1 School of Engineering and Information Technology ENG470 Engineering Honours Thesis A Revision of IEC 60891 2 nd Edition 2009-12 Data Correction Procedures 1 and 2: PV Module Performance at Murdoch University Tim Blagojevic 2016 “A thesis submitted to the School of Engineering and Energy, Murdoch University in patrial fulfilment of the requirements for the degree of Bachelor of Engineering” Unit Coordinator: Dr Gareth Lee Supervisor: Dr David Parlevliet
Transcript
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School of Engineering and Information Technology
ENG470 Engineering Honours Thesis
A Revision of IEC 60891 2nd Edition
2009-12
Data Correction Procedures 1 and 2:
PV Module Performance at Murdoch
University
Tim Blagojevic 2016
“A thesis submitted to the School of Engineering and Energy, Murdoch University in patrial fulfilment
of the requirements for the degree of Bachelor of Engineering”
Unit Coordinator: Dr Gareth Lee
Supervisor: Dr David Parlevliet
ii
Author’s Declaration
Except where I have indicated, the work I am submitting in this report is my own and has not been
submitted for assessment in another course.
Signed: Date:
iii
ENG460 Engineering Thesis
Academic Supervisor endorsement pro forma
I am satisfied with the progress of this thesis project and that the attached report is an accurate
reflection of the work undertaken.
Signed:
Date:
iv
Abstract
The focus of this project is to review and effectively assess the first two photovoltaic module
electrical performance data correction procedures contained in the international engineering
standard IEC 60891: “Photovoltaic Devices- Procedures for temperature and irradiance corrections
to measured I-V characteristics.” The formulated workings of the project were used to assess the
effectiveness of the correction methods in translating electrical performance data for determining
the degradation or performance of photovoltaic modules.
A preliminary literature review of concepts involved in the implementation of project procedures
was conducted, so that appropriate experimental testing conditions could be formulated. This
project covers information regarding factors that may affect photovoltaic module performance
variation and degradation.
Over a period of months in autumn/winter, outdoor field electrical performance data for different
PV module technologies at the Murdoch University location was recorded and processed. The data
collected was obtained under varying atmospheric conditions, with the tilts and orientations of the
modules altered to change the total amount and nature of solar irradiation reaching the modules.
The algebraic equations of the first and second standard correction procedures utilised parameters
with values that could be measured directly from the outdoor testing of modules, or deduced from
electrical performance data obtained from testing modules indoors at known values of irradiance,
temperature and atmospheric spectra.
Indoor performance data simulated with solar irradiance levels and cell temperatures recognised as
those matching international standard test conditions, was obtained for use in effectively
implementing the correction procedures. The data was also independently analysed and compared.
Outdoor module test performance data was corrected with both correction procedures and collated
for analysis. The results highlighted the effects of and correlations between factors that influence
module I-V curve dynamics.
When implemented for data translation, “correction procedure one” was found to produce a range
of maximum power mismatch accuracy levels from 0.09 to 22.97% with an average accuracy
mismatch level of 9.54%. “Correction procedure two” was found to produce a range of accuracy
maximum power mismatch levels of 0.19 to 28.64%, with an average accuracy mismatch level of
8.58%
An assessment of the correction procedures showed that they could be effectively used to gauge
module degradation or for comparison of module performance against factory specifications. Both
methods showed similar variations in accuracy, with “correction procedure 2” being better suited to
situations where the irradiance level difference between two data sets is more than 20%.
“Correction procedure 2” has more working parameters and takes more time to establish for correct
implementation.
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Acknowledgments
I would like to wholeheartedly thank my supervisor Dr David Parlevliet for his consistent and
effective guidance, direction and patience while assisting me in the undertaking of this thesis
project.
I would like to express my pleasure in dealing with the facilities and staff at Murdoch University.
Without the user-friendly facilities and great efforts of the academic staff and technical assistants,
this project would not have been possible.
Finally, I would like to thank my family and friends for their support and help that they have
contributed over the duration of this project and my university studies.
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Glossary
Abbreviation Definition
Voc Open Circuit Voltage
Isc Short Circuit Current
FF Fill Factor
AM Air Mass Coefficient
Poly-Si Polycrystalline Silicon
Mono-si Monocrystalline Silicon
Amorphous-si Amorphous Silicon
STC Standard Test Conditions
PV Photovoltaic
I-V Current-Voltage
MPP Maximum Power Point
Pmax Maximum Power
Voltage at the maximum power point
Current at the maximum power point
STC Standard Test Conditions
W Watt as a unit of electrical power measurement
Δ The symbol delta, representing a change in a parameter
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Contents
Author’s Declaration ............................................................................................................................... ii
ENG460 Engineering Thesis ................................................................................................................... iii
Abstract .................................................................................................................................................. iv
Acknowledgments ................................................................................................................................... v
Glossary .................................................................................................................................................. vi
Contents ................................................................................................................................................ vii
List of Figures .......................................................................................................................................... x
List of Tables ......................................................................................................................................... xii
Table 16: Poly-Si: Difference(%)in Pmax(W) After Correction to 968 Using Method 2 ... 44
Table 17: Mono-Si: Difference(%) in Pmax(W) After Correction to STC Using Method 1 .................... 45
Table 18: Poly-Si: Difference(%) in Pmax(W) After Correction to STC Using Method 2 ....................... 46
Table 19: Amorphous-Si: Difference(%) in Pmax(W) After Correction to STC Using Method 2 ........... 46
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Chapter 1: Introduction
1.1 Background
In recent times, there has been an increased focus on progressing energy generation away from systems that cause damage to the environment and towards more cost effective and sustainable sources, that cause less damage to the environment. Energy generation systems that use renewable energy generation technology represent a way to generate energy with low emissions. The increased generation of energy from renewable sources means that there is less reliance on fossil fuels. Current substitutes for fossil fuel generation include systems that utilise wind, biomass, tidal/ocean, geothermal and solar energy mechanisms and unlike fossil fuels, the energy sources are essentially unlimited [1]. “Photovoltaic”, “PV” or “solar” panels use semi-conductors to generate electrical energy from photons, sourced from the sun’s radiation [2]. This energy can be utilised as electricity that can be injected into established power electricity grids or used independently as a power source at the site of generation. PV panels are used increasingly often as a source of electricity for family homes [3]. Recent data analysis illustrates that renewable energy technology provided 13.47 % of the total electricity generated in Australia in the year 2014, with 15.47% of this electricity coming from solar generation [3]. Solar energy generation continues to be an important facet of total electricity production in Australia. With continuous improvements in energy storage and panel technologies [3], there is further room for growth and expansion in the solar sector.
Figure 1: Cumulative Household and Commercial Solar PV Installation for 2007 to 2014 in Australia [3]
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Cumulative Household and Commercial Solar PV Installation Capacity for 2007 to 2014 in Australia (MW)
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Determining the output power production of a PV panel over a period of time is a very important
characteristic in its working dynamics. The output power slowly reduces in magnitude from its
original amount due to a number of environmental factors, such as the sun, high temperature and
moisture. This decline in power is known as one of the factors in the “degradation rate” of a panel.
PV degradation rate is of a particular importance to any stakeholders which make use of the
technology, including utility companies, investors, and researchers. Any changes in power output to
grid connected PV systems can cause possible interruptions to power quality and can cause other
problems in an electricity grid [4].
It is also important to understand PV degradation from a financial viewpoint, because higher
degradation rates of energy system modules means earlier replacement costs meaning lower future
cash flows, due to the loss of output power. Other financial concerns involve end user situations
where panel array space is an energy system design constraint. Having higher panel efficiency from
the panels used in a system array on a roof top with limited space for example, means more power
per given area. This can mean that the end user has more electricity from their limited roof space
[5]. Comparing the degradation rates and performances of PV panels can lead to better financial and
technical choices for energy systems [6,7].
Measuring the electrical performance of a particular photovoltaic or “PV” panel enables an analysis
of any performance degradation to be conducted. The “current-voltage characteristic” or “I-V curve”
is a graphical representation of the relationship between the current and the voltage of a panel
under operation [8].
For a correct assessment of the operating characteristics of a specific panel, the performance data
must be standardised to allow for a direct comparison with data from another panel. The data must
be translated or “mapped” to produce results that would have been obtained had the panel been
operating under agreed standardised testing conditions [9] when tested. International and other
specific engineering standards provide instruction and guidelines for the use of algebraic
translational methods. The mapping methods contain parameters that account for some
environmental and performance factors that can affect module output power and therefore the
measured data and I-V curves. Some factors that affect the output of a panel are not accounted for
by data translation methods alone.
PV Modules are commonly made of different derivatives of silicon and are also made of other
materials. Radiation from the sun comes in varying wavelengths of light and therefore has a certain
spectral distribution. Particular module material types respond to different wavelengths in the
sunlight spectrum at varying rates when compared to other module types. Power output levels can
therefore vary with spectral differences. The varying module spectral response must be taken into
account when mapping panel data, as the outdoor sunlight spectral characteristics may not match
the spectrum of the light under standard test conditions [10].
This project reviews and illustrates algebraic translational data mapping methods, with a specific
focus on methods that are outlined in the standard “IEC 60891 edition 2.” The mapping methods
were applied to electrical performance data collected in the field at Murdoch University, for three
different solar module technologies. Variations in factors that affect electrical performance were
exploited to allow for a wider data set for analysis. Alternative performance data was obtained from
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testing involving a sun simulator, or an indoor energy source that mimics conditions experienced
when panels receive sunlight under ideal standard test conditions [10]. The mapped data and ideal
performance data collected indoors was then compared to examine the effectiveness of the
mapping methods implemented. Any discrepancies in the data were investigated.
1.2 Aims and Objectives
This thesis aims to examine methods that assess the degradation and performance of different PV
modules individually and comparatively. Performance data at the Murdoch University location was
to be comparatively assessed using the first two data translation methods outlined in the
international engineering standard: IEC 60891 2nd Edition (2009-2012). For the proper assessment of
the data using the translation methods contained in this standard, a number of preliminary steps
were necessary for completion in the achievement of these objectives.
An initial literary review was necessary for development as to understand all physical
concepts and phenomena involved in the processes of PV modules producing electrical
energy and the recording and mapping of performance data.
One necessary objective was to effectively test and record valid electrical performance of
different panel technologies outdoors. The practical methodology of this project was
implemented and carried out to achieve this. Performance data was selectively set to be
affected by changing irradiance and temperature. The orientation of the incident solar
radiation to testing PV modules and the tilt of the modules were to be used as factors to
selectively vary irradiance levels. The specific spectral wavelength distribution of the sunlight
was to be noted with each variation in data recording conditions.
There was an aim to produce valid electrical performance data under ideal standard test
conditions, which would be obtained indoors using solar simulation equipment. Further
practical methodology in this project was formulated to achieve this.
Following on from the outdoor and indoor performance data collection, all valid data was
mapped using the three different methods outlined in the standard. Once the data mapping
was complete, the different data translation methods could be assessed. The validity,
effectiveness and selectivity of the different methods could be determined.
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Chapter 2: Literary Review
A broad range of literary research was necessary to effectively achieve the outcome aims and
objectives set out in this project. An understanding and establishment of concepts involved within
the physical phenomena, instruments, international engineering standards and practical
methodology involved in this project was necessary to examine the validity of the data translation
methods implemented.
2.1 Solar Radiation and the Solar Spectrum
This research project involved the processes of capturing solar radiation and the analysis of electrical
power obtained from this captured energy. It is necessary to establish how these processes occur
and note factors that may affect the nature of the solar radiation.
Energy travels from the sun in an electromagnetic form, which reaches the earth’s atmosphere as
sunlight. This light that reaches earth contains infrared, ultraviolet and visible light as part of its
spectrum. The particles representative of the light are termed “photons”. The photonic energy can
be represented by a function of its wavelength as represented by the following equation:
Where represents the photon energy, h is Planck’s constant and c is the speed of light [11].
The position of the sun will change compared to the surface of the earth and consequently, the
atmospheric distance that the photons travel through will change also. Air Mass (AM) is descriptive
of the measured distance or thickness of the atmosphere that solar flux must travel through, when
the sun is above the horizon, to reach the earth’s surface. The air mass will represent the shortest
path length that is possible for the sunlight to travel through, when finally reaching ground level
[12].
The actual spectrum of sun that reaches the earth’s surface is called the global radiation. Global
radiation has multiple components. Radiation that comes directly from the sun unmodified by
atmospheric processes is termed direct radiation. Solar radiation that reaches the earth’s surface
after atmospheric interaction is described as diffuse radiation [13]. The global irradiation level is
equal to the sum of the diffuse and direct irradiation components multiplied by the cosine of the
solar zenith angle as seen by the formula:
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Example solar zenith angles for AM 2.0 and AM 1.5 are illustrated in figure 2 below:
Figure 2: Solar Zenith Angle, AM1.5 and AM2.0
Upon entering the earth’s atmosphere, the nature of sunlight will change. The main influences of the
change to the light coming into the atmosphere are due to particular chemical components like
ozone, water vapour, oxygen and carbon dioxide. Atmospheric composition exhibits dissimilarity,
changing with different geographical locations on the planet. The solar radiation intensity differs
according to wavelength and the relationship between measured solar intensities and solar radiation
wavelengths is called the solar spectral distribution [13].
When environmental factors change the physical nature of sunlight, PV system electrical output
performance can fluctuate. Some major atmospheric effects can be seen to cause a change in the
total amount of energy and also the particular colour component wavelength distribution of the
solar radiation that finally reaches the land. These changes can result in a reduction of power levels
from a solar panel. The specific effects that cause changes to solar radiation reaching the land
include atmospheric reflections, absorption, and the atmospheric scattering of light. Locality
dependent effects such as water vapour, clouds and pollution can also affect the power, spectrum
and direction of the light incident to a PV panel [12]. For sunlight, a longer path length through the
atmosphere will mean that the aforementioned atmospheric affects can most often have a more
pronounced influence on the spectrum and intensity of the sunlight.
The spectral distribution of the radiation can change due to scattering and absorption of some
wavelengths. Normally, the proportional amounts of radiative energy compared to the total solar
energy available show that roughly around 6% of terrestrial solar energy contains wavelengths in the
UV region of the spectrum, around 50% in the visible light range and around 44% of wavelengths in
the infrared light portion of the spectrum [13].
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Filtering of wavelengths less than around 300nm occur as a result of light absorption by gas
molecules like ozone, nitrogen and oxygen. Specifically, the infrared portion of the spectrum has a
loss of energy and dips because of the effects of atmospheric water and carbon dioxide [13].
For solar radiation outside the atmosphere, the spectral distribution is sometimes known as the
extra-terrestrial or air mass zero (AM 0) spectrum. The graphical apex of this spectrum coincides
with a wavelength value of around 500 nm [14].The measured amount of irradiance is called known
as the solar constant. If a surface was set to face the sun outside the atmosphere at a right angle to
it, it would receive energy at approximately 1360 ⁄ , kept to an average [15]. This value of extra-
terrestrial total irradiance represents the total integrated energy of the whole spectrum.
The spectrum for direct radiation alone, when the sun directly overhead and has a zenith angle of
zero degrees can be known as “Air Mass Direct,” or AM1D. When the sun is in the same position and
global radiation is accounted for, the spectrum is termed AM1G, which is abbreviated from “Air
Mass Global.” A standard spectrum that is recognized globally is Air mass 1.5 or AM1.5,
corresponding to a solar zenith angle of approximately 48.2 degrees. For PV data analysis AM1.5
Global is usually used as a spectral reference. An example plot of the spectrum can be seen in Figure
3 below:
Figure 3: Standard Solar Spectra with AM0, AM1.5 Direct and AM1.5 Global
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AM1.5 Direct
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2.2 PV Module Performance, Measurement and IV Curves
As the analysis of data for this project dealt with the electrical performance of PV modules, it is of
importance to establish how modules capture and harness electrical energy and how this energy is
measured, presented and interpreted.
In a PV cell, the production of electricity relies on a number of crucial occurrences and physical
characteristics. The unique semi-conductive characteristics of silicon and other materials used in
solar cells allow electrical conductivity, while some insulating properties are still present. For Silicon
based cells, the internal structure of the semiconductor is purposely interrupted by the addition of
other elements, creating two distinctive zones. The n-type zone is negatively charged with excess
electrons, while the p-type zone is positively charged, with “holes” [16].
Sunlight travelling from the sun as photons can either reflect off the surface of silicon material, or
cause silicon to emit electrons in a process described as the “photoelectric effect.” This will occur
only if the energy of the photon is higher than the band-gap energy of the semi-conductor. If the
energy is less than the band-gap energy, the photon will not be absorbed and any excess energy
over the band gap amount will be dissipated as heat [5]. Excess electrons from the n-type zone will
cross over to the p-type zone to fill the electron holes.
Electric current will flow in the depletion region, where the free electrons have merged with the
holes. When conductive material is connected to the n-type cathode and the p-type anode,
electrons can flow. The electrons balance total system electrical neutrality by recombining with the
p-type zone holes near the back electrical contact in the solar cell [17].
Different atomic materials and molecular configurations of silicon allow for different solar cell technologies, which can have different electrical performance and efficiency. A single PV cell will typically produce between 1 and watts of power, at a voltage of between 0.5 to 0.6 volts, under standard test conditions. Multiple single solar cells are connected in series so that the overall voltage and power levels of the series circuit are much higher in value. Multiple cells encapsulated and manufactured to perform in a series circuit are called solar modules. Often, the voltage potential is manipulated to match that of a 12V battery for practical purposes. Very often, solar modules will contain 36 cells in series to account for typical load operating voltage fluctuations and other excess energy requirements [18].
The power output and efficiency exists as the main contributing factor in discerning between different PV modules. Generated electrical power and electrical performance of modules can be recorded and is expressed graphically in the form of an I-V curve or PV characteristic curve. Three critical points of interest in an analysis of these plotted curves are the short circuit current ( ), the open circuit voltage ( ) and the maximum power point (MPP) [19]. The short circuit current can be described physically as the maximum current level that will flow through the output terminals of a PV module, meaning that the resistance at the output terminals is very small in magnitude. This can usually be measured by having a conductive wire or cable connected to the output terminals, which has a very low resistance. This connection is often the wiring of an appropriate measuring device.
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The open circuit voltage is physically described as the maximum measured voltage potential difference across the output terminals of a PV module, with the negative terminal being grounded. This is usually measured by a connection being made across the output terminals with an appropriate measuring device [19]. Another variable “fill factor” is also a key indicator for module performance. The fill factor can act as a gauge for the squareness or how close the I-V curve actually resembles that of a perfect situation where it would be a perfect rectangle. Graphically illustrated, it can be seen that the FF represents the proportionality between the output power level that is determined from the product of and compared to the power calculated, using actual observed MPP voltage and current levels
and [20]. As an equation, this can be stated as follows:
⁄
Figure 4: I-V Curve Illustrating Voc, Isc, MPP, Vmp, Imp and Determinants of Fill Factor
2.3 Standard Test Conditions
The expansion of the photovoltaic industry and manufacturing levels of PV modules in the 1980s warranted a need for established standards for performance referencing. Standard reporting conditions for PV modules were developed as a benchmark by the ASTM or the “American Society of
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Testing Materials” committee [21]. PV modules respond differently to different atmospheric spectra. Taking into account that a majority of the world’s major population locations with solar PV installations exist at equatorial locations, the standard spectra AM1.5 was developed as illustrated in Figure 4. This serves as the common reference for international standard, which was developed directly from the ASTM standards. Standard references for module cell temperature are required as module temperature can vary greatly. The STC reference cell temperature is 25 degrees Celsius. The standard reference level of irradiance is ⁄ representing the normalised surface irradiance at sea level on a clear day [22]. A summary of STC can be seen in Table 1 below:
Table 1: Parameters and Values for Standard Test Conditions (STC)
Testing Conditions Parameter Value
Irradiance ( ⁄ 1000
Module Cell Temperature (degrees Celsius) 25
Air Mass Coefficient (AM) AM1.5
2.4 PV Performance Data Mapping Methodologies
In order to compare different PV Modules for performance and degradation analysis, different mapping methodologies are used. These methods translate I-V curve data to desired performance conditions. For mapping to STC, different algebraic and numerical methods can be used. IEC 60891and ASTM E 1036-08 [23] are commonly used standards that contain guidelines for and methods of translation. Other examples methods of that are used with similar procedures to that of ASTM E 1036-08, are the Blaesser method [24] and the Anderson method [25]. Numerical methods can also be used to translate data. An example of a numerical method is mentioned by Hermann and Weisner [26], which relies on a model of the electrical circuit of solar cells and requires certain cell parameters such as the series resistance, shunt resistance, diode ideality factors, and generated photo-current and dark saturation currents. This project focuses specifically on the implementation and assessment of the methods used in the second edition of IEC 60891.
2.4.1 IEC Standards and IEC 60891 Edition 2
The International Electrotechnical Commission or “IEC” are a non-government, non-profit organisation. The IEC is the world’s leading producer and publisher of international standards for engineering in the electrical and electronic fields. The role of the IEC is to provide documental publications that contain content which focuses on instruction and guidelines that may apply when providing certain services, or dealing with certain products and systems. There are numerous standards that apply to the application of PV technology [27]. The standard IEC 60891 2009-12 provides procedures that can make adjustments to measured I-V curves that account for the irradiance and temperature differences from a desired norm, usually
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standard test conditions. Applying methodology outlined in the standard, two I-V curves that were produced under different temperature and irradiance levels can be compared to each other at a new standard level for both formally different variables.
The standard requires certain prerequisites when applying the methodologies. These pre-requisites actually involve other standard practises themselves. When measuring temperature of the PV device, IEC 60904-5 stipulates that the measurement sensor must have an accuracy of 1% [28].When taking measurements for global irradiance, the appropriate device must be calibrated in accordance with the requirements listed in IEC 60904-2 and be within ±2 degrees of the testing module [29]. When comparing one PV module to another, the reference module technology type must be the same as the comparative technology or it has to be spectrally matched according to IEC 60904-7 [30]. In addition, the reference module must adhere to the guidelines of IEC 60904-10 stating that the region must have appropriate linearity. For conversions to STC, the reference device measurements must not fluctuate by more than ±1 % and the global irradiance level must be at least [28]. When recording PV module output I-V curve data using an I-V curve tracer, certain operating requirements are to be adhered to. The current and voltage values that are measured from module operation are to be obtained by an instrument with a 4 wire connection to the output terminals of the module and with a ± 0.2% accuracy level for values of and . The standard IEC 60891 contains different algebraic translation methods. All methods are used to map an initial set of module output data to a set of data that would reflect different temperature and irradiance conditions. The methods can be used for any PV module technology type, but the output test data must show linear behaviour when factoring in changes to temperature and irradiance [31].
2.4.1.1 Correction Procedure 1 The first method in IEC 60891 uses an empirical approach and is based on the work of Sandstrom [32]. Two temperature coefficients alpha (α) and beta (β) are used. Both coefficients illustrate how electrical output parameters behave with changing temperature, with α representing the behaviour of and β representing the behaviour of . Other parameters are necessary in calculations, such as the series resistance ( ) and the curve correction factor (κ). These parameters actually reflect any changes in the graphical form of an I-V curve when temperature or solar irradiance levels change [33]. Two equations are used in the first method:
(
) (1)
(2) [33]
In these equations, and represent a pair of points on a measured I-V curve. and are the new corrected current and voltage that is desired. and are the measured solar irradiance for the testing conditions and the desired solar irradiance for the new conversion, respectively. is the
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measured temperature of the module that has data to be translated. is the temperature that is desired when performing data mapping. is the short circuit current of the testing module when there is an irradiance level corresponding with parameters and . The coefficients and β are implemented as the current and voltage temperature coefficients for the test specimen, which refer to the standard or target irradiance for correction and also within the temperature range of interest. is the internal series resistance of the testing sample and κ is the curve correction factor [23]. .
2.4.1.2 Correction Procedure 2
For correction procedure 2, there are 2 equations to use in deducing the new corrected values for current and voltage:
(3)
( (
)) ( - (4) [33]
This particular method is developed from the one diode model circuit for PV modules. The translation equations that are used are semi-empirical in nature. The equations contain 5 different correction parameters that are able to be deduced by measurement of I-V curves at differing temperature and irradiance conditions. Instead of α and which are from method 1, an initial coefficient κ’ is employed for use to allow for a representation of changes to the fill factor and internal series resistance with temperature. is the open circuit voltage at test conditions. and in the equations are the relative temperature coefficients for current and voltage respectively, of the test module. The coefficients are measured at 1000 ⁄ and are related to STC. The parameter “a” represents the irradiance correction factor for the open circuit voltage which is related to the diode thermal voltage of the p-n junction in the solar module cell material, and the number of solar module cells ( ) which are connected in series within the module in use. is the internal series resistance of the testing module, while κ’ is the temperature coefficient of the internal series resistance [33]. .
2.4.1.4 Thermal Coefficients
Thermal coefficients α, , and from correction procedure 1 and 2 can be determined using
methods outlined in section 4 of IEC 60891 [33]. To determine the coefficients, a constant irradiance
level is selected. I-V characteristics are then calculated at different operating temperatures. The
selected performance parameter ( , or ) is then plotted with module operating
temperature. A “least squares” regression line is then calculated from the performance parameters
temperature plot and added to the same plot, with the gradient of the regression line being the
thermal coefficient. The standard recommends that the thermal coefficients be applied to
irradiances which are within 30% of the irradiance level which they were determined at.
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There are slight differences in the thermal coefficients between correction method 1 and 2, with the
second procedure having coefficients that are normalised by the performance parameter ( , or
) at STC, such that the parameters are dimensionless.
There are options to determine the coefficients indoors with a solar simulator, or also outdoors
using natural solar resources. If determining the temperature coefficients outdoors, the temperature
range must be within at least ± 2 degrees. If determining the coefficient indoors, one can rely on an
irradiance source being provided by a solar simulator and temperature of the testing module being
controlled by temperature control apparatus, such as air-conditioners of close contact heat radiation
devices. The indoor method provides less uncertainty in when finding coefficient values [33].
2.4.1.5 Correction Factors
Correction factors used in the procedures outlined in IEC 60891 must be determined experimentally,
as they are not usually found in PV module specifications. The standard states that the processes
involved in determining and from (1) and (2) are different but the processes for determining
κ and κ’ are the same. The difference in the procedure for finding and is that the parameter a
must be found for .
Finding from correction procedure 1 relies on three different I-V curves being obtained from a
particular module in question. The three curves must all be traced at the same temperature, but at
different irradiance levels. The equations from correction procedure 1 take on a new form as = :
+
(7)
(8) [33]
Equations 7 and 8 show that to obtain , the temperature coefficients are not required. For
correction procedure 2, as = the equations take on a new form:
) (9)
) -
(10) [33]
Similarly, with correction procedure 2, the temperature coefficient parameters are not required to
obtain the relevant series resistance of the module.
Applying these methods to obtain series resistances for both correction procedure 1 and 2 relies on
the I-V curves that are of lower irradiance levels being translated to the level of the highest one.
While doing this, one must start with in equation 8 being set to 0 and and a in equation 10
being set to 0.
The next step is to increase in steps of 10mΩ and when the value of the two translated
lower irradiance value I-V curves are within 0.5% of each other in value, the value for the series
resistance in the equations at that time is the correct one.
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For correction procedure 2, the irradiance correction factor for or a, must be found before
finding the value of To achieve this objective, is kept to zero and a is increased from zero in
steps of 0.001 until the of the translated curves are within 0.5% of each other in value. The value
of a at this point will be correct. Using this value of a, is increased in increments of 10mΩ until
the values of from the two lower irradiance value translated I-V curves are within 0.5% of each
other.
When deducing the curve correction factors κ and κ’, three or more different I-V curves are required.
The different I-V curves must all have the same tested solar irradiance level but also must have
different module operating temperatures.
As the solar irradiance levels do not change for any particular data set, the equations for correction
procedure 1 with parameters , can also be expressed alternately:
(11)
(12)
For correction procedure 2, the original equations will become:
(13)
(14)
Equations 11-14 illustrate that to obtain the curve correction factors κ and κ’, the temperature
coefficients and series resistance for both correction procedure 1 and correction procedure 2 are all
required.
When deducing the correction factors, the selected I-V curve with the lowest temperature is
selected as the reference curve with the two lower curves then translated to match the I-V curve
with the lower temperature. For correction procedure 1, parameter is set initially to zero and for
correction procedure 2, parameter κ’ is also set to zero. The values of each curve correction factor
are then increased by increments of 1 ⁄ and the desired value is reached when the two
translated I-V curves values of are within ±0.5% of each other [33].
2.5 PV Module Degradation
As a PV module ages, the power performance can drop. Other environmental factors can induce
immediate performance loss. From an end user point of view, it is desirable to prevent and avoid as
much degradation of a module as possible, to get more useable power.
14
One study showed that over a ten year period of a PV module can lose 1-2% of its original factory
specified output power performance capabilities due to degradation factors [34]. Another study
found that degradation contributed to a performance loss of around 0.5% per year in a poly-
crystalline module over an eight year period [35].
There are numerous different causes of module degradation. They can be grouped together in
different descriptive categories. Degradation due to cell failure can include problems with panel hot
spots and cells cracking. Package material degradation can occur as a result of encapsulate material
degradation, glass breakage or delamination. Module failure can be induced as a result of soiling and
shading. Changes to the shunt and series resistances can all contribute to power degradation.
Panel hot spots occur when a short circuit occurs in a series connection between cells and the cell
overheats [36]. Cell cracks can occur when an external force or thermal stress is applied. The
packaging material of a module always degrades over time but can hot spot heating of the module
and water intrusion can greatly increase the rate at which this degradation occurs [24]. The glass
encapsulation packaging on the module may break, leading to lowered performance, possibly due to
leakage current. If the series resistance of a solar cell increases, the short circuit current of a solar
module can decrease. If the shunt resistance decreases, the open circuit voltage of a module can
reduce.
The series resistance in a solar cell will normally increase as a result of water vapour inducing
corrosion or delamination of contacts. The shunt resistance can decrease as a result of partial
shading, thermal stress, hot spot occurrence or ohmic shorts [37].
Recombination in a solar cell occurs when an electron hole in the cell material disappears. Electrons
can fall back into the valence band, recombining with the holes. This process will reduce voltage and
current and lower power [8]. This process can often occur at the contacts of the module and on the
surface.
Different types of soiling on the outside surface of a PV module can lead to reduced electrical
current. Dust, animal faecal matter, mud, frost, snow or soot can all accumulate on the surface of
the panel and block or reflect solar radiation.
Module shading can be caused by external environmental factors or also other localised factors
affecting the amount of sunlight reaching the surface of the module. Obstacles such as rooftops,
trees and walls can cause localised shading and horizon shading can be caused by very large objects
such as hills at a distance. The amount of direct radiation reaching a solar module can be greatly
affected by shading.
2.6 Factors Affecting IV Curves and Performance
A number of factors affect the voltage and current and therefore power output levels of a PV
module. Of these factors, some are a product of degradation. Irradiance, temperature, series
irradiance and shading all have an effect on the shape of an I-V curve.
2.6.1 Solar Irradiance
Increasing global irradiance has a positive effect on module power. As seen in Figure 5, increasing solar irradiance levels will in turn increase both the values of and . Many other environmental factors directly affect I-V curve shape by changing the amount of irradiance available to a solar module.
Figure 5: I-V Curves for a Change in Global Irradiance Levels
2.6.2 Module Temperature
Temperature increases in a module can be caused by ambient environmental temperature changes,
cloud patterns and wind speed. Temperature increases in a PV module have an effect in the
graphical representation of the power output. Isc increases slightly, while Voc decreases with rising
temperature. PV module performance is less sensitive to temperature than irradiance changes, but
temperature changes are still significant [19].
16
Figure 6: I-V Curves for a Change in Module/cell Temperature
2.6.3 Module Soiling
The soiling of a module can lower the levels of sunlight that penetrate the absorbing surface of a panel and consequently, each current reading for every different voltage level is reduced. The I-V curve has a similar shape, but the height is affected. This phenomenon can occur with uniform and non-uniform soiling [20].
2.6.4 Module Shunt Resistance
If the shunt resistance of a module changes, the slope of the module performance output I-V curve
near the region can change. Any shunt resistance reductions may cause the slope to be steeper
than normal and appear less flat. The changes to the resistance can be due to shunt paths existing in
the PV cells.
2.6.5 Module Shading Shading of a module in any form will cause a reduction in module output current. If a particular cell is shaded, the other cells connected in series will have a reduction in the maximum current that they may otherwise have produced. Graphically, shading will be shown by notches in an I-V curve [20]. The graphical value of can be reduced.
17
2.6.6 Module Cell Cracking
If a module cell has cracks in it, some physical parts of the cell may become electrically isolated. Cell
cracks can have the same effect on module performance I-V curves as seen by shading.
2.6.7 Environmental Reflection Any reflecting solar radiation received by a module from close foreign objects can actually increase the power output. Any module performance I-V curve will be different graphically and behave similarly as if receiving a larger amount of irradiance [20]. The effect of reflection and increased irradiance is more pronounced in the early day and late afternoon.
2.6.8 Angle of Incidence / Tilt
The angle of incidence a panel in relation to the sun overhead will have an effect on the total solar
radiation that hits the surface of the module [38]. Other factors can come into play when examining
how much irradiance will change if a panel is tilted, such as latitude, albedo and clearness index.
Taking into account the sum of direct, diffuse and any reflected light, the yearly average optimal
maximum solar radiation levels available to a module occur around an angle of incidence that
coincides with the latitude angle of the module location [39].
The power output of a panel and therefore graphically, the of a module performance output I-V
curve, can be affected by the geometrical positioning of the sun (involving the tilt angle of the panel)
and also by any optical effects that depend on the module design. Optical effects on captured
irradiance levels are caused by certain optical characteristics of module materials that are in the
path between the solar radiation and the cells. An example of this is the reflection of radiation from
glass front surfaces on flat plate modules. Reflecting factors become more significant when the tilt
angle of a module exceeds approximately 50 degrees [40].
2.6.9 Orientation
PV Module output power production is close to being linearly proportional to the amount of solar radiation that reaches the surface of the module. The orientation of a solar panel can refer to the azimuth angle of the sun. When the azimuth angle is zero degrees, it is “solar noon” and the sun will be directly south in the northern hemisphere and directly north in the southern hemisphere. Most of
18
the global irradiance comes from the direct irradiance component and more of this direct irradiance can be captured from the sun by a PV module if it directly faces the sun. The more a module is orientated away from the solar noon azimuth angle, the less average solar irradiance it will receive over the course of a day. These principles are at the origin of the reasoning for the existence of solar trackers, which will physically change the orientation of solar panels throughout the day to maximise the amount of absorbed solar radiation [41]. Graphically, the of an I-V curve will be generally effected in the same way by changing module orientation as a change in tilt angle, as both factors result in a change in the total global irradiance received by a PV module.
Figure 7: Azimuth Angle as Seen in the Southern Hemisphere
2.7 PV Module Technologies
Silicon is the second most abundant element on the earth’s surface. Silicon is refined from its
oxidised form of silicon dioxide (Si ) through great heat and a combination with carbon. Ninety
eight percent pure silicon is produced that is further purified through the use of trichlorosilane
(SiH ) to produce silicon that is pure enough to use in producing solar cells and PV modules [42].
Numerous different cell technologies exist and are employed in the use of functional PV modules. In
achieving the aims of this project, three different more common module technologies were used for
analysis to give different physical material performance responses and data sets for contrast and
comparison. The three technologies of focus were amorphous silicon, mono-crystalline and poly
crystalline, with one module representing each technology used as a testing specimen.
19
2.7.1 Mono-Crystalline Silicon Modules
Modules made from this material use a form of crystalline silicon that consists of a crystal lattice that
is continuous throughout the solid material and doesn’t have any grain boundaries. Very large ingots
of silicon mono-crystals are grown and thinly cut into wafers that are ready for more processing to
reach the final product stage. The modules are usually black in colour. Lab efficiencies for modules of
this technology currently rank at 20 percent as of 2012. One square metre of monocrystalline cells
will potentially generate around 190W of power. The modules are more efficient than poly-
crystalline or amorphous silicon modules and are used commonly when space saving is of a concern.
[45].
2.7.2 Poly-Crystalline Silicon Modules
PV Modules made of this form of silicon are made of small crystals that are commonly known as
crystallites, which can contain grain boundaries or 2D defects that can decrease the electrical and
thermal conductivity of the solar cells made of this material. Large rods of the material are made
into ingots and cut into wafers to make cells. Poly-crystalline silicon cells exhibit a “metal flake”
effect causing the outward appearance to have random internal patterns. Modules using this
technology are usually blue in colour, although some newer types are darker blue. A square metre of
polycrystalline cells will generate around 180W of power. Modules have a lower temperature
coefficient than mono-crystalline silicon modules and over a period of time can generate more
useable electrical power than mono-crystalline modules of the same power rating [19].
2.7.3 Amorphous Silicon Modules
Amorphous silicon is known as a second generation cell technology. The material is an alloy of
hydrogen and silicon. The formation of silicon atoms in amorphous silicon resembles a continuous
random network. Amorphous silicon modules typically have performance efficiency percentage
levels in the 6-8% range. The efficiency of this material drops when exposed to light. The efficiency
levels also drop in winter but are better in summer due to annealing. Many modules now use a
hydrogenated dilution to increase module operation quality. The modules are relatively low in cost
to produce, being cheaper than mono or poly crystalline variants. PV applications of amorphous
silicon cells are more typically used indoors [19].
Graphically, performance output I-V curves are affected by light induced module degradation. The
fill factor is less and short circuit current changes, but the open circuit voltage remains relatively
unchanged. [42].
20
2.8 Module Spectral Response
The ability to respond to sunlight is influenced by the band gap of a solar cell material, which is the amount of energy needed to release electrons into the conduction band from the valence band. A larger band gap corresponds with higher energy, so solar cell materials with a higher band gap respond to higher frequency and more energetic portions of the solar spectrum. More precisely, the spectral response limiters of a cell material are the band gap at long wavelengths and the material absorption at shorter wavelengths. For spectral response other influencing factors may include: the independent device design; the material system and the electrical contacts [42,43].
The band gap of amorphous silicon, which is around 1.7 eV, is higher than crystalline silicon (at
around 1.1 eV). As a consequence of the difference in band gap levels, amorphous silicon responds
to the visible part of the solar spectrum more than the higher power density areas of the spectrum
like the infrared frequency area. In summer, the average light path is shorter than in the winter
months, as the sun is more overhead. This means that the higher energy blue light portion becomes
larger than the AM1.5 standard reference spectra. In winter, as the average path length for the
sunlight is longer, the spectrum will contain more “red” or higher wavelength light. As a result of
these occurrences, amorphous silicon modules will have less response in winter months because for
this module technology the spectral response upper limits are at about 800-900 nm in wavelength
[44].
The spectral response region for poly-crystalline silicon and mono-crystalline silicon is different in
the range of wavelengths when compared to the typical response for amorphous silicon. Both poly
and mono-crystalline silicon are more responsive to the portion of sunlight that has a higher
wavelength. When the air mass decreases, generally there is more blue light in the solar spectrum
and this will mean that poly or mono-crystalline modules will be more sensitive to performance
change [45]
The typical differences in the spectral response between amorphous silicon, mono-crystalline and
poly-crystalline silicon cell materials can be seen graphically in figure 8 below:
21
Figure 8: Spectral Response Plots of Different Silicon Solar Cell Materials
22
Chapter 3: Method
3.1 PV Module Selection
Three different PV module technologies were selected for an electrical performance analysis from
those available at Murdoch University.
An amorphous silicon module was selected for testing and was identifiable by the number string of PN -7-02. The module can be seen in figure 8 below:
Figure 9: Amorphous Silicon PV Module Used for Testing
A poly-crystalline silicon module was selected for testing and was identifiable by the make and model: “Sharp PN-1-01.” The module can be seen in figure 10 below:
Figure 11: Poly-Crystalline Silicon PV Module Used for Testing
24
Figure 12: Poly-Crystalline Silicon PV Module Used for Testing
A mono-crystalline silicon module was selected for testing and was identifiable by the make, model and ID number: “Solar E” SE-150M. The module can be seen in figure 12 below:
Figure 13: Mono-Crystalline Silicon PV Module Used for Testing
This project relied on the sustained use of 3 different PV module technologies. Performance data
was taken as a means for analysis and to test data mapping methodologies. The 3 modules used in
the project had some noted signs of degradation, such as cell cracking and soiling. If any further
panel degradation occurred, whether a change in performance was permanent or semi-permanent,
the integrity of any recorded data could be compromised. A visual inspection of the modules was
performed to check for any changes in appearance due to cracking or extra physical stress. Any
controllable factors that could lead to degradation were avoided. Any shading obstacles were
avoided when data capture was done. The modules were very seldom exposed to sunlight as they
were kept indoors when not in use. The storage area was a dry, clean environment with normal
ranges for room temperatures. The modules were also cleaned when any soiling was noted. No
abnormal physical stress was applied to the module surfaces. As a result of these practices, the
condition of the three modules from the first instance of analytical data capture until the last
occurrence was kept as unchanged as possible, meaning that any panel degradation was very low or
negligible and data integrity could be maintained.
26
3.2 Outdoor Testing As established, this project relied on the sustained use of 3 different PV module technologies for outdoor testing. Outdoor electrical performance data was taken as a means for analysis and to test data mapping methodologies. For a given module orientation and tilt, simultaneous measurements of module tilt irradiance and module temperature were made using PV modules. Solar spectral data was also taken at the time of the other measurements.
All outdoor testing was conducted on the campus of Murdoch University, Perth, Western Australia.
The testing location was at a latitude and longitude of 32.066 degrees south and 115.836 degrees
Outdoor electrical performance data was recorded outdoors using a 4 wire connection PROVA 210 I-V curve tracer. I-V Curve data is displayed on the screen for immediate inspection and saved for further use. The device meets the standard requirements for data recording. Connections are made directly to the PV module output terminal wires by clip. Appendix A gives more detailed instructions as to how data was taken using the curve tracer.
Figure 15: PROVA 210 Make and Model Solar Analyser
27
Figure 16: PROVA 210 Make and Model Solar Analyse 4-Wire Connections
3.2.3 Irradiance Data Measurements
Irradiance data was taken with the aid of a Kipp and Zonen irradiance meter. The Instrument
displayed the irradiance level directly in ⁄ . The instrument specifications are contained in
appendix C. Different measurements were taken for each individual orientation and tilt combination
of the 3 modules. The measuring device and technique complied with standard requirements as
described in the international standard.
Figure 17: Kipp and Zonen Irradiance Meter Measuring Irradiance on the Plane of the module
28
Figure 18: Kipp and Zonen Irradiance Meter
3.2.4 PV Module Temperature Measurements
Temperature measurements were made with a Protek 506 Multimeter, switch to temperature data
reading mode. A light gauge wire was used as a back sheet material temperature sensor. This wire
was attached directly to the module via the multimeter. The positioning of the wire contact with the
back sheet was kept away from the cooler perimeter of the module. The positioning was selected to
obtain an average temperature of the module.
Figure 19: PROTEK 506 Multimeter Temperature Reader
29
3.2.5 PV Module Tilt and Orientation Measurements
The principle of a change in global irradiance levels as a result of changing module tilt angle and
orientation is used to get different levels of irradiance from a given set of environmental conditions
over a very short period of time. This is an alternative to recoding module performance data at
different times of the day where the atmospheric spectrum distribution could be different and
therefore add more uncertainty and additional scope for data discrepancies. Tilt angles of
30,35,40,45 and 50 degrees were implemented for each module. Orientations of -75, -37.5, 0, 37.5
and 75 degrees were implemented, with 0 degrees as a reference taken from true north.
Figure 20: Data Recording Location Monument Facing True North
3.2.6 Solar Spectrum Measurements
Solar spectrum data measurements were taken for each new module tilt/orientation combination.
The instrument in use was a StellarNet Spectrometer[46]. The apparatus was placed next to test
modules and matched to the tilt angle. Data was transferred via the spectrometer straight to a
laptop, which could be obtained for analysis.
30
Figure 21: StellarNet Spectrometer Sensor used for Solar Spectrum Data Recording
Figure 22: StellarNet Spectrometer used for Solar Spectrum Data Recording
31
3.3 Indoor Testing
Indoor testing of the selected modules was implemented so that a reference to data measured at
STC was possible. The instrument used for simulation was the Spire 5600SLP solar simulator. The
use of this simulator allows the user to determine parameters of PV modules such as
module efficiency and fill factor. Modules are placed face down between two fastener
guides as seen in figure 23. The computer control monitor shown in figure 24 is used to control the
use of the simulator by appropriate software. Users can change the irradiance, temperature and air
mass coefficient settings that testing panels will be subject to via the control monitor.
The measuring performance specifications of the simulator quote a repeatability of ≤ 0.15% Pmax,
Isc, Voc and FF [47].
Data was taken for each of the poly and mono-crystalline silicon modules as well as the amorphous
silicon module at three different irradiance levels of 1000 ⁄ , 700 ⁄ and 400 ⁄ . For
these different irradiance levels, all data was taken with a module temperature of 25 degrees and an
air mass coefficient of AM1.5.
Data was also taken with the irradiance level held constant at 1000 ⁄ at and air mass coefficient
held constant at AM1.5 but with a range of different temperatures. The data was simulated under
these circumstances to allow the temperature coefficients to be evaluated.
Figure 23: The Spire 5600SLP Solar Simulator Located at Murdoch University
32
Figure 24: The Spire 5600SLP Solar Simulator Control Monitor
3.4 Procedure Correction Factors
All the parameters described in section 2.4.1.4 and 2.4.1.5 were determined by the use of the indoor
testing data, as described in section 2.4. The graphical results for the determination of the correction
factors can be seen in appendix B.
3.5 Experimental Device Limitations
Accuracy of experimentally recorded data can be greatly affected by the uncertainty and consistency
of a particular recording apparatus. Table 2 lists the uncertainties of the devices used in this project
when recording data. These uncertainties can become cumulative and become greater when device
data is combined in relation with another set of data from a different device.
Table 2: Uncertainties of Measurement Parameters by Data Recording Apparatus
Figure 25,26 and 27 represent results for I-V curve electrical performance data recorded indoors at STC for 3 different modules, using the SPI sun simulator.
Figure 25: Indoor Measured Current vs Voltage Output for Mono-Crystalline Silicon at STC
0
1
2
3
4
5
6
0 10 20 30 40 50
Cu
rre
nt
(A)
Voltage (V)
Indoor Measured Current vs Voltage Output for Mono-Crystalline Silicon at STC
34
Figure 26: Indoor Measured Current vs Voltage Output for Poly-Crystalline Silicon at STC
Figure 27: Indoor Measured Current vs Voltage Output for Amorphous Silicon at STC
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8 10 12 14 16
Cu
rre
nt
(A)
Voltage (V)
Indoor Measured Current vs Voltage Output for Poly-Crystalline Silicon at STC
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 10 20 30 40 50 60 70
Cu
rre
nt
(A)
Voltage (V)
Indoor Measured Current vs Voltage Output for Amorphous Silicon at STC
35
Table 3: Critical Electrical Parameters at STC for Different Module Technologies
4.2 Correction Procedure 1 and 2 Parameters
Table 4 contains the values obtained for the parameters needed to implement Correction
procedures 1 and 2, which vary in value for the three different module technologies used.
Table 4: Correction Procedure 1 and 2 Parameters for Different Module Technologies
Tables 5, 6 and 7 represent data sets for outdoor field testing of the three different module technologies. In the three tables, for each different combination of varied module orientation and tilt, the module temperature and measured tilted solar irradiance are listed.
Table 5: Mono-Si: Outdoor Testing Electrical Performance Data
Table 6: Poly-Si: Outdoor Testing Electrical Performance Data
Data Number
Module Orientation (Degrees)
Module Tilt (degrees) Module Temperature (⁰C) Irradiance
( ⁄
1 -75 30 39 703
2 -75 35 43 717
3 -75 40 45 721
4 -75 45 44 711
5 -75 50 45 723
6 -37.5 30 45 803
7 -37.5 35 46 822
8 -37.5 40 47 844
9 -37.5 45 48 846
10 -37.5 50 46 881
11 0 30 46 878
12 0 35 48 900
13 0 40 48 930
14 0 45 48 968
15 0 50 49 954
16 37.5 30 46 770
17 37.5 35 48 763
18 37.5 40 47 740
19 37.5 45 47 742
20 37.5 50 43 746
21 75 30 37 418
22 75 35 36 383
23 75 40 33 335
24 75 45 32 284
25 75 50 32 230
38
Table 7: Amorphous-Si: Outdoor Testing Electrical Performance Data
Data Number
Module Orientation (Degrees)
Module Tilt (degrees) Module Temperature (⁰C) Irradiance
( ⁄
1 -75 30 28 635
2 -75 35 28 542
3 -75 40 28 559
4 -75 45 28 502
5 -75 50 28 511
6 -37.5 30 28 868
7 -37.5 35 28 890
8 -37.5 40 29 857
9 -37.5 45 28 822
10 -37.5 50 27 680
11 0 30 30 884
12 0 35 31 894
13 0 40 31 879
14 0 45 31 1022
15 0 50 32 1060
16 37.5 30 32 965
17 37.5 35 32 784
18 37.5 40 31 935
19 37.5 45 32 937
20 37.5 50 33 1001
21 75 30 32 762
22 75 35 31 830
23 75 40 31 786
24 75 45 30 765
25 75 50 32 756
39
4.4 Correction Procedure 1
4.4.1 Correction for Outdoor Tested Modules to Non-STC
When referring to the mono-si testing module, Table 8 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 1007 W/m^2 and 49 degrees celcius, by implementing correction procedure 1.
When referring to the poly-si testing module, Table 9 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 968 W/m^2 and 48 degrees celcius , by implementing correction procedure 1.
When referring to the amorphous silicon testing module, Table 10 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 1001 W/m^2 and 33 degrees celcius, by implementing correction procedure 1.
Table 8: Mono-Si: Difference(%)in Pmax(W) After Correction to 1007 ⁄ Using Correction Procedure 1
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 4.73 1.40 4.35 4.62 2.97
35 6.21 0.87 N/A 3.73 5.65
40 7.54 0.16 0.24 1.93 10.27
45 12.20 0.81 0.09 6.63 5.94
50 22.97 0.81 0.50 7.69 16.92
40
Table 9: Poly-Si: Difference(%)in Pmax(W) After Correction to 968 ⁄ Using Correction Procedure 1
Table 10: Amorphous-Si: Difference(%)in Pmax(W) After Correction to 1001 ⁄ Using Correction Procedure 1
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 0.61 2.87 3.05 5.27 1.68
35 5.38 1.69 3.07 3.99 3.60
40 1.24 2.79 3.40 0.20 5.40
45 2.17 2.53 N/A 0.50 7.89
50 1.26 3.16 3.28 3.99 16.00
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 13.41 3.77 12.58 3.16 1.48
35 2.23 7.20 15.67 20.09 13.39
40 15.48 1.27 19.52 7.26 10.44
45 13.18 0.71 1.14 7.09 0.11
50 15.92 18.40 0.78 N/A 4.80
41
4.4.2 Correction for Outdoor Tested Modules to STC
When referring to the mono-si testing module, Table 11 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 1.
When referring to the poly-si testing module, Table 12 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 1.
When referring to the amorphous silicon testing module, Table 13 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 1.
Table 11: Mono-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 1
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 6.26 2.89 2.55 7.34 2.10
35 7.66 0.89 1.94 5.30 12.54
40 8.86 1.68 2.55 3.60 11.28
45 13.44 1.05 0.44 8.81 11.55
50 16.55 2.33 2.34 9.26 17.70
42
Table 12: Poly-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 1
Table 13: Amorphous-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 1
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 9.34 8.54 7.80 10.37 11.74
35 8.71 6.89 6.71 8.24 13.94
40 7.15 7.38 7.09 4.61 16.86
45 8.52 6.63 8.97 7.01 19.52
50 7.18 8.14 6.80 10.76 26.73
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 21.85 13.05 1.80 12.41 11.63
35 11.77 16.11 4.59 12.44 21.67
40 23.74 10.78 8.06 3.01 19.05
45 21.81 8.98 8.54 3.15 11.33
50 24.11 13.73 10.25 9.56 5.32
43
4.5 Correction Procedure 2
4.5.1 Correction for Outdoor Tested Modules to Non-STC
When referring to the mono-si testing module, Table 14 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 1007 W/m^2 and 49 degrees celcius, by implementing correction procedure 2.
When referring to the amorphous silicon testing module, Table 15 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 1001 W/m^2 and 33 degrees celcius, by implementing correction procedure 2.
When referring to the poly-si testing module, Table 16 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to 968 W/m^2 and 48 degrees celcius, by implementing correction procedure 2.
Table 14: Mono-Si: Difference(%)in Pmax(W) After Correction to 1007 ⁄ Using Correction Procedure 2
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 1.21 2.29 5.41 4.64 10.86
35 3.18 1.72 N/A 2.84 6.47
40 3.44 1.68 0.47 1.25 4.68
45 8.40 2.74 1.45 6.17 5.87
50 11.99 0.84 0.19 7.34 14.37
44
Table 15: Amorphous-Si: Difference(%)in Pmax(W) After Correction to 1001 ⁄ Using Correction Procedure 2
Table 16: Poly-Si: Difference(%)in Pmax(W) After Correction to 968 ⁄ Using Correction Procedure 2
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 2.07 0.52 0.34 2.53 4.37
35 1.18 1.49 1.63 0.16 8.09
40 0.55 0.93 1.41 3.58 12.46
45 0.84 1.72 N/A 0.85 16.73
50 0.34 0.34 2.24 3.08 25.72
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 17.84 6.45 9.90 4.10 5.72
35 6.95 9.40 13.30 0.60 15.99
40 22.08 3.96 16.76 5.23 13.54
45 19.68 2.37 N/A 5.17 3.52
50 21.13 -20.57 1.58 1.29 1.14
45
4.5.2 Correction for Outdoor Tested Modules to STC
When referring to the mono-si testing module, Table 17 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 2.
When referring to the poly-si testing module, Table 18 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 2.
When referring to the amorphous silicon testing module, Table 19 represents the percentage difference in the values of generated Pmax, with respect to the solar irradiance before and after correction to STC, by implementing correction procedure 2.
Table 17: Mono-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 1
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 2.57 2.72 10.11 1.31 16.25
35 0.27 6.47 4.11 1.24 3.48
40 0.44 6.24 2.85 2.98 1.89
45 6.05 6.96 5.93 3.52 3.17
50 10.11 5.28 3.72 3.93 12.23
46
Table 18: Poly-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 2
Table 19: Amorphous-Si: Difference(%) in Pmax(W) After Correction to STC Using Correction Procedure 2
Panel Orientation
(degrees)
Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 4.81 6.32 6.58 3.95 1.57
35 5.32 8.37 8.60 6.40 2.38
40 7.24 7.79 8.40 10.53 6.97
45 6.98 8.58 6.74 7.51 11.72
50 6.30 7.21 9.25 3.46 21.55
Panel Orientation
(degrees) Panel Tilt (degrees)
-75
-37.5
0
37.5
75
30 24.67 14.41 0.93 11.99 13.49
35 14.69 16.88 4.04 14.70 22.94
40 28.64 11.87 7.21 3.38 20.70
45 26.39 10.41 8.20 3.41 11.49
50 27.71 10.69 9.64 9.34 7.19
47
4.6 Correction Procedure 1: ΔPmax and Module Orientation
Figure 28,29,30,31 and 32 display data relating to the implementation of correction procedure 1.
Figure 28 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a -75 degree module orientation.
Figure 29 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a -37.5 degree module orientation.
Figure 30 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 0 degree module orientation.
Figure 31 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 37.5 degree module orientation.
Figure 32 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 75 degree module orientation.
Figure 28: Corrected Difference in Pmax for -75 Degree Module Orientation
0.00
5.00
10.00
15.00
20.00
25.00
30.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for -75 Degrees Orientation
Poly-Si M1
Mono-Si CP1
Amorphous-Si
Mono-Si to STC
Poly-Si to STC
Amorphous Si to STC
48
Figure 29: Corrected Difference in Pmax for -37.5 Degree Module Orientation
Figure 30: Corrected Difference in Pmax for 0 Degree Module Orientation
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for -37.5 Degree Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC CP1
Amorphous Si to STCCP1
0.00
5.00
10.00
15.00
20.00
25.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 0 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC CP1
Amorphous Si to STCCP1
49
Figure 31: Corrected Difference in Pmax for 37.5 Degree Module Orientation
Figure 32: Corrected Difference in Pmax for 75 Degree Module Orientation
0.00
5.00
10.00
15.00
20.00
25.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 37.5 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC CP1
Amorphous Si to STCCP1
0.00
5.00
10.00
15.00
20.00
25.00
30.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 75 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC CP1
Amorphous Si to STCCP1
50
4.7 Correction Procedure 2: ΔPmax and Module Orientation
Figure 33,34,35,36 and 37 display data relating to the implementation of correction procedure 2.
Figure 33 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a -75 degree module orientation.
Figure 34 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a -37.5 degree module orientation.
Figure 35 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 0 degree module orientation.
Figure 36 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 37.5 degree module orientation.
Figure 37 shows the relationship between module tilt angle and the percentage difference in Pmax
after correction, for a 75 degree module orientation.
Figure 33: Corrected Difference in Pmax for -75 Degree Module Orientation
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
rnce
in P
max
(%
)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for -75 Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC
Amorphous Si to STC
51
Figure 34: Corrected Difference in Pmax for -37.5 Degree Module Orientation
Figure 35: Corrected Difference in Pmax for 0 Degree Module Orientation
0.00
5.00
10.00
15.00
20.00
25.00
30.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for -37.5 Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC
Amorphous Si to STC
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 0 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC
Amorphous Si to STC
52
Figure 36: Corrected Difference in Pmax for 37.5 Degree Module Orientation
Figure 37: Corrected Difference in Pmax for 75 Degree Module Orientation
0.00
5.00
10.00
15.00
20.00
25.00
30.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 37.5 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC
Amorphous Si to STC
0.00
5.00
10.00
15.00
20.00
25.00
30.00
30 35 40 45 50
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax (
%)
Module Tilt (Degrees)
Corrected Difference in Maximum Output Power for 75 degrees Orientation
Poly-Si CP1
Mono-Si CP1
Amorphous-Si CP1
Mono-Si to STC CP1
Poly-Si to STC
Amorphous Si to STC
53
4.8 Averaged ΔPmax Variation for Influencing Parameters
Figure 38 illustrates the average percentage difference in the value of Pmax after correction for
correction procedure 1 and 2 for all three module technologies.
Figure 39 illustrates the average percentage difference in the values of Pmax after correction for
correction procedure 1 and 2 for all possible PV module orientations.
Figure 40 illustrates the average percentage difference in the values of Pmax after correction for
correction procedure 1 and 2 for all possible PV module tilts.
Figure 38: Average Percentage Difference in Pmax after Correction vs Module Technology and Correction Procedure
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Technology and Procedure
Ave
rage
Pe
rce
nta
ge D
iffe
ren
ce (
%)
Average Percentage Difference in Pmax after Correction
Mono-Si CP1
Poly-Si CP1
Amorphous-Si CP1
Mono-Si CP2
Poly-Si CP2
Amorphous-Si CP2
CP1 Overall
CP2 Overall
54
Figure 39: Average Percentage Difference in Pmax after Correction vs Module Orientation and Correction Procedure
Figure 40: Average Percentage Difference in Pmax after Correction vs Module Tilt and Correction Procedure
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Procedure and Orientation
Ave
rage
Pe
rce
nta
ge D
iffe
ren
ce in
Pm
ax(%
)
Average Difference in Pmax vs Correction Procedure and Orientation
CP1 -75 Orientation
CP1 -37.5 Orientation
CP1 0 Orientation
CP1 37.5 Orientation
CP1 75 Orientation
CP2 -75 Orientation
CP2 -37.5 Orientation
CP2 0 Orientation
CP2 37.5 Orienation
CP2 75 Orientation
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Procedure and Tilt
Ave
rage
Pe
rce
nta
ge D
iffe
ren
ce (
%)
Average Difference of Pmax vs Correction Procedure and Tilt
CP1 30 Tilt
CP1 35 Tilt
CP1 40 Tilt
CP1 45 Tilt
CP1 50 Tilt
CP2 30 Tilt
CP2 35 Tilt
CP2 40 Tilt
CP2 45 Tilt
CP2 50 Tilt
55
4.9 ΔPmax vs Irradiance and Temperature
Figure 41 concerns correction procedure 1. The figure illustrates the relationship between the
percentage difference of Pmax after correction and the size of irradiance correction from the
reference module used, for all three possible module technologies.
Figure 42 concerns correction procedure 2. The figure illustrates the relationship between the
percentage difference of Pmax after correction and the size of irradiance correction from the
reference module used, for all three possible module technologies.
Figure 43 concerns correction procedure 1. The figure illustrates the relationship between the
percentage difference of Pmax after correction and the size of temperature correction from the
reference module used, for all three possible module technologies.
Figure 44 concerns correction procedure 2. The figure illustrates the relationship between the
percentage difference of Pmax after correction and the size of temperature correction from the
reference module used, for all three possible module technologies.
Figure 41: Correction Procedure 1- Percentage Difference of Pmax (%) vs Irradiance Difference from Reference Module
(W/m^2)
0.00
5.00
10.00
15.00
20.00
25.00
30.00
0 200 400 600 800 1000
Pe
rce
nta
ge D
iffe
ren
ce o
f P
max
(%
)
Irradiance Difference (W/m^2)
Correction Procedure 1: Percentage Diffference of Pmax (%) vs Irradiance Difference from Reference Module (W/m^2)
Amorphous-SiCP1
Mono-Si CP1
Poly-Si CP1
56
Figure 42: Correction Procedure 2- Percentage Difference of Pmax (%) vs Irradiance Difference from Reference Module (W/m^2)
Figure 43: Correction Procedure 1- Percentage Difference of Pmax (%) vs Temperature Difference from Reference
Module (W/m^2)
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0 200 400 600 800 1000
Pe
rce
nta
ge D
iffe
ren
ce o
f P
max
(%
)
Irradiance (W/m^2)
Correction Procedure 2: Percentage Diffference of Pmax (%) vs Differnce from Reference Module (W/m^2)
Amorphous-Si CP2
Mono-Si CP2
Poly-Si CP2
0.00
5.00
10.00
15.00
20.00
25.00
30.00
0 5 10 15 20 25 30
Pe
rce
nta
ge D
iffe
ren
ce o
f P
max
(%
)
Difference from 25 degree Refererence Temperature (Degrees)
Correction Procedure 1: Percentage Diffference of Pmax (%) vs Temperature Difference of Reference Module (Degrees)
Amorphous-Si CP1
Mono-Si CP1
Poly-Si CP1
57
Figure 44: Correction Procedure 2- Percentage Difference of Pmax (%) vs Temperature Difference from Reference
Module (W/m^2)
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0 5 10 15 20 25 30
Pe
rce
nta
ge D
iffe
ren
ce o
f P
max
(%
)
Difference from 25 Degree Reference Temperature (Degrees)
Correction Procedure 2: Diffference of Pmax (%) vs Temperature Difference of Reference Module (Degrees)
Amorphous-Si CP2
Mono-Si CP2
Poly-Si CP2
58
4.10 Spectral Distribution and AM1.5
Figure 45 illustrates different spectral distribution plots involving data captured on different days.
Included in this figure is a plot of AM1.5 global for data comparison.
Figure 45: Outdoor Tested Spectral Irradiance Distribution and AM1.5
0
0.2
0.4
0.6
0.8
1
200 400 600 800 1000
Spe
ctra
l Irr
adia
nce
(W
m^(
-2)n
m^(
-1))
Wavelength (nm)
AM1.5Global
930irradiance2/05/16
418Irradiance2/05/16
1007Irradiance10/05/16
1005Irradiance16/05/16
59
Chapter 5: Analysis and Discussion of Correction Procedure Performance
The percentage deviation of the magnitude of Pmax from a corrected I-V curve to the original
reference I-V curve was used as a basis for assessment of the effectiveness and accuracy of the first
two correction methods in the standard IEC 60891. An average of the percentage difference of Pmax
before and after correction was also noted when taking into account different factors that could
affect module performance. PV Module orientation, module tilt and module technology were
independently focused on in this analysis.
The spectral distribution of the irradiance for different testing days was compared to the standard
AM1.5 spectra.
5.1. Orientation, Tilt and Correction Procedure Accuracy
It is observed in Figures 28-32 involving correction procedure 1, that in general, modules tested that
utilised amorphous-silicon module technology showed the highest percentage Pmax deviation. This
deviation was observed when referencing the data from all possible module orientations, except
where the module orientation was at 75 degrees in Figure 32. The data in Figure 32 shows that the
percentage deviation of Pmax is highest for the module utilising Poly-Si technology.
In Figure 28 and 32, it can be seen that at the module orientations of 75 degrees and -75 degrees
that modules tested using mono-si technology show higher deviation of Pmax. This observation is
made for module tilt angles of 37.5 degrees and zero degrees.
On average, for both correction procedures, tested modules that were made from amorphous
silicon show higher recorded levels of variation in Pmax, with a greater spread of values for varying
module tilt and module orientation combinations.
For Figures 28-37 where the PV technology of the test specimen is noted as a point of difference, it is
difficult to note any significant overall correlations or linear trends for any of the different module
orientation and module tilt angle combinations. It can be seen in Figure 39, when differences in
module technology are not taken into account, that there are increasing higher average deviations in
Pmax with module orientations further away from 0 degrees. This observation is made for both
correction procedures. The highest average deviation of Pmax is at 14.2% for a 75 degree module
orientation with correction procedure 1. The lowest average deviation of Pmax is at 5.36% for a 0
degree module orientation for correction procedure 1.
When module technology differences are not taken into account as in Figure 40, an increasing
module tilt angle generally correlates with greater average deviation from Pmax in both correction
procedures. There is an overall greater error associated with correction procedure 1, peaking at an
average 11.38% difference for a 50 degree module tilt angle. Out of both procedures, the lowest
margin of difference (7.99%) is at a module tilt angle of 35 degrees for procedure 2.
60
From both Figure 39 and 40 it can be observed that differences in module orientation have a more
immediate effect on the accuracy of the correction procedures, as the % deviation of Pmax for both
procedures has a much greater rate of change than for changes in module tilt angles.
5.2 Irradiance and Correction Procedure Accuracy
From Figures 41 and 42, it can be observed that if a translation of data is made between two IV
curves of different irradiance levels, there is a weak general correlation between higher levels of
difference in curve irradiance levels and higher deviation in Pmax. This observation is made with
reference to variations in module technology. For correction method 2, the observation could just
as easily be dismissed for mono-si and poly-si, as the linear trend-lines show very weak to non-
existent correlation.
5.3 Temperature and Correction Procedure Accuracy
Referencing figures 43 and 44, an inspection on potential relationships between the size of
temperature correction and variations of Pmax after data correction can take place. The fitted linear
trend-lines in Figure 43 and 44 show that for correction procedure 1 and 2, data correction with
amorphous-si data displays a moderate inverse relationship between the two variants. It is observed
that as the temperature correction increases, the difference in Pmax decreases. Mono-si data for the
use of correction method 1 shows weak inverse correlation between temperature correction and
Pmax variation. It is observed that as the temperature correction is larger the difference in Pmax
decreases.
5.4 Module Technology and Correction Procedure Accuracy
Figure 38 illustrated that data corrected from the testing of PV modules using mono-si technology
has the least difference in Pmax variation for both correction procedures. For both correction
procedures, data corrected from the testing of modules using poly-si technology is more accurate
than data corrected from modules that utilise amorphous silicon technology. Corrected data
obtained from the use of poly-si module technology was not as accurate as corrected data that was
obtained from the use of mono-si module technology. The correction data set that was the most
accurate came from the test specimen utilising mono-si module technology and data correction
procedure 2. For this module technology and procedure combination, there was only a 4.95%
variation in Pmax. The correction data set that was the least accurate came from the test specimen
utilising amorphous silicon module technology and data correction procedure 2. For this module
technology and procedure combination, there was a 13.4% variation in Pmax.
61
5.5 Correction Procedure Comparison
From an analysis of Figure 38, on average, correction procedure 2 is slightly more effective overall
than correction procedure 1. This conclusion can be made because less difference in the values of
Pmax after correction will mean that a correction procedure is more effective. As stated in the
standard IEC 60891, correction procedure 2 produces better results for irradiance differences over
20%. From the data set a large proportion of the data corrections were made with irradiance
differences of at least 20%.
Overall, the mismatch accuracy level of correction procedure 1 by measuring the variation of Pmax is
observed as 9.54% and correction procedure 2 as 8.58%. These figures are not as high as expected,
but data measurement methods and measurement conditions must factor in figures for uncertainty
and margins for error.
5.6 Spectral Distribution
The winter weather patterns at the Murdoch location at the time of data collection for this project
provided some interesting atmospheric conditions. The spectral irradiance distribution for the
location taken over a number of different days can be seen if figure 45.
When compared to AM1.5, the recorded atmospheric spectra data shows a notable ”red shift”, with
results typical for winter weather conditions, where the average solar radiation path length to the
ground is longer in distance.
The larger inaccuracies shown on average by data translated from the module using amorphous
silicon technology could be attributed somewhat to the different spectral response from that of the
modules utilising the more similarly responsive technologies of poly-si and mono-si. The shift away
to a lower frequency, redder light variation means that the amorphous silicon module performance
is affected proportionately more than the other module technologies.
The correction parameters are developed from data indoors using simulated AM1.5 spectra, but
outdoors, data comes from altered spectra, as seen in figure 45. Applying correction parameters
developed under different solar irradiance spectral distributions could lead to errors.
For a certain level of received solar irradiance, module testing specimen electrical power production
and recorded module electrical output I-V curve dynamics could be different if a given module
technology is more spectrally responsive to the given distribution of solar spectral wavelengths.
Correction procedures in IEC 60891 do not correct for air mass coefficient discrepancies.
62
5.7 Measurement Device Uncertainty
The measuring device uncertainties listed in Table 2 may contribute to larger than expected
inaccuracies in the application of the two data correction procedures. Given that the indoor testing
data was used to develop the correction parameters, the uncertainty of the SPIRE solar simulator is
the only device relevant when these parameters come into consideration.
When applying the procedures in entirety, the cumulative uncertainties of the irradiance meter and
the multimeter must be considered, as they are both used for the temperature and irradiance levels
used for translation. These uncertainties could very well contribute to the overall higher than
expected inaccuracies for the correction procedures and create possible data outliers.
Another uncertainty to factor in when considering results is that the temperature coefficients are
recommended for use with solar irradiance levels of no more than ±30% of the solar irradiance level
used to determine them. The level of irradiance used in this project to determine the coefficients
was . A large proportion of the irradiance differences corrected for were greater than
the recommended level.
Chapter 6: Future Works
The scope of this project could be extended for future research. A larger pool of PV module electrical
performance data, recorded at different times of the year, would be effective in determining a more
accurate figure for the accuracies of the correction methods.
Different module technologies, such as more efficient newer multi-cell types could be used for
testing to make more relevant assessments on the current relevance of the correction procedures.
The standard IEC 60981 contains a third correction method, not involving correction parameters, but
multiple I-V curves for correction. An inclusion of the third method in the workings of this project for
analysis and comparison with the first two data correction methods could possibly extend the scope
and effectiveness of this project.
63
Chapter 7: Conclusion
An initial literary review was conducted prior to the implementation of methodologies explored in
this project. An understanding of physical concepts involved in the processes of electrical energy
being produced by PV modules was developed. Parameters involved in measuring and presenting
electrical data were investigated and understood, to formulate appropriate experimental
methodologies to effectively test the first two mapping methodologies in IEC 60891.
Factors that may hinder the collection of and integrity of performance data were examined and
minimised, such as controllable PV degradation factors. Factors that may change the nature of
produced I-V curves that were not implemented in the standard were similarly avoided when
capturing test data.
One example each of poly-crystalline, mono-crystalline and amorphous silicon modules were used
effectively to obtain a pool of data for analysis. Spectral irradiances of the data recording location
were recorded for interpretation, with a development of understanding gained for light spectra
present during winter months at Murdoch.
Indoor testing data was obtained using the on-campus solar simulator at the Murdoch University
location. The data was used in conjunction with the method in the international engineering
standard IEC 60891 to produce correction parameters for the procedures set for analysis.
The established correction parameters were used to map PV module electrical performance data to
data which would reflect that recorded with different irradiance and temperature levels. In
achieving this aim, a different range of temperature and irradiance differences were included in
practice. The implementation of both data correction procedures produced reasonable results for
analysis.
From the results of the study, it was established that both correction methods were somewhat
effective, with overall averaged accuracy levels of 9.54% for correction procedure 1 and 8.58% for
correction procedure 2. The second correction procedure involved the development of extra
parameters, which make its implementation more time consuming than the first correction
procedure. Either procedure could be preferred for use, depending on the circumstances involved.
These correction methods, implemented effectively, can be useful in comparing two different PV
modules for electrical performance or to assess the electrical performance degradation levels of a
particular module compared to its factory listed specifications.
64
References
[1] "What is renewable energy?", Australian Renewable Energy Agency, 2013. [Online]. Available:
[39]M. Benghanem, "Optimization of tilt angle for solar panel: Case study for Madinah, Saudi
Arabia", Applied Energy, vol. 88, no. 4, pp. 1427-1433, 2011. [40]”Photovoltaic module and array performance characterization methods for all system operating
conditions”. Washington, DC: United States. Dept. of Energy. Office of Energy Efficiency and
Renewable Energy, 1996.
[41] "Solar Panel Orientation and Positioning of Solar Panel", Alternative Energy Tutorials, 2016.
