels and the
tilt angles. The second part of the experiment compares the performance of the panel under fixed
tilt conditions for both the panels on the blade as well as on the tower structure. The power
output of the panels varies due to the different efficiency levels of the panels.
6.1 Rationale for Hybrid System
This section provides the rationale behind the selection of hybrid system and locating the PV
panels on turbine blades and the tower structures. The wind turbine carries a generator placed in
the nacelle of the structure, and it requires certain excitation voltage, which is usually provided
by a battery system or some other excitation source. Moreover, there is no power generation if
the windmill is idle due to low wind speed. Solar PV can provide energy in both these situations
if a hybrid system is in place. Usually, PV panels are installed on the support structure, because it
is static and exposed to solar radiation. However, the support structure is prone to shading from
blades. Further, it cannot be installed in a hub. It is due to the rudder mechanism, which orients
the hub in the direction of the wind that changes the orientation of PV panel away from the sun.
Also, the panels cannot be mounted on the nacelle because the nacelle is prone to shading effect
from wind turbine blades. Therefore, the solar panels are mounted on blades. The inherent
advantage of placing the PV panel on wind turbine blades is that it can be used in both horizontal
and vertical configuration. Further, there is no extra space requirement. The idea of mounting

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panels on the tower structure of the wind turbine is also explored. While doing so, the orientation
of panels in the direction of the sun with no shading effect from blades is ensured. Further, the
tower is a long vertical structure with enough area and strength to support the panels.
6.2 Use of Thin Film
Copper indium gallium selenide (CIGS) layers are thin enough to be flexible, allowing them to
be deposited on flexible substrates. However, as most of the technologies use high-temperature
deposition techniques, the cells deposited on glass give better performance. This performance is
also marginally better compared to polysilicon based panels. Recent advances in a low-
temperature deposition of CIGS cells have erased much of this performance difference. The thin
film was particularly used because their economies of scale are considerably improved. With the
advent of technology, these can be manufactured in ever decreasing thickness with the help of
chemical vapor deposition techniques on glass, plastic and in some cases even paper. It imparts
flexibility to the module and finds applications in varied fields. The different substrate material
can be deposited on top of each layer and implanted on flexible polymer based material.
6.3 Effect of Light on Thin Film
Variation in light intensity incident on solar cell changes all solar cell parameters, including the
short-circuit current, the open-circuit voltage, the fill factor, the efficiency and the impact of
series and shunt resistances. The light intensity on a solar cell is called the number of suns,
where one sun corresponds to standard illumination at AM1.5, or 1 kW/m2. For example, a
system with 10-kW/m2 incidents on the solar cell would be operating at ten suns, or at 10X. A
PV module designed to operate under 1-sun conditions is called an “at plate" module while those
using concentrated sunlight are called "concentrators."

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7. EXPERIMENTAL PROCEDURE
In this section, experimental setup, testing procedure and analysis is described.
7.1 Set Up
The apparatus used was a standing fan with three-speed options. The blades and the frame of the
panel were dismantled. The shaft of the motor (single phase synchronous motor) was attached to
the wood frame and a thermocol on which the PV was mounted. The arrangement was fastened
with the help of a motor fastener and screws. The two terminals on the panel were taken out and
connected to one end of 22 AWG copper wire. The other end of the copper wire was connected
to the copper rings and attached to the wooden frame cut according to the size of the rotor. The
continuity was made from panel to the copper rings.
The second mechanical arrangement was made by connecting two spring loaded carbon brushes
to the copper rings. The carbon brushes were continuously pressing against the copper rings. The
other end of spring loaded copper brushes were soldered to the 20 AWG copper wire and
connected to the solar analyzer. The readings of sun radiation and power values were recorded,
and results were plotted on excel sheet.
7.2 Testing
In this section testing procedure in STC and outdoor conditions are described
7.2.1 STC Testing
The standard testing condition (STC) testing has been carried out by IEC 61215, maintaining the
module at 25 0C and tracing its current-voltage characteristic at an irradiance of 1000 W/m2, by
IEC 60904-1, using natural sunlight or a class B or better simulator conforming to the
requirements of IEC 60904-9, (International standard 2005).
In the first part of the experiment, a thin film flexible solar PV was tested under STC conditions

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i.e. at 25 0C, AM 1.5 and zero wind speed at an irradiance of 1000, 600 and 200 W/m2, and three
speeds 170, 460 and 720 rpm. The indoor experimentation was carried out by using an AAA
class, single pulse ash sun simulator 700 A in a dark room with the tilt angle equal to 88. The
values were recorded while maintaining the ambient temperature and cell temperature of 25 0C.
7.2.2 Outdoors Testing
In the second part, the module was tested under actual conditions exposing it to outdoor sunlight
and wind speed and again subjecting it to three- speed levels with the tilt angle of the panel equal
to 88 0C. To analyze the effect of temperature on the performance of the panel, the ambient
temperature was recorded with the help of a digital thermometer, cell temperature with the help
of laser temperature analyzer and wind speed with the assistance of digital anemometer thereby
recording data in different time duration and at various time intervals. Series and shunt
resistance, effective irradiance and power were also found out using PVPM apparatus
7.3 Analysis
In this sub-section analysis using Matlab software is carried out.
7.3.1 Matlab
MATLAB analysis was carried out to derive the mathematical relation between the rotational
speed and irradiance with respect to the power output of the panel. Plots of Power vs Time were
plotted using the data from the PVPM and Matlab codes for static and all the three-mentioned
speed of rotation (Matlab release 7.7, 2010). For this refer Fig. 1 to 4. The energy for a day was
found out extrapolating the analysis for 12 hours.
7.3.2. PVPM
The PVPM series measure and calculate the peak power Ppk, the Rs and Rp directly and the
measurement results and I-V diagram are displayed on the PVPM units LCD. PVPM device
enables the measurement of the I-V-curve of photovoltaic modules as well as of strings or arrays.

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The proposed procedure measures and calculates the peak power Ppk, the Rs and Rp directly at
the installation site of the PV system. (Refer Table 1 to 10). The evaluated results and the
diagrams are displayed on the inner LCD-display.
7.3.3 Experimental Detail
The experiments were carried out under the following conditions.
 The panels are south facing with an azimuth angle of 180°.
 NISE (National Institute of Solar Energy) Gurgaon latitude is 28.613.
 Tilt angle for summer=(28.613*0.93)-21=5.61.
 Tilt angle for winter=(28.613*0.875)+19.2=44.236.
 The tilt of panels is adjusted twice in a year for maximized output for Table 2.
 The tilt of panels is fixed for values in Table 3.
8. RESULT
In this section results are shown in tabular as well as in graphical form.
8.1 PVPM Data Values
The data obtained from PVPM are given in Tables 1 to 10.

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Table 1: Irradiance and Power data- Static condition
Condition- Static; Speed of rotation (0 m/s)
Irradiance Irradiance Irradiance
(W/m2) (W/m2) (W/m2)
212.946091 0.44428106 220.503204 0.46817862 209.776291 0.4376186
212.946091 0.44428106 221.736023 0.46936688 160.788315 0.28724293
212.946091 0.44428106 221.794663 0.46769339 145.70343 0.25752877
204.150726 0.41741815 219.776932 0.45639351 169.768051 0.30941836
210.685043 0.43401841 220.315002 0.44953391 211.962006 0.45379116
215.387451 0.45206123 218.65947 0.44860675 216.129639 0.46136181
214.423004 0.44345931 217.740463 0.43716483 202.236694 0.40855746
211.597092 0.42472107 154.995132 0.27899339 196.92572 0.42337114
210.338882 0.4346956 136.773087 0.22510645 172.902741 0.34219724
213.833588 0.4170085 132.487961 0.22155361 199.374634 0.41524362
212.814346 0.43931967 131.972977 0.22247564 217.478867 0.44782529
212.359467 0.4381947 135.490418 0.22240127 221.029511 0.45639351
213.144165 0.44243224 138.931824 0.23863539 220.7332 0.44959913
211.701492 0.43839789 194.419861 0.39527239 219.383591 0.44267002
212.120804 0.44430141 150.453156 0.27755313 220.831299 0.4446518
213.389206 0.43889468 208.603775 0.43158867 220.729218 0.44296618
215.079056 0.43098683 221.007919 0.46589237 218.21579 0.39296712
226.437012 0.4557963 223.432007 0.46589237 220.276352 0.39841345
215.594254 0.43842363 224.362503 0.46785071 217.889465 0.38327904
174.970291 0.30967053 206.917831 0.38887595 214.578278 0.373586
153.881897 0.21361347 219.575455 0.45101207 204.511292 0.4343886
199.080048 0.39065064 221.115692 0.45553173 193.821976 0.46470922
149.708633 0.26379463 213.132156 0.43664472 209.658096 0.49754235
148.944397 0.25324514 189.816849 0.36673678 212.196823 0.43543697
146.835068 0.25672634 208.090775 0.42698365 217.265625 0.5002315
165.276901 0.29899958 209.677994 0.43019255
208.226288 0.4147909 217.618439 0.44971438
Pmax (W) Pmax (W) Pmax (W)

22
Table 2: Irradiance and Power data- Speed 1
Condition- Speed 1; Speed of rotation (170 m/s)
Irradiance Irradiance
(W/m2) (W/m2)
217.662918 0.46109203 169.643158 0.32308074
215.959717 0.44669191 200.529236 0.44275512
216.000107 0.44986665 201.495132 0.43135859
212.419663 0.47142099 205.864227 0.46618788
214.358795 0.51791777 197.310028 0.45474531
215.717087 0.51056212 205.332687 0.47638812
202.177719 0.38275504 205.025528 0.43070726
205.288559 0.38841891 204.075531 0.4537977
157.841583 0.30432417 140.255707 0.20844914
216.429367 0.47269491 134.31807 0.28659276
218.996704 0.45938976 192.26796 0.42925157
213.872086 0.56765379 204.691666 0.43014297
198.752426 0.40686839 185.837189 0.36759235
215.269653 0.54606406 200.965881 0.40091489
212.540482 0.53614519 189.065857 0.33039629
214.152954 0.52439627 145.179916 0.23513993
216.819855 0.50602566 133.760986 0.23158421
204.170212 0.54841616 127.046005 0.24281371
144.131088 0.31487253 122.519753 0.22947971
140.621964 0.27234189 120.908257 0.23825272
146.951172 0.31104731 125.199898 0.24088884
207.592804 0.45673063 125.182388 0.24293622
199.160919 0.4847177 137.321762 0.29462476
209.625702 0.51289682 146.405075 0.34780474
206.467468 0.43752541 193.189407 0.46556827
206.765747 0.47310965 199.466782 0.53788316
208.510254 0.46035492 203.199402 0.50368561
205.605057 0.39903489 206.660019 0.65448316
198.444031 0.53409429 206.345657 0.49638462
Pmax (W) Pmax (W)

23
Irradiance
(W/m2)
Pmax (W) Irradiance
(W/m2)
Pmax (W)
207.1439667 0.564666673 194.6603699 0.482964167
208.7263947 0.575227252 192.8964233 0.495105601
211.9656525 0.540746477 193.1230621 0.52673336
206.4020691 0.505786992 199.3097687 0.403007894
203.8174438 0.484227149 192.6960297 0.559636184
167.603241 0.444498264 191.8487091 0.551448385
153.5130768 0.329332704 193.7997284 0.410140268
184.2087555 0.460438786 190.0096283 0.593462191
176.1513977 0.367601021 189.5578461 0.547662766
191.0437622 0.426170663 189.2383728 0.511682722
191.9649506 0.45266077 190.1705475 0.380441764
196.7017975 0.398983003 187.546463 0.443304626
186.2847137 0.561776813 187.0035706 0.591833331
188.4769592 0.491660239 188.9303436 0.557593696
194.6258545 0.582456476 189.0683594 0.556551605
207.5748444 0.478992937 194.8125458 0.430960463
197.7201385 0.515157961 188.1367798 0.5282618
199.8065033 0.576583823 186.5062408 0.503128244
198.347229 0.560874659 174.1833954 0.34845376
198.6942139 0.615435136 191.5275116 0.57170226
192.6241608 0.589939792 199.0700226 0.422234283
182.3628082 0.535911319 187.9991608 0.490483463
140.0007019 0.311334148 189.5718231 0.502656672
144.7336578 0.307918052 199.7040405 0.4772908
140.7084808 0.298501841 191.6824951 0.47796959
152.0910492 0.343335643 194.6603699 0.482964167
194.8844147 0.578012629

24
Irradiance
(W/m2)
Pmax (W) Irradiance
(W/m2)
Pmax (W)
189.9588623 0.37416765 148.9458771 0.334967972
189.9549561 0.50507491 186.5288696 0.546685612
188.3755951 0.552021863 189.5767059 0.536184092
189.2098236 0.523146569 161.9722443 0.43746621
191.1368408 0.543819866 184.2200775 0.524642363
186.6060486 0.491592302 189.6193695 0.495252333
187.9893341 0.478864994 189.7282562 0.423178113
187.5878906 0.510798392 206.695343 0.379342017
189.422226 0.581901021 139.9539642 0.306743483
189.8624725 0.567709959 185.6934509 0.537201101
189.7299652 0.519705664 192.7059479 0.547180613
189.0619202 0.521854408 192.8383026 0.556970586
188.4194946 0.491607578 192.833374 0.525195751
189.001709 0.559832006 193.295639 0.574468344
191.3235931 0.532889599 191.2900085 0.562335005
191.2237396 0.561056876 190.6667175 0.529814701
191.4793396 0.523266788 191.0127716 0.546389746
193.3960266 0.539601941 185.9205627 0.55533785
193.805954 0.491608746 183.4442139 0.530434222
190.5878754 0.551292754 183.6121521 0.5382362
181.7229004 0.463306323 181.7776642 0.5611045
142.5132446 0.284052829 183.5692749 0.484645457
145.206192 0.33119629 180.5614624 0.501457221
180.0269623 0.453006727 184.837204 0.470004617
186.0249939 0.570845523 174.9974518 0.480914842
133.1091919 0.275613889 198.8092804 0.344269852
128.3227081 0.255302047 540.8120728 0.409101844
126.6514053 0.262209954 778.3188477 0.42430369

25
Table 5: Derived Values with optimized tilt - Sunpower X21-345
Sunpower
X21-345
Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
No of Days AC Energy
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 4.29 7.3 31 103 3.322580645 2.197473972 2.275740168 2.664459218 2.643262248
February 5.6 8.5 28 119 4.25 2.810846561 2.910958904 3.408179631 3.38106603
March 6.21 7.5 30 142 4.733333333 3.130511464 3.242009132 3.79577653 3.765579422
April 6.78 9 31 146 4.709677419 3.114866018 3.225806452 3.776806271 3.746760079
May 6.39 8 30 143 4.766666667 3.152557319 3.264840183 3.822507351 3.792097587
June 6.22 7 31 136 4.387096774 2.901519031 3.004860804 3.51812091 3.490132676
July 5.59 7 31 130 4.193548387 2.773510838 2.872293416 3.362909693 3.336156235
August 5.19 6 31 122 3.935483871 2.602833248 2.695536898 3.155961404 3.130854313
September 5.67 7 30 128 4.266666667 2.821869489 2.922374429 3.421545041 3.394325113
October 6.03 9.5 31 139 4.483870968 2.965523127 3.071144498 3.595726518 3.567120897
November 5.22 9.5 30 119 3.966666667 2.62345679 2.716894977 3.180967656 3.155661628
December 4.55 8 31 109 3.516129032 2.325482164 2.408307556 2.819670435 2.797238689
Annual 5.645 365 1536
Tilt angle
=44.236
Winter
MaximizedEnergy output for
October-March
Tilt angle
=5.61
Summer
MaximizedEnergy output for
April-September

26
Table 6: Derived Values with optimized tilt - Sharp NU-U240F2
Sharp
NU-U
240F2
Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 4.29 7.5 31 101 3.258064516 2.154804574 2.231551038 2.612722146 2.591936767
February 5.6 8.5 28 116 4.142857143 2.739984883 2.837573386 3.322259136 3.295829071
March 6.21 7.5 30 137 4.566666667 3.020282187 3.127853881 3.662122427 3.632988597
April 6.78 9 31 138 4.451612903 2.944188428 3.049049934 3.569857982 3.541458157
May 6.39 8 30 136 4.533333333 2.998236332 3.105022831 3.635391607 3.606470432
June 6.22 6.5 31 129 4.161290323 2.752176139 2.850198851 3.337041157 3.310493494
July 5.59 5.5 31 126 4.064516129 2.688172043 2.783915157 3.259435549 3.233505274
August 5.19 6 31 118 3.806451613 2.517494453 2.607158639 3.05248726 3.028203352
September 5.67 7 30 123 4.1 2.711640212 2.808219178 3.287890938 3.261734288
October 6.03 9.5 31 133 4.290322581 2.837514934 2.93857711 3.440515301 3.413144456
November 5.22 9.5 30 116 3.866666667 2.557319224 2.648401826 3.100775194 3.076107133
December 4.55 8 31 106 3.419354839 2.261478068 2.342023862 2.742064827 2.720250468
Annual 5.645 365 1479
Tilt angle
=44.236
Winter
Maximized Energy output for
October-March
Tilt angle
=5.61
Summer
April-September

27
Table 7: Derived Values with optimized tilt – Solastica Thin film
Solastica
Thin film
Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 4.29 7.5 31 87 2.806451613 1.855505199 1.922227132 2.250562641 2.232658403
February 5.6 8.5 28 104 3.714285714 2.455726092 2.544031311 2.978577157 2.954881237
March 6.21 7.5 30 127 4.233333333 2.798898072 2.899543379 3.394814221 3.367806948
April 6.78 9 31 133 4.290322581 2.836576913 2.93857711 3.440515301 3.413144456
May 6.39 8 30 130 4.333333333 2.865013774 2.96803653 3.475006683 3.447361443
June 6.22 6.5 31 122 3.935483871 2.601972807 2.695536898 3.155961404 3.130854313
July 5.59 5.5 31 115 3.709677419 2.452679286 2.540874945 2.974881651 2.951215131
August 5.19 6 31 106 3.419354839 2.260730472 2.342023862 2.742064827 2.720250468
September 5.67 7 30 112 3.733333333 2.468319559 2.557077626 2.993851911 2.970034474
October 6.03 9.5 31 124 4 2.644628099 2.739726027 3.207698476 3.182179793
November 5.22 9.5 30 104 3.466666667 2.292011019 2.374429224 2.780005346 2.757889154
December 4.55 8 31 93 3 1.983471074 2.054794521 2.405773857 2.386634845
Annual 5.645 365 1357
Tilt angle
=44.236
Winter
October-March
Tilt angle
=5.61
Summer
April-September

28
Table 8: Derived Values with Fixed Tilt - Premium
Premium Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 3.3 7.3 31 80 2.580645161 2.197473974 2.275740164 2.664459215 2.643262249
February 3.96 8.5 28 85 3.035714286 2.810846564 2.910958901 3.408179635 3.381066029
March 3.57 7.5 30 81 2.7 3.130511468 3.24200913 3.795776528 3.76557942
April 2.63 9 31 54 1.741935484 3.114866016 3.225806451 3.776806269 3.746760075
May 1.79 8 30 37 1.233333333 3.15255732 3.264840184 3.822507352 3.792097584
June 1.46 7 31 30 0.967741935 2.901519033 3.004860803 3.518120907 3.490132674
July 1.44 7 31 31 1 2.773510838 2.872293417 3.36290969 3.336156236
August 1.86 6 31 41 1.322580645 2.602833246 2.6955369 3.155961402 3.130854312
September 3 7 30 66 2.2 2.821869491 2.922374427 3.421545043 3.394325116
October 4.01 9.5 31 93 3 2.965523126 3.071144496 3.595726516 3.567120895
November 3.97 9.5 30 92 3.066666667 2.623456787 2.716894978 3.18096766 3.155661626
December 3.66 8 31 88 2.838709677 2.325482168 2.40830756 2.819670432 2.797238688
Annual 2.89 365 778
Tilt
angle=88
degree

29
Table 9: Derived Values with Fixed Tilt - Standard
Standard Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 3.3 7.5 31 79 2.548387097 2.154804578 2.231551035 2.612722148 2.591936768
February 3.96 8.5 28 83 2.964285714 2.739984886 2.837573386 3.322259136 3.295829075
March 3.57 7.5 30 78 2.6 3.02028219 3.12785388 3.662122425 3.6329886
April 2.63 9 31 52 1.677419355 2.944188432 3.049049934 3.569857983 3.541458159
May 1.79 8 30 36 1.2 2.998236328 3.105022832 3.635391608 3.606470432
June 1.46 6.5 31 29 0.935483871 2.752176141 2.850198852 3.337041156 3.310493492
July 1.44 5.5 31 30 0.967741935 2.688172042 2.783915156 3.259435548 3.233505275
August 1.86 6 31 40 1.290322581 2.517494454 2.60715864 3.052487262 3.028203354
September 3 7 30 64 2.133333333 2.711640211 2.808219176 3.287890935 3.261734287
October 4.01 9.5 31 90 2.903225806 2.837514939 2.938577107 3.440515298 3.413144458
November 3.97 9.5 30 89 2.966666667 2.557319222 2.64840183 3.100775196 3.076107135
December 3.66 8 31 86 2.774193548 2.261478064 2.342023864 2.742064824 2.720250472
Annual 2.89 365 756
Tilt
angle=88
degree

30
Table 10: Derived Values with Fixed Tilt - Thin film
Thin film Software
Value
Rotation
Speed (0)
Rotation
Speed (170)
Rotation
Speed (460)
Rotation
Speed (720)
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Tilt =88
degree
Month Intensity
(Kwh/m2/day)
No of
hours/day
(Kwh)
Energy in
a day
Energy in
a Day
Energy in
a Day
Energy in
a Day
Energy
in a Day
January 3.3 7.5 31 72 2.322580645 1.855505198 1.922227133 2.250562643 2.232658403
February 3.96 8.5 28 77 2.75 2.455726092 2.544031309 2.978577157 2.95488124
March 3.57 7.5 30 73 2.433333333 2.798898075 2.89954338 3.39481422 3.367806945
April 2.63 9 31 45 1.451612903 2.836576917 2.938577112 3.440515302 3.413144457
May 1.79 8 30 28 0.933333333 2.865013776 2.968036528 3.47500668 3.44736144
June 1.46 6.5 31 21 0.677419355 2.601972809 2.695536896 3.155961406 3.130854311
July 1.44 5.5 31 21 0.677419355 2.452679284 2.540874947 2.974881652 2.95121513
August 1.86 6 31 31 1 2.260730472 2.34202386 2.742064824 2.720250468
September 3 7 30 57 1.9 2.46831956 2.557077628 2.99385191 2.970034473
October 4.01 9.5 31 85 2.741935484 2.644628098 2.739726024 3.207698475 3.182179793
November 3.97 9.5 30 84 2.8 2.292011021 2.374429221 2.780005349 2.75788915
December 3.66 8 31 80 2.580645161 1.983471072 2.05479452 2.405773856 2.386634848
Annual 2.89 365 674
Tilt
angle=88
degree

31
8.2 Curve Fitting
The power vs time graphs were plotted for static as well dynamic conditions. These are shown in Fig. 1 to
4.
Static condition
Figure 1: Power Vs Time for Static condition
Equation of fit
y = p1*z^6 + p2*z^5 + p3*z^4 + p4*z^3 + p5*z^2 + p6*z + p7
where, z is centered and scaled:
Coefficients:
p1 = 0.0031706; p2 = 0.022969; p3 = -0.017232; p4 = -0.084558; p5 = 0.05654;
p6 = 0.068874; p7 = 0.36266

32
Speed=170 RPM
Figure 2: Power Vs Time for Dynamic condition at 170 RPM
Equation of Fit
y = p1*z^6 + p2*z^5 + p3*z^4 + p4*z^3 + p5*z^2 + p6*z + p7
Coefficients:
p1 = -0.0021534; p2 = 0.036399; p3 = 0.071879; p4 = -0.062598; p5 = -0.15372;
p6 = -0.057792; p7 = 0.44871

33
Speed=460 RPM
Equation of Fit
y = p1*z^6 + p2*z^5 +p3*z^4 + p4*z^3 +p5*z^2 + p6*z + p7
Coefficients:
p1 = 0.053597; p2 = -0.028854; p3 = -0.20407; p4 = 0.078584; p5 = 0.18717;
p6 = -0.030514; p7 = 0.46066

34
Speed=720 RPM
Equation of Fit
y = p1*z^6 + p2*z^5 +p3*z^4 + p4*z^3 +p5*z^2 + p6*z +p7
Coefficients:
p1 = 0.032892; p2 = 0.0090383; p3 = -0.20778; p4 = -0.044592; p5 = 0.33596;
p6 = 0.037804; np7 = 0.39242

35
9. CONCLUSION
In this work, it demonstrated that the PV panels are capable of producing power even under
dynamic conditions, i.e. when the panels are subjected to rotational speeds and exposed to
radiations. As the relationship between irradiance and power and power and rotation are known,
a relation between irradiance and rotation is derived.
It is also observed that there is a fall in power when rotational speed increases keeping the
irradiance at the same level. However, the drop in power is more for higher irradiance values
than for lower irradiance. It is found that the module efficiency is higher in STC conditions.
In MATLAB analysis, through surface and contour plotting it was found that the error margin
was substantially reduced when the exponent of rotational speed term is increased. Therefore, it
is concluded that the panels when producing electricity under dynamic conditions are more
influenced by the rotational speed of the turbine rather than by the irradiance.
Further, it is found that the error margin was least for the irradiance value of 3 and a rotational
speed of 3. The proximity of error was reduced for expressing equations in the higher
polynomial.
For the same value of irradiance, the error was reduced to an increase in rotational speed and for
the same rotational speed, the error was reduced with increasing irradiance value. However, the
effect was not as prominent as in the previous case.
The power output was more for all the three panels in optimized tilt configuration, i.e., the panel
on the tower structure is producing more power than the panel on the blade because the tilt angle
is optimised twice for the panel on the tower whereas it is fixed for the one on the blade. The
power output was, however, more of the panels when the tilt angle is same for the panels on the

36
blade and tower structure. The panels except some cases under the static or dynamic condition
are producing more power than the one on the tower structure which is fixed.
REFERENCES
Ahmed, S. A. B., A. H. Kazem, A. H. Al-Badi and M.F. Khan. 2016. “A review of optimum
sizing of hybrid PV– Wind renewable energy systems in Oman.” Renewable and Sustainable
Energy Reviews 53:185–93. doi: 10.1016/j.rser. 2015.08.039.
Alternative Energy. 2015. “Solar Energy”. Accessed on 25 June.
http://www.altenergy.org/renewables/solar.html
Bennett, Coleman and Company. 2015. “Mumbai Mirror.” Accessed on 10 June.
http://www.mumbaimirror.com/columns/columnists/ajit-ranade/300-days-of-sunshine/ articles
how/30013611.cms.
Bouzelata, Y., N. Altin, R. Chenni and E. Kurt. 2016. “Exploration of optimal design and
performance of a hybrid wind-solar energy system.” International Journal of Hydrogen Energy
XX: 1-15. doi: 10.1016/j.ijhydene.2015.12.165.
Gonzalez, A., J. R. Riba, A.Rius, R. Puig. 2015. “Optimal sizing of a hybrid grid-connected
photovoltaic and wind power system.” Applied Energy 154:752–62. doi: 10.1016/ j.apenergy.
2015.04.105.
International Energy Agency. 2015. “Renewable Energy.”Accessed on 2 June.
http://www.iea.org/aboutus/ faqs/ renewableenergy/.
International standard. 2005. IEC 61215, Qualification testing of module
Kashyap, R. 2006. “Patent –US 7045702B2 Solar paneled wind mill.” Accessed on 20 July,
2015. http://www. google.co.in/ patents/ US7045702.

37
Khare, V., S. Nema and P. Baredar. 2016. “Solar– wind hybrid renewable energy system: A
review.” Renewable and Sustainable Energy Reviews 58: 23–33. doi: 10.1016/j.rser.2015.12.223
Mahesh, A. and K.S. Sandhu. 2015. “Hybrid wind/photovoltaic energy system developments:
Critical review and findings.” Renewable and Sustainable Energy Reviews 52:1135–47. doi:
10.1016/j.rser.2015.08.008.
MATLAB release 7.7. 2010. The Math Works Inc., Natick, Massachusetts, USA.
Bennett, Coleman and Company. 2015. “Mumbai Mirror.” Accesses on 10 June.
http://www.mumbaimirror.com/ columns/ columnists/ajit-ranade/300-days-of-sunshine/
articleshow/30013611.cms
Ragheb, M. 2015. “Energy and power content of the wind”. Accessed on 2 June.
http://mragheb.com/ NPRE% 20475%20Wind%20Power%20Systems/Energy% 20 and %20
Power%20Content%20of%20the%20Wind.pdf.
Sinha, S. and S. S. Chandel. 2015. “Prospects of solar photovoltaic–micro-wind based hybrid
power systems in western Himalayan state of Himachal Pradesh in India.” Energy Conversion
and Management 105:1340–51. doi:10.1016/j.enconman.2015.08.078
Sinha, S. and S. S. Chandel. 2015a. “Review of recent trends in optimization techniques for
solar photovoltaic–wind based hybrid energy systems.” Renewable and Sustainable Energy
Reviews 50:755–69. doi:10.1016/j.rser.2015.05.040.

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APPENDIX
Appendix A - Technical Data of Panel
Cell Area:11.44 cm2
Module Area: 0.066600 m2
Cells in Parallel: 2
Cells in Series: 16
Cell Efficiency: 9.20% (STC measured)
Module Efficiency: 5.06 % (STC measured)
Shunt Resistance:- 855 ohm (STC measured)
Series Resistance:3.31 ohm (STC measured)
Appendix B - MATLAB CODE
a. For Surface plot
clc% clear command window.
clearall% Clear workspace window.
load('SurfP.mat'); % load the SurfP.mat file into command window.
x1=SurfP(:,1); % copy the 1st column contents to the SurfP.mat = Irradiance.
y1=SurfP(:,2);% copy the 2nd Column Contents to the SurfP.mat = Rotation Speed.
z1=SurfP (:,7);% copy the 7th Column contents to the SurfP.mat = Power.
sftool(x1,y1,z1); % surfaceplot(Irradiance, Rotation Speed, Power).
b. For Linear plot
% data is extracted from Combined Excel File
Irr=data(:,1);% load Irradiance data
Rot=data(:,3); % load Roataional Speed of module
Power=data(:,8);% Load Power
Tdif=data(:,11); % Load temperature difference b/n amb& module
cftool(Rot,Power);% Curve Fitting Tool
% data is extracted from Combined Excel File
Irr=data(:,1);% load Irradiance data
Rot=data(:,3); % load Roataional Speed of module
Power=data(:,8);% Load Power
Tdif=data(:,11); % Load temperature difference b/n amb & module
cftool(Rot,Power);%Curve Fitting Tool

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