solar windmill final

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 83.1 Solar Panels 8

3.1.1 Silicon Solar Cells 93.1.1.1 Monocrystalline Silicon Solar Cells 93.1.1.2 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 114.1 Horizontal Axis Wind Turbine 124.2 Vertical Axis Wind Turbine 12

5. MATLAB AND STATISTICAL ANALYSIS - PRELIMINARIES 135.1 Matlab Introduction 135.2 Terminology - Statistical Analysis 13

6. SYSTEM DESCRIPTION AND SELECTION ISSUES 166.1 Rationale for Hybrid System 176.2 Use of Thin Film 186.3 Effect of Light on Thin Film 18

7. EXPERIMENTAL PROCEDURE 197.1 Set Up 19

7.2 Testing ................................................................................................................................207.2.1 STC Testing 207.2.2 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 8

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:

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SSE is the sum of squares due to the error of the fit. A value closer to zero indicates a fit that is

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

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predicted response values. It is also called the square of the multiple correlation coefficients and

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

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nested are compared, i.e., a series of models each of which adds additional coefficients to the

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 15

the difference in the orientation to the sun. The panels are based on different technologies, and

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

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and vertical configuration. Further, there is no extra space requirement. The idea of mounting

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).

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In the first part of the experiment, a thin film flexible solar PV was tested under STC conditions

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 19

enables the measurement of the I-V-curve of photovoltaic modules as well as of strings or arrays.

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)

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Table 2: Irradiance and Power data- Speed 1

Condition- Speed 1; Speed of rotation (170 m/s)

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23



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Table 5: Derived Values with optimized tilt - Sunpower X21-345

Sunpower X21-345

SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (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

Energyin a Day

January 4.29 7.3 31 103 3.32258064 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 4.73333333 3.130511464 3.242009132 3.79577653 3.765579422

April 6.78 9 31 146 4.70967741 3.114866018 3.225806452 3.776806271 3.746760079

May 6.39 8 30 143 4.76666666 3.152557319 3.264840183 3.822507351 3.792097587

June 6.22 7 31 136 4.38709677 2.901519031 3.004860804 3.51812091 3.490132676

July 5.59 7 31 130 4.19354838 2.773510838 2.872293416 3.362909693 3.336156235

August 5.19 6 31 122 3.93548387 2.602833248 2.695536898 3.155961404 3.130854313

September 5.67 7 30 128 4.26666666 2.821869489 2.922374429 3.421545041 3.394325113

October 6.03 9.5 31 139 4.48387096 2.965523127 3.071144498 3.595726518 3.567120897

November 5.22 9.5 30 119 3.96666666 2.62345679 2.716894977 3.180967656 3.155661628

December 4.55 8 31 109 3.51612903 2.325482164 2.408307556 2.819670435 2.797238689

Annual 5.645 365 1536

Tilt angle =44.236

Winter

Maximized Energy output forOctober-March

Tilt angle=5.61

Summer

25

Maximized Energy output for April-September

Table 6: Derived Values with optimized tilt - Sharp NU-U240F2

SharpNU-U240F2

SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (720)

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Month Intensity

(Kwh/m2/day)

No of hours/day


Energy in a day

Energy in a Day

Energy in a Day

Energy in a Day

Energyin a Day

January 4.29 7.5 31 101 3.25806451 2.154804574 2.231551038 2.612722146 2.591936767

February 5.6 8.5 28 116 4.14285714 2.739984883 2.837573386 3.322259136 3.295829071

March 6.21 7.5 30 137 4.56666666 3.020282187 3.127853881 3.662122427 3.632988597

April 6.78 9 31 138 4.45161290 2.944188428 3.049049934 3.569857982 3.541458157

May 6.39 8 30 136 4.53333333 2.998236332 3.105022831 3.635391607 3.606470432

June 6.22 6.5 31 129 4.16129032 2.752176139 2.850198851 3.337041157 3.310493494

July 5.59 5.5 31 126 4.06451612 2.688172043 2.783915157 3.259435549 3.233505274

August 5.19 6 31 118 3.80645161 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 4.29032258 2.837514934 2.93857711 3.440515301 3.413144456

November 5.22 9.5 30 116 3.86666666 2.557319224 2.648401826 3.100775194 3.076107133

December 4.55 8 31 106 3.41935483 2.261478068 2.342023862 2.742064827 2.720250468

Annual 5.645 365 1479

26

Tilt angle=44.236

Winter

Maximized Energy output for October-March

Tilt angle=5.61

Summer

Maximized Energy output forApril-September

Table 7: Derived Values with optimized tilt – Solastica Thin film

SolasticaThin film

SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (720)

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Month Intensity

(Kwh/m2/day)

No of hours/day


Energy in a day

Energy in a Day

Energy in a Day

Energy in a Day

Energyin a Day

January 4.29 7.5 31 87 2.80645161 1.855505199 1.922227132 2.250562641 2.232658403

February 5.6 8.5 28 104 3.71428571 2.455726092 2.544031311 2.978577157 2.954881237

March 6.21 7.5 30 127 4.23333333 2.798898072 2.899543379 3.394814221 3.367806948

April 6.78 9 31 133 4.29032258 2.836576913 2.93857711 3.440515301 3.413144456

May 6.39 8 30 130 4.33333333 2.865013774 2.96803653 3.475006683 3.447361443

June 6.22 6.5 31 122 3.93548387 2.601972807 2.695536898 3.155961404 3.130854313

July 5.59 5.5 31 115 3.70967741 2.452679286 2.540874945 2.974881651 2.951215131

August 5.19 6 31 106 3.41935483 2.260730472 2.342023862 2.742064827 2.720250468

September 5.67 7 30 112 3.73333333 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 3.46666666 2.292011019 2.374429224 2.780005346 2.757889154

December 4.55 8 31 93 3 1.983471074 2.054794521 2.405773857 2.386634845

27

Annual 5.645 365 1357

Tilt angle=44.236

Winter

Maximized Energy output forOctober-March

Tilt angle=5.61

Summer

Maximized Energy output forApril-September

Table 8: Derived Values with Fixed Tilt - Premium

Premium SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (720)

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Month Intensity

(Kwh/m2/day)

No of hours/day


Energy in a day

Energy in a Day

Energy in a Day

Energy in a Day

Energyin 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

28

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

Table 9: Derived Values with Fixed Tilt - Standard

Standard SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (720)

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Month Intensity

(Kwh/m2/day)

No of hours/day


Energy in a day

Energy in a Day

Energy in a Day

Energy in a Day

Energyin 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

29

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

Table 10: Derived Values with Fixed Tilt - Thin film

Thin film SoftwareValue

RotationSpeed (0)

RotationSpeed (170)

RotationSpeed (460)

RotationSpeed (720)

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Tilt =88 degree

Month Intensity

(Kwh/m2/day)

No of hours/day


Energy in a day

Energy in a Day

Energy in a Day

Energy in a Day

Energyin 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

30

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

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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

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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

9. CONCLUSION

35

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

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.

36

REFERENCES

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2015.04.105.

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Kashyap, R. 2006. “Patent –US 7045702B2 Solar paneled wind mill.” Accessed on 20 July,

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APPENDIX

Appendix A - Technical Data of Panel

38


http://dx.doi.org/10.1016/j.enconman.2015.08.078



Cell Area: 11.44 cm2Module Area: 0.066600 m2Cells in Parallel: 2Cells in Series: 16Cell 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 FileIrr=data(:,1);% load Irradiance data Rot=data(:,3); % load Roataional Speed of modulePower=data(:,8);% Load PowerTdif=data(:,11); % Load temperature difference b/n amb& module cftool(Rot,Power);% Curve Fitting Tool

% data is extracted from Combined Excel FileIrr=data(:,1);% load Irradiance data Rot=data(:,3); % load Roataional Speed of modulePower=data(:,8);% Load PowerTdif=data(:,11); % Load temperature difference b/n amb & module cftool(Rot,Power);%Curve Fitting Tool

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