Development of electroluminescence imaging for quantitative image analysis of photovoltaic modules

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Master’s Thesis 2020 30 ECTS Faculty of Science and Technology

Development of

electroluminescence imaging for quantitative image analysis of photovoltaic modules

Jonas Rydtun Winsvold

Environmental Physics and Renewable Energy

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Acknowledgements

This thesis will seek to establish a relationship between current and EL signal and provide the groundwork for establishing an electroluminescence imaging setup and a corresponding imaging procedure with the aim of quantitative degradation analysis and performance estimation. The task is an initiative by the Department for Renewable Energy Systems (ENSYS) of the Institute for Energy Technology (IFE) to increase the capacity and capability of characterizing PV-devices.

The process of writing a master has been exceptionally educational, fun, at times, exhausting, and an experience way beyond expectations. I have had the honor of completely destroying a module, with the great help of my advisor Gaute, after it proved to be way more robust than expected. I’ve also improved my programming and writing abilities, with the great support of the wast forums of the internet and the help of my advisors and friends.

Thanks to all the fantastic people, helping me during this process, everything was up to my abilities and my willingness to perform.

First of all, Gaute Otnes, my supervisor at IFE, deserves a special thanks for his problem-solving abilities, creative solutions, and his patience. Arne Auen Grimenes, my supervisor at NMBU, also deserves special recognition for guiding me from the start to this exciting subject, his enthusiasm, austerity, and always well-placed advice.

I also want to thank Bjørn Lupton Aarseth, Heine Nygard Riise, and Halvard Haug for assisting me when problems seemed unsolvable or not understandable. They are always happy to help.

Last but not least, thanks to all my family and friends for supporting me through this strenuous process. Special thanks go to Danayt Wolday, Emmanuel Skånseng, Taylor Ricca, and Daniel Mirzayan.

Kjeller, 28.07.2020 Jonas Rydtun Winsvold

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Med en betydelig etterspørsel etter fornybar energi, der PV-teknologi har et stort potensial for en økning i effektivitet, arbeides det kontinuerlig med enorm instats for å forbedre den. Elektrolumi- nescens er en av de lovende teknologiene som muliggjør degraderingsanalyse av PV-enheter og kan bidra i arbeidet med å øke systemeffektiviteten.

Formålet med denne studien er å etablere et elektroluminescensavbildningsoppsett med et modifis- ert, kommersielt tilgjengelig Nikon D850-kamera. I et provisorisk mørkerom er moduler forspent for å avgi elektroluminescenssignaler med en datastyrt strømforsyning. En tilsvarende bildebe- handlingsprosedyre utvikles, med sikte på kvantitativ degraderingsanalyse og ytelsesestimering.

Prosedyren benyttet offentlig tilgjengelig programvare for bildekorrigering i kombinasjon med utviklede algoritmer for estimering av ytelse.

En korrelasjon mellom bildeintensiter og den påførte strømmen ble forsøkt etablert. Resultatene indikerte imidlertid en eksponell trend i korrelajonen, imotsetning til den linære korrelsjonen utledet teorien. Årsaken til den exponensielle sammenhengen vil derfor kreve mer forskning.

Imidlertid ble degraderingsnivåer estimert for en modul, med en korrelasjonsuavhengig, histogram- basert, degraderingsestimeringsmetode. Sammenligning av degraderingsnivåene med reduksjonen i kortslutningsstrøm resulterte i en tilsvarende trend mellom de to, som vist i figur ref fig: morph- d-area. Degraderingsnivåene bar tydelig preg av overstimering. Denne feilestimering var antakelig et resultat av en underutviklet algoritme. Estimeringen kan derfor enkelt forbedres med en bedre algoritmeutvikling.

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Abstract

With a considerable demand for renewable energy where PV technology has a high potential for an increase in efficiency, there are continuously immense efforts to improve it. Electroluminescence is one of the promising technologies enabling quantitative degradation analysis of PV devices and could help the efforts to increase system efficiency.

The purpose of this thesis is to establish an electroluminescence imaging setup utilizing a modified, commercially available, Nikon D850 camera. In a provisional darkroom are modules exited to emit electroluminescence signals with a computer-controlled power supply. A corresponding imaging procedure is developed, with the aim of quantitative degradation analysis and performance estimation. The procedure utilized publicly available software for image correction in combination with developed algorithms for performance estimation.

This thesis attempt to establish a correlation between image intensities and the applied current.

However, the results indicated an exponential trend in the correlation, as opposed to the linear correlation derived in the theory. Therefore, the reason for the exponential relationship will require more research.

However, a correlation independent, histogram-based, degradation estimation method successfully estimate degradation levels of one module. Comparing the degradation levels to the decrease in short circuit current resulted in a similar trend between the two, as displayed in figure 6.5. The levels of degeneration were marked by overestimation. This estimation error was likely a result of an underdeveloped algorithm. Therefore, estimates can easily be improved with better algorithm development.

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Nomenclature

Abbreviations Si Silicon

BIPV Building integrated PV eCS Enhanced crack segmentation EL Electroluminescence

IEC 60904 IEC-DTS 60904-13 technical specification draft “Electroluminescence of photovoltaic modules”

IV Current-voltage LED Light emitting diode PV Photovoltaic

ROI Region of interest Constants

e The natural exponential function 2.71828

k Boltzmann’s constant 1.380·10⁻²³J/K

q The elementary charge 1.602·10⁻¹⁹C

Symbols

α Angel °

η Efficiency %

φ Luminescence –

~k Wave vector in the direction of momentum. m⁻¹

b Histogram bin –

C Calibration factor –

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NOMENCLATURE

E Energy eV

F F Fill factor –

G Gaussian operator detecting the image intensity gradients. –

I Current A

Im The image –

L Length m

LEL Relative area with low or non-EL signal %

P Power W

p Number of pixels –

R Ctskoefficient of determination –

R Resistance Ω

r50 The distance to where the intensity increases to 50 % of the maximum m

S Sobel operator for edge detection. –

Sharp The sharpness of an image –

SN R Signal-to-noise ratio –

T Temperature K

th Threshold –

U Voltage V

Subscripts 0 Saturation app Applied av Average

bg Background calibration image C Calibration

c Energy level of the conduction band

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cell Cell

D Degraded

d Diode

G Energy difference of the bandgap i Local cell position

m Module

mpp Max power point oc Open circuit p Shunt resistance peak Peak power

ph Photon

s Series resistance s The measured signal sc Short circuit

T Thermal

v Energy level of the valence band v Vignetting correction image x Kernel in horizontal direction y Kernel in vertikal direction

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CONTENTS

1 Introduction

With the unquestionable crisis that is global warming already ravaging across the planet, is there at the writing moment also a pandemic unfolding. Even this devastating combination has resulted in some surprising favorable effects. The pandemic has halted the whole engine of capitalism, the global consumption has decreased along with production. The result is plummeting greenhouse gas emissions, and a solid decrease in energy consumption. What’s fascinating is the decrease in energy production not affecting the production of renewable energy; the only resource where production increased (1).

The unquestionable crisis, global warming, is ravaging across the planet. On top of this is there, at the moment of writing, is also a pandemic unfolding. Amid the calamity of these two catastrophes, is there luckily some upsides in the industry og renewable energy. The pandemic has halted the whole engine of capitalism, and the global consumer consumption has diminished along with production in a matter of days. The result is plummeting greenhouse gas emissions and a substantial decrease in energy consumption. In turn, energy production decreased correspondingly except for renewable energy, the only resource where production increased (1). This rapid change directly contradicts the perception of many that change will take time. The fact that renewable energy output is mostly unaffected by demand proves that it is a favorable energy source and should experience a more significant investment and development.

Almost all energy sources, including most renewable energy, originates from the sun. Energy sources such as wind, hydro, coal, and oil are merely different means of solar energy storage or conversion. However, solar power, such as PV and thermal collectors, are the only means of directly harvesting solar energy.

As for any other energy production technology is the efficiency of the conversion essential. The average efficiency of PV, at 20-25 % (2), compared to that of many others, might seem low, but keep in mind that this is the amount of solar energy directly converted to applicable electric energy.

Other technologies are often dependant on harvesting from other energy sources that are arguably already converted solar energy. Take subterranean oil, originally from plants absorbing energy from the sun. The relative efficiency of oil, as a ratio of the solar energy initially harvested by plants and the output of the oil, would be meager in comparison to the efficiency of solar power.

Also, the PV efficiency does enable room for improvement and a continuous increase.

Degradation is a substantial cause of decreased efficiency as this occurs in different forms through-

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out the whole lifetime of PV devices. As such, several degradation detection techniques have been and are being developed, enabling a higher power production over the lifetime of the solar plant.

Moreover, there is still room for improvement of the efficiency, and electroluminescence is a strong candidate for taking the industry further.

Electroluminescence is widely utilized qualitatively, where low performing areas such as cracks, shunts, and dead areas are detected in images where they appear as areas with weaker signals or non at all. Therefore, electroluminescence provides clear indications of degradation in PV devices;

through grayscale images. However, these images also enable the possibility of quantitative image analysis. In order for the quantitative analysis to be possible, it is necessary to understand and prove the relationship between these image intensities and the output parameters of the PV devices. This thesis will seek to establish this relationship and provide the groundwork for establishing an electroluminescence imaging setup and a corresponding imaging procedure with the aim of quantitative degradation analysis and performance estimation.

Quantitative electroluminescence analysis is a relatively new technology. The earliest study found, to achieve a quantitative analysis of electroluminescence images, was performed by Breitenstein et al. (3) in 2010. This study found that the performance of solar cells could be estimated, to some extent, quantitatively from electroluminescence images. Furthermore, there have been many different methods proposed in recent years as one study by Bedrich et al. (4) demonstrated in 2018.

More specific quantitative crack detection had also been demonstrated by Stromer et al. (5) in 2019.

The electroluminescence imaging method presented here utilizes a modified commercially available camera. The images are corrected with standard procedures before analyzed with a rudimentary processing algorithm.

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2 PV

2.1 Principles

The working principle of photovoltaic (PV) devices is the photovoltaic effect. This effect describes the generation of charge through the absorption of energy from electromagnetic radiation. The PV device utilizes a potential difference, generated at the junction of two semiconductors, to push the charge generated through an external circuit. A simplified sketch of a PV-cell is presented in figure 2.1.

Figure 2.1. Schematic illustration of a cross-section of a cell, when illuminated. The photons, in orange, are converted to the energy of electron-hole pairs in blue. The junction in green separates the pair and pushes the electrons through the fingers and busbars, the silver lines and bars on top, to an outer circuit indicated with a voltmeter.

Figure 2.1 displays how the radiative energy of the photons, in orange, is converted to the chemical energy of electron-hole pairs, in blue, through the photovoltaic effect. A potential barrier, called the junction, separates the photogenerated charge carriers, the electrons, and the holes. This junction works as a diode where the electron-hole pairs will be separated because of a potential difference, the depletion layer in green. The interface between two differently doped semiconductors creates this depletion layer, which generates a potential difference between the two semiconductors, as

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demonstrated in green in figure 2.1. The depletion layer will separate and push the generated free electrons to an external circuit where they can perform work. They will then recombine with the holes at a back-sheet contact between the cell and the circuit.

Only the radiation with sufficient energy will be able to generate charge. Because the photon en- ergyE_phof the radiation needs to be above the bandgap energyE_Gof the semiconductor material in order to excite an electron into the conduction band where it is free to move. Described mathematically as

E_ph≥E_G (1a)

E_G=E_c−E_v (1b)

whereEGis the difference betweenEv, the valence bond or the initial energy level, andEc, the conduction bond, or the final energy level.

The valence bond and conduction bond of the whole semiconductor can be modeled as bands in figure 2.2. The shortest energy difference between the bands,E_G, is where the charge generation occurs. Ideally, the materials of the solar cell, the semiconductor, should be a direct bandgap, as displayed in figure 2.2a. Because the direct bandgap does not require a change in momentum~k, and therefore allows for a better absorption coefficient than the indirect bandgap of figure 2.2b. On the contrary, most solar cells consist of indirect bandgap semiconductors because of other factors, like cost.

E_g

E_c

Ev

~k

E

(a) Direct bandgap excitation.

Eg

Ec

Ev

~k

E

(b) Indirect bandgap excitation.

Figure 2.2. Bandgap diagrams for both direct and indirect bandgap. HereE_G is the difference betweenEv, the valence bond or the initial energy level, andEc. Incoming energy in orange is absorbed, and the electron in blue is excited to the valence bond.

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2 PV

The theoretical thermodynamic limit of the photovoltaic conversion, in silicone PV-devices, is 67 % (6) for non-concentrated sunlight. Additional losses are setting further limits on efficiency. These losses will be discussed later in subchapter 2.4.

2.2 The diode-model

A thin slice, called a wafer, of the semiconductor silicon (Si), is most commonly used to create these junctions. Figure 2.1 displays a wafer with two regions of different doping. The wafer, together with busbars and fingers creates a solar cell.

In order to analyze these cells, they are modeled as the electrical circuit of figure 2.3, called the one-diode model.

I

_ph

I

_d

R

_p

R

_s

U

Figure 2.3. Circuit presentation of a solar cell with the one-diode model. The current source in parallel with a diode represents the junction. The series resistanceRs and a parallel shunt resistanceR_prepresents the reaming parts of the cell .

In the one-diode model the cell is modeled as a current source (when light is incoming), in parallel with the solar cell diode. Additionally is the bulk of the cell, fingers, and busbars are presented as a parallel shunt resistance and a series resistance. These will be discussed in section 2.4.

2.3 Modules and arrays

Modules consisting of multiple cells is a means of generating sufficient power. The modules are constructed of series-connected cells in strings, and these strings are connected in series or parallel.

The one-diode model of cells are reduced to a simple diode when modeling whole module, as demonstrated in figure 2.4. A typical module consists of cells, bypass diodes, interconnectors, encapsulation, protective front- and back-sheet, frame, and a junction box. A typical solar plant consists of many arrays of series-connected modules.

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Figure 2.4. Circuit demonstration of cells (boxed diodes) forming a module, including bypass diodes (full diodes).

This thesis analyses small mini-modules (4-cells), developed for laboratory testing. The mini- modules are a representation of modules for building integrated applications (BIPV-modules, e.g.

in roofs or facades). BIPV modules typically have glass both as front and back-sheet to make them more durable as a building material, and they also often have less framing, in order to make them more adaptable to the integration surface. One of the modules tested are depicted in figure ..

together with the corresponding circuit demonstration.

Figure 2.5. Circuit demonstration of a four-cell mini-module.

2.4 Performance parameters and efficiency

In ordered to ensure the best performance of these arrays, are performance parameters estimated and utilized to calculate efficiency. Performance parameters are possible to derive from a single cell or a module as a whole. These parameters are derived from the current-voltage (IV) characteristics of the device under illumination. The IV-curve is measured voltage and current output under illumination, as presented in figure 2.6. In this thesis, light emittance of the cell, under a reverse bias current, will be analyzed and correlated with the IV characteristics.

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2 PV

Voc

Vmpp

I_sc I_mpp

P_peak

F F I

V

Figure 2.6. The general IV-curve of a PV-device is presented on the right-hand side. The param- eters discussed below are indicated in the plot. On the left hand side the corresponding revers IV-curve is presented; where power is supplied to the PV-device.

The parameters used to measure performance is:

• Ppeak: The peak power relates to the maximum power output estimated as the product of V_mppandI_mpp.

• Isc: Short circuit current. The maximum current output when there is no voltage drop in the circuit. It is a measure of charge generation in the device.

• Voc: Open circuit voltage in which no current flows through the external circuit and corresponds to the maximum voltage the cell can deliver. It is a measure of the amount of recombination in the device.

• F F: The fill factor is the ratio between theP_peakand the product ofV_ocandI_sc.

• η: Conversion efficiency is the ratio of generated power over incident power. Generally, this lies in the range of 20-25 % for Si cells; as of 2019 (2).

Efficiency is one of the key factors deciding the cost of solar energy, and therefore it is important to take all measures to achieve maximum efficiency. The improvement of efficiency is a continuous issue with many factors.

The biggest loss is the incident energy on the cells that are lost and will not contribute to electrons collected at the electrodes of the outer circuit. In fact, a study by Richter et al. (7) derives an upper efficiency limit of 29,43 % for Si solar cells. This limit consists not only of the thermodynamic limit but also internal and external factors (7).

One cause is optical losses as a result of reflection because of light arriving at an interface between

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air and cell. Parts of the incident energy are reflected, and thereby energy that could be converted into electric energy is lost (6).

There are multiple other factors, on top of the once contributing to the upper limit, limiting the efficiency even further. Two important ones are the material resistors in the cells. The series resistanceR_sand the shunt resistanceR_p. R_sis the resistance of the current path through the cell and consists of the material resistances of the wafers, contact resistance of the fingers and busbars, and their material resistance (6). TheR_p is the current leakage through the junction or at the cell edges caused by material defects. The leakages are modeled as shorting of the current circuit with a resistance and are therefore defined as such;R_p(6).

Cracks are also a substantial loss driver because the cells easily crack as they are only about 100 µm thick and made of Si. Cracking can, therefore, occur during the whole lifetime from production, through transportation and mounting, to stress on site. The effects on power production are, on average, 10 %, if the cracks create isolated cell areas that cause the area to be inactive or low performing (8).

The simplest methods of increasing total array efficiency are grouping modules by performance and filtering out defective modules at the factory. Both modules with production defects and modules with lifetime induced defects are crucial to interchange for working ones. The quantity of modules produced and installed make quantitative methods essential for efficient and effective filtering and grouping.

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3 ELECTROLUMINESCENCE IMAGES OF PV-MODULES

3 Electroluminescence images of PV-modules

A promising method of efficient quantitative analysis is electroluminescence (EL) of PV devices.

This is a well-established and widely utilized effect in many fields. It is also widely used in solar research and industry, but so far mostly in a qualitative fashion.

3.1 Radiative recombination

Radiative recombination is the foundation of EL and also widely utilized in LED’s. Solar cells or PV devices can also emit light under a reverse bias current. This happens when electrons are pushed into the conduction band of the n-type side of the junction. They will then recombine with holes in the valence band from the p-type side. Simply put is this the revers process of the electricity generation displayed in figure 2.1. Thus, a solar cell under revers bias current can be compared to a LED, electricity is converted to light instead of converting incoming light to electricity.

When the electrons falls to a lower energy band when it recombines, is excess energy released. The energy will mainly be converted to lattice vibration but also radiation energy in the form of light, or photons. The conversion to radiation is defined as EL.

Furthermore, EL of PV devices is closely connected to their power generation. This relationship is proven with the fact that the probability of the recombination excess energy being released as radiation, or EL signals, is equal to the probability of a photo generated carrier being collected at the fingers/busbar (9).

3.2 Electrical properties of EL

This relationship between the detectable emitted radiation, the luminescence, of and the applied voltage to the cell is generally defined as

φi =Cie^UT^Ui (2a)

U_T = kT

q (2b)

whereφ_iis the local luminescence at positioni,U_T is the thermal voltage defined by the temperature T, the Boltzmann’s constantkand the elementary charge q. Ui is the local junction voltage at positionion the cell of the PV device andC_iis a local calibration factor relating to the module and camera properties (3, 10).

This fundamental relationship that in principle enables analysis tools to calculate performance pa-

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rameters of the PV device from a simple image is widely understood and utilized by many (3, 9–12).

This thesis will focus on the applied currentI_appto the module, together with the average lumi- nescenceφ. Because the voltage is distributed differently over each cell in a module where the applied current is equal When considering a whole module, with cells in strings, will the voltage be distributed differently over each cell in a module where the applied current would equal. Therefore will this thesis analyze the applied currentI_appto the module, together with the average lumines- cenceφ. Only Hinken et. al (11) was found to mathematically define the relationship between φ andI_app, but it was only defined for the local positioniof a cell. The relationship was defined by modeling the cells asinumber of diode-model circuits. Whereicorresponds to the number of pixels in the EL-image. As displayed in figure 3.1 each circuit will consist of a local diode, local shunt resistanceRp,iand local series resistanceRs,i. The diode will have a local reverse saturation currentI_0,iand the voltage dropU_i.

I_i R_s,i

U_i I_d,i

Rp,i U_cell

Figure 3.1. Diode model of the device under reverse bias current. The difference from figure 2.3 is the junction modeled as an LED.

The local diode is modeled with the diode equation for an ideal diode.

I_d,i =I0,ie^UT^Ui (3) Combining equation 3 with equation 2a yields

I_d,i = I_0,i

C_i φ_i (4)

This equation only accounts for the local diode current, when the applied current is the one of interest. However, it’s safe to assume thatI_app would be a sum of all the localI_i when R_p,i is infinitely large causing the current through to be negligible. When by Kirchhoff’s Current Law is

Ii =Id,i (5)

and

I_app=

n

X

i

I_i if R_p,i=∞ (6)

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order to account for all.

I_app= P_n

i C_2,iφ_i

n (7a)

C2,i= I0,i

Ci

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3.3 EL signal sensitivity of camera sensors

The wavelength of the emitted signal from the PV device through EL corresponds to the band gap of the material. For Si modules this band gap energy approximately corresponds to 1,1 eV or a wavelength of 1128 nm. This is the approximate energy of the photons of the emitted signal, which will have a slight variation corresponding to a distribution from 900 nm to 1300 nm as plotted in orange in figure 3.2 (13).

Common camera sensors based on Si CMOS, Si CCD or InGaAs photodiodes are sensitive for different ranges of this interval. Their approximate sensitivity area is demonstrated in figure 3.2.

Because the sensitivity area of the photodiodes overlaps with the EL signal distribution, is it possible to utilize these types of devices to detect EL signal emitted from the Si PV-device. The InGaAs sensor has the best spectral response in the range of interest, but the most commonly used camera sensor is the CCD. This is because of the low cost of the CCD and a sufficient response to the EL wavelength distribution. CMOS is even cheaper than the CCD, but it also has a slightly lower response of the EL spectra. In this thesis a CMOS camera has been chosen on the grounds of the lower cost not influencing the image quality compared to a CCD (14).

500 600 700 800 900 1,000 1,100 1,200 1,300 20

40 60 80 100

nm

% ^CCD

CMOS InGaAs EL

Figure 3.2. Quantum efficiency of common camera sensors (CMOS, CCD, InGaAs) in comparison to a typical EL emission spectrum of a c-Si cell (orange) (12, 13, 15–17).

It’s also important that the sensor collects the signal linearly. This will enable comparison of results

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from different exposure times because the intensities can easily be scaled such that they are time independent. In addition is it a fundamental precondition for the correlation of intensity and performance parameters. Therefore, the camera utilized in this thesis are tested for linearly collection of signal as discussed in chapter B.1a.

The camera sensor is one of the fundamental parts of establishing a quantitative analysis method with EL. It will serve as the link between the PV device and the analysis tool or the algorithm.

3.4 Imaging interface

A commercially available Nikon D850 camera is utilized as an EL-detector in this thesis. This camera is assumed to be linear as found by Darrodi et al. (18). The IR-filter of the camera is interchanged with an 850 nm long-pass filter in order for this commercially available camera to work as an EL-detector. The camera calibration discussed in chapter 4.2 is performed every time the settings are changed. Along with this camera is the EL imaging setup in figure 3.3 is utilized.

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Figure 3.3. The EL imaging setup with a module mount in silver, computer controlled camera, power supply, current and voltage measurement. The light is turn of in the windowless room in order to perform the EL imaging.

The modules IV-characteristics is firstly measured with the sun simulator depicted in 3.4. This Spire Solar Simulator illuminates the modules with given irradiance and measures their performance parameters.

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Figure 3.4. The Spire Solar Simulator utilized to measure the IV-characteristics of the modules.

This device simulates standard test conditions with different irradiance intensities possible. In this thesis is 1000 W/m²and 200 W/m²is used.

A custom-made software written in LabVIEW is utilized to make an interface between the camera, computer and power supply of figure 3.6. It enables all the components in figure 3.6 to be controlled from the same console. Her the sample ID number, distance, focus and aperture is specified.

Exposure time, output current and voltage limit is set. All these parameters are stored in a separate text file generated for each picture taken.

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Figure 3.5. Interface of the custom-built control software.Her the sample ID number, distance, focus and aperture is specified. Exposure time, output current and voltage limit is set, and images are taken.

A circuit diagram of the setup is presented in figure 3.6.

Computer I_app

A

Camera

VV

Figure 3.6. Circuit diagram of the interface between all lab components.

Here the Ampere- and voltmeters are connected in 4-point setup to minimize resistance noise.

The images taken with the interface presented her are corrected as discussed in chapter 4.3 as to performer an accurate analysis as discussed in chapter 5.

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4 Image calibration and correction

Calibration of camera and correction of images is essential for efficient quantitative image analysis.

This section will describe how to optimize image quality and correction of camera dependent distortions following the procedure described in the IEC-DTS 60904-13 technical specification draft

“Electroluminescence of photovoltaic modules” (IEC 60904) (19).

4.1 Software

The image analysis software, dataArtist, developed by K. Bedrich was utilized for camera calibration and image correction. This is a free, open source software available at gitHub. In dataArtist a camera calibration file “.cal” is created. The calibration file stores all the image corrections such that they can be applied to all images automatically when uploaded to the software (12).

The software is not able to perform further analysis of the intensities because the estimation of a ridged cell estimation based on a pattern-recognition-algorithm that was not able to identify the cells properly. This resulted in major errors and therefore algorithms were developed in order to perform further analysis as describe in section 5.

4.2 Optimization of camera settings

The first step to ensure optimal image quality is proper camera settings. This includes a verification of the linearity discussed in 3.3, the manufacturer settings and the optimal focus setting. The latter is the one setting that is possible to adjust and quantify objectively.

4.2.1 Linearity test

The linearity of the camera is verified by imaging the module setup without a module; the background. The background is imaged at different exposure times. Preferably, the exposure time where the sensor is saturated and down to at least one tenth of this. Then the intensities are plotted in order to analyse the assumed linearity.

4.2.2 Camera focus

Most cameras have a manually focused lens where the best focus is subjectively estimated from comparison of different focus levels. However, it is possible to quantify the focus level by utilizing the Tenengrad function on different images at different focus. The Tenengrad quantifies the level

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4 IMAGE CALIBRATION AND CORRECTION

focus or the blur of an image and is defined by the ICE-DTS as follows

T enengrad(Im) =mean(G²_x+G²_y) (8a)

G_y =S_y⊗Im (8b)

G_x =S_x⊗Im (8c)

whereImis the image,G_x/y are the image intensity gradients obtained from convolution of an image with the Sobel operator in x and y directionS_x/y(20). The Sobel operator is an edge detection filter and provides the magnitude of the derivative or the image intensity gradients. Simply put does the Tenengrad quantify the average variance of the image edges, and therefore the greater the Tenengrad value the sharper the image (19).

The Tenengrad is implemented as an algorithm that calculates the level of focus for multiple images.

This makes it possible to plot the Tenengrad values as a function of focus settings as depicted in figure 4.1. By analyzing this plot, it is possible to determine the optimal focus at the Tenengrad maximum (12, 19).

(a) EL image take with the fo- cus level at 1.6.

(b) EL image taken with the focus level at 1.7.

(c) EL image taken with the focus level at 1.85

Figure 4.1. Three EL images utilized to identify the optimal focus. The images are taken in series with three different focus levels, then the Tenengrad of each image is estimated.

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1.6 1.65 1.7 1.75 1.8 1.85 150

200 250 300 350

Focus

Tenengrad

Figure 4.2. Estimated Tenengrad of the three images in figure 4.1 with the different focus of 1.6, 1.7 and 1.85 respectively.

The Tenengrad only measures the focus of the image and is not a direct measurement of the absolute image sharpness. Though focus relates to the image sharpens in a way that only objects in focus can be sharp, but the sharpness is defined by the camera sensors resolution and pixel per area.

4.2.3 Image sharpness

However, other methods can be used to quantify the absolute sharpness of the image. The sharpness is used to determine the resolvable object size, or the level of blur, of an image. Sharpness is also used to measure position uncertainty, sharpen images and to compare image qualities. Quantitative determination of sharpness is important for fast and effective analysis (12).

The IEC 60904 suggests utilizing the so called “V-cut” method to quantify the sharpnessSharp.

This is an uncomplicated method without the need for processing software. The module is masked with opaque tape on top of aluminum foil to create a v-shaped edge as depicted in figure 4.3 (19).

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(a) The EL image of the masked PV cell. The green box indicates region where the sharpness is estimated.

(b) The masked PV cell generating the EL imagef and used for estimation of sharpness.

Figure 4.3. The masked PC cell together with its EL image.

This mask is then utilized to calculate the sharpness with the equation

Sharp= 0.5·α·r50 (9a) α=arccos

L²₁−L²₂+L²₃ 2·L1·L3

(9b) whereα is the angel in figure 4.4a,r₅₀is the distance from the intersection of the linesL₁ and L3 in figure 4.4a along the lineL2 in figure 4.4b to where the intensity increases to 50 % of the maximum. The actual measured edges are displayed in figure 4.5, and figure 4.3 shows the actual PV module.

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L₁

L₃ α

(a) The edges created with the tape mask

L2

r₅₀

50%

x φ

(b) The mask with intensity plot of L₂ and the measuredr₅₀

Figure 4.4. Illustrations of the V-cut method based on the figure in (12)

Figure 4.5. The ROI of the masked PV cell used for estimation of sharpness; tilted 90°counter clockwise.

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4.3 Correction of images to optimize quality

After optimization of camera settings, image will correction increase image quality further. Image correction of camera distortions is also important in order to secure image comparability with other setups. Three main steps to correct images are presented in this section as describe in the IEC 60904 (19).

4.3.1 Background correction

Firstly, background noise is reduced. Bedrich, the IEC 60904, Hinken et al. and Potthoff et al.

(3, 11, 12, 19) refer to background noise as dark current/signal. The terms dark signal/current of optical sensors implies that the sensor reads a signal even without there being any actual signal. The target of the background calibration is to reduce the influence of not only this dark signal/current, but also environmental stray- and reflected EL light, all of which can be defined as background noise. Therefore the term background calibration is utilized in this thesis (3, 11, 12, 19).

The background noise will alter what the camera sensor detects as an EL signal, as it will add to the pixel intensities. In the IEC 60904 a background image of an unbiased device is subtracted in order to minimize this effect. A background image is depicted in figure 4.6. The subtraction of such background images will, however, add another noise level to the EL image. Instead, the Bilateral filter (21) is applied to the EL image as a means of noise reduction. The Bilateral filter is a smoothing filter based on the Gaussian filter. The Gaussian filter calculates the weighted averaged of a specified kernel in order to preserve edges, and the Bilateral filter is even better suited to preserve the edges. The Bilateral filter was chosen because of its ability to reduce the noise level of the image while it preserve edges, as documented by Tomasi C et al. (21). A comparison between the unfiltered and filtered image is presented in figure 4.7.

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Figure 4.6. A background image taken of an unbiased PV device with an exposure time of10s.

(a) A small region of a calibration image of non- degraded module.

(b) A small region of a smoothed calibration im- age with the bilateral filter.

Figure 4.7. Comparison of the calibration image and the bilateral filtered image. This demon-

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4.3.2 Signal to noise ratio

One way of measuring the effects of background image calibration is calculating the signal-to-noise ratio (SNR). Bedrich (12) defines the SNR generally as

SN R= Im_s−Im_bg Imn

(10) where theIm_sis the measured signal,Im_bg is its background andIm_nthe noise (12).

The IEC 60904 recommends that the SNR is calculated from two background excluded EL images taken in series, and a background image taken at the same exposure time. The quantification of this specific SNR estimation method is performed with the formula,

SN R=

P

k(^(I¹^(k)+I₂ ²^(k))−I_bg(k)) P

k( q1

2 · |I₁(k)−I2(k)| ·(_π²)^−0.5)

(11) where the ratio i calculated as a sum of the pixelsk. To create the noise reduced imageS−Bg, the average of the two noisy equivalent EL imagesIm₁andIm₂ are reduced by the background imageImbg. The noise levelN is derived from the root mean square deviation ofIm1andIm2, scaled with a noise factor

q1 2. 4.3.3 Region of interest

The IEC 60904 requires anSN R≥45, for lab measurements. However, this procedure is region of interest (ROI) sensitive, meaning that the SNR will vary based on how much of the region outside of the module that is included. Implemented on the two different ROI’s in figure 4.8 generates two different SNR values. ROI-1 results in an SNR of 20.4 and the ROI-2 results in an SNR of 44.0

Figure 4.8. A picture of a PV module with two different ROI sizes.

The SN R will therefore be quantified for ROI’s of images that only include the PV module as demonstrated in figure 4.8. This is achieved with the perspective rectification function in dataArist.

This function is performed to align the PV device within the image where its perspective is corrected

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with a manually made pattern for detecting module and cell boundaries as demonstrated in figure 4.9 (4).

(a) The blue lines with a red bounding box indi- cates a manually made pattern of the PV modules edges and busbars. This pattern is used for per- spective correction.

(b) The perspective corrected image used as ref- erence image.

Figure 4.9. Comparison of the image before and after perspective correction.

4.3.4 Exposure time

SNR is an important parameter for estimation of the optimal exposure time. Longer exposure time will increase the EL signal, but it will also increase noise. With SNR comparison is it possible to determine the shortest exposure time which provides a sufficient EL signal and the lowest noise level. According to the ICE DTS the optimal exposure time would be the shortest exposure time that provides images withSN R≥45.

4.3.5 Vignetting

Vignetting is defined as a fall in the intensity of the pixels towards the edge of the image. This fall in intensity is caused by partial obstruction of the illumination of the sensor. This obstruction occurs due to shading from the camera lens of oblique illumination (12, 19, 22).

To correct this distortion a flat field image is created with the method ”DIRECT MEASUREMENT AT SHORT DISTANCE WITH SPATIAL INHOMOGENEITY CORRECTION” described by Bedrich (12). This is a method in line with the IEC 60904. The method describes the calculation of

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maximum intensity of the average image (12).

The images for averaging are taken by placing the camera directly in front of the device, with no gap between them. Then the device is imaged at slightly different positions and rotations. An example of the resultingImvis presented in figure 4.10. The EL-image pixels are then divided by the corresponding pixels of theIm_v to correct the vignetting effect.

Figure 4.10. The vignetting correction imageIv. The image is a normalized average of 15 images taken directly in front of the fully illuminated PV device.

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5 Analysis

EL is a relatively new analysis method for solar modules, but it has already been correlated with many different performance parameters, all with different degrees of success. Still, as mentioned in section 3.2, there is uncertainty connected to the correlation between the luminescence of the PV device and its performance. It still remains necessary to fundamentally correlate EL with the general performance of PV devices (4, 9–11, 23–27).

5.1 Preprocessing

In this thesis different software are utilized as means of achieving an efficient image correction method. Still there are shortcomings of the software’s ability to perform image analysis, and so algorithms were developed for this purpose. For both the software and the algorithms is also it necessary to convert the NEF image format, the RAW format of Nikon cameras, to other more applicable format which can cause data loss.

One fundamental algorithm that needs to be implemented to achieve accurate results is a preprocessing algorithm with the purpose of identifying of the ROI in the image. In this algorithm the target will be to exclude background, busbars and fingers from the image analysis. The preprocessing algorithm (ROIa) developed in this study is based on the preprocessing procedure described in the study about crack segmentation by Stromer et al. (5) and the corresponding developed algorithm enhanced crack segmentation (eCS). Here a binary ROI is calculated that only includes the cell area as depicted in 5.1b (5).

In order to identify the cell edges and busbars properly the Bilateral filter is first applied. This filter reduces noise and preserves edges which increases the accuracy of the edge detection. The result of the bilateral filter is presented in figure 4.7.

Then the binary ROI can be estimated. This is achieved in ROIa with two image operations. First is the Otsu method for image thresholding utilized to exclude background, edges and busbars. The ROI from an Otsu threshold in figure 5.1a tend to only identify parts of the edges and busbars.

Therefore morphology filters are utilized to enhance identification of the cells edges and busbars further and achieve a more accurate ROI as depicted in figure 5.1b.

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5 ANALYSIS

(a) The estimated ROI with Otsu thresholding.

(b) The estimated ROI with Otsu thresholding and morphological filters.

Figure 5.1. Comparison of ROI calculated with Otsu and enhanced with morphology filters.

5.2 Correlation estimation

This thesis proposes to correlate the EL, as image intensity averages, with one of the most fundamental performance parameters, namely the moduleI_sc. The correlation between the two is a reasonable assumption based on equation 7a; derived in section 3.2.

In this experiment a Spire Solar Simulator displayed in figure 3.4 is utilized to measure theI_sc. Then the same current is applied in reverse directionIappto the PV module such that EL images can be taken, and the signal intensity level can be measured. After image correction, is the module intensity average calculated on the ROI estimated by the preprocessing algorithm ROIa. A regression plot between the EL signal intensity averages and the applied current is then estimated.

Analyzing this plot and calculating the R-squared will provide grounds for determining the correlation between the luminescence and theI_sc.

In this thesis onlyIscat two irradiance levels where measured. One at 1000 W/m²,I_sc¹, and one at 200 W/m²,I_sc² I_sc². EL images on the other hand was take of the PV devices withI_appapproximately equal to 10 %, 20 %, 40 %, 60 %, 80 % and 100 % ofI_sc.

The accuracy of the power supply is verified by plotting the measured applied voltage by the loga- rithm of the measured applied current. These are measured independently of the power supply with the voltmeter and ammeter in figure 3.6. This should provide a linear plot as opposed to the plot in figure 2.6; where the exponential relationship of the two is presented.

In order to achieve sufficient SNR, the EL images of the modules were taken with exposure times

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at 10, 8, 6, 4, 2, 1srespectively. The images are still comparable because of the assumed linear response of the camera sensor. This means that different exposure times can be compared by scaling the intensities. With the exposure time ofI_sc¹ at 1sas reference, the scaling factor of the EL images at for example 10 % ofI_sc¹ would be ₁₀¹.

5.3 Histogram Analysis

Cell areas with defects like shunts and cracks will obstruct the applied current flow and alter the relationship between applied current and EL signal. As such these areas demonstrated in figure 5.2, will have lower or no EL signal and appear as inactive or low performing areas in the EL images.

These areas are there for detectable with image analysis.

(a) Module with a clear shunt as a dark spot. (b) Module with a clear crack as a dark line.

Figure 5.2. Pictures of modules with common defects.

One such image analysis is the IEC 60904 procedure ”Quantifying Solar Cell Cracks in Photovoltaic Modules”. This procedure describes the analysis of the pixel intensity distribution, in the form of a histogram, of the images. The procedure performs an estimation of the relative ”degraded module area” (D) defined as the percentage of the cell area with low or non-EL signal (LEL) relative to that of a calibration module or a non-degraded module.

D=LEL_D−LEL_C (12)

TheLEL_Cof the calibration module is estimated with LEL=

Pth i=0bi

p (13)

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5 ANALYSIS

threshold as they are classified as low. Dividing this sum with the number of pixelspin the ROI gives the percentage of degraded areaLEL_C. Then the same threshold is utilized on the degraded module to calculateLEL_D. Subtracting these two will provide the actual degraded areas (19).

The images are preprocessed in order to achieve a more accurate estimation ofD. The preprocessing will exclude the background, cell edges and busbars from the histogram analysis such that they are not classified asLEL. Finally, the histogram analysis can be performed on the emitting cell area.

5.4 Procedure

Combining the imaging methods discussed in chapters 4-5 creates the following procedure utilized in this thesis.

Calibration of camera:

1. Focus is measured by taking multiple pictures at different focus settings. A graph is generated in dataArtist to clearly indicate the optimal focus (figure 4.1).

2. Sharpness is determined with the v-cut method.

3. TheImbg correction function in dataArtist generated a background correction image to be subtracted from EL images.

4. Vignetting is calculated as described in subsection 4.2. dataArtist generates a correction as presented in figure 4.10.

5. SNR is measured by applying the IEC 60904 defined function embedded in dataArtist as described in subsection 4.2. The resulting value determines the performance of the lab.

With sufficient SNR, EL images are corrected and analyzed:

1. Im_bg is subtracted from the image.

2. Vignetting is corrected.

3. ROI is estimated with developed algorithm ROIa.

4. Average module intensity is calculated on the ROI.

5. Correlation between EL images and LIV-characteristics are estimated by applying regression estimation for intensity averages and applied current.

6. The IEC 60904 procedure ”Quantifying Solar Cell Cracks in Photovoltaic Modules” is applied with developed algorithm.

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6 Results

The correlation estimation along with the quantitative degradation analysis procedure presented in the section 5.4 provided the results presented here. In the correlation estimation where four modules analyzed consists of two non-degraded and two with cracks and shunts as figure 6.1 depicts. This provides grounds for determining if there is a difference in correlation between the two types of modules.

(a) Module 601871 as representing non- degraded module.

(b) Module 601872 representing a degraded module.

Figure 6.1. Example of the difference between non- and degraded modules analyzed.

6.1 EL intensity and IV-characteristics

The correlation between theI_sc and the EL signal is estimated, as described in section 5.2, by plotting the scaled average EL intensities withIappand performing a regression estimation.

Moreover, the results are verified with the linearity test of the camera, the DS identification and the accuracy test of the power supply as discussed in chapter 4.2.1, 4.3.1 and 5.2 respectively. The result are presented i figure 6.2. The error control is perform with images imported through the software and directly from the camera to identify any error in the software as discussed in chapter 3.4.

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6 RESULTS

0 2 4 6 8

100 120 140 160 180 200 220

t [s]

Signal[counts]

Intensities of bg trough software Intensities of bg from camera

(a) The linearity test with the image intensities of the background image at different exposure times. The y-axis represents the signal or the im- age intensities and the x-axis represents the cor- responding exposure times.

2 4 6 8 10

14 16 18

t [s]

EL[counts]

Intensities of DS trough software Intensities of DS from camera

(b) The image intensities of the DS at different exposure times. The y-axis represents the signal or the image intensities and the x-axis represents the corresponding exposure times.

2.5 3 3.5 4 4.5

2 4 6 8

Voltage [V]

Current[A]

(c) The measured applied current and corre- sponding voltage at the same levels utilized in the correlation estimation. These are data points measured independently of the power supply, with a volt- and ammeter.

Figure 6.2. Three error tests. On the upper left, the result of the linearity test of the camera sensor.

On the upper right, the DS identification. On the bottom, the result of the accuracy test of the power supply.

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Contradicting the theoretical linear correlation of equation 7a, the data points indicated an exponential trend. Therefore, an exponential regression was performed. The results of the regression plot, with a better suited exponential correlation, are displayed in the regression plot in figure 6.3. The resulting average R squared was estimated atR¯ =0.988, as an average of all four regression plots.

The R squared supplies further confidence of an exponential correlation, with estimated values for each module presented in table 1.

0 2 4 6 8

0 50 100

I [A]

EL[counts/s]

Regression plot of each module

Figure 6.3. Point plot of the EL signal fromIappand the estimated exponential regression of these data points. The different colors represent the4different modules analyzed.

Table 1. R squared of the exponential correlation betweenIm_appand EL signal for both non- and degraded modules.

Module ID R

601 867 0.989

601 870 0.988

601 871 0.988

601 872 0.988

6.2 Performance thresholds

Development of analysis algorithms is explored in many studies like Stromer et al. (5) who devel-

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6 RESULTS

current loss of -0.9 A/cm from cracks. This is a result that is not representative for the modules in this study as presented in figure 6.4 (5, 25).

1 2 3 4 5 6 7 8 910111213141516 9.06

9.08 9.1 9.12 9.14 9.16

Modules Isc[A]

Normal modules Degraded modules

(a)I_sc¹ of modules, with degraded modules indi- cated in red.

1 2 3 4 5 6 7 8 910111213141516 1.82

1.83 1.84

Modules Isc[A]

Normal modules Degraded modules

(b)I_sc² of modules, with degraded modules indi- cated in red.

Figure 6.4. Plots ofIscfrom irradiance at 1000 W/m²and 200 W/m². The modules with cracked and shunted cells are indicated in red.

This thesis utilized the IEC 60904 histogram analysis for a case study of degradation detection as mentioned in section 5.3. Including quantification of degradation from cracks. The result is displayed as a plot ofIsc,d and D in figure 6.5 and a graphic example of the first estimatin is dispalyed in figure 6.6.

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10 20 30 40 50 60 70 80 90 100 0

20 40 60 80 100

Degraded area [%]

DecraesainIsc[%]

I_sc,dat 1000W andDat 9A I_sc,dat 200W andDat 1,8A

Figure 6.5. Estimated degraded area at two differentI_app, 9 A and 1,8 A, plot- ted against relative decrease inIsc of the corresponding illumination intensi- ties, 200 W and 1000 W respectively. All data points are relative to perfor- mance of the module before degradation. The degradation is estimated with the morphological-based ROI and the IEC 60904 histogram analysis. I_sc is analysed with the Spire Solar Simulator.

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6 RESULTS

Figure 6.6. The image with the second degree of degradation, where degra- dation estimation of the module is indicated. The pixels that are classified as degraded are depicted in red in the image analyzed.

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7 Discussion

7.1 Correlation of intensity and current.

The results in section 6.1 indicate a correlation betweenI_appand the image intensity average.I_app is closely related toIsc, as proven by Kirchartz T (9), and the image intensity average is closely related to the EL signal. Therefore, do the results indicate a correlation betweenI_sc and the EL signal.

Visual inspection highlights an evident exponential (not linear) trend, as in the data points. There- fore, exponential regression was performed. The R squared values further substantiates this assumption. The estimated exponential correlation in figure 6.3 appears not to be an error for the camera sensors linearity, as established by figure 6.2a. Likewise, it is not a result of setup errors as figure 6.2b and 6.2c substantiate. However, does the plot in figure 6.2a indicate a mismatch between the increase of exposure time and EL-counts. At an 8-fold exposure time is only a dou- bling of EL-counts observed. The mismatch does not explain the exponential trend, but it is a clear indication of some unexpected behavior of the camera influencing the results. It is also important to point out that the y-axis of the IV-curve in figure 6.2c is not, in fact, logarithmic. The reason being an already linear trend caused by the high current levels.

Moreover, it is reasonable to assume there could be an exponential correlation, and that some module factor is the cause. One possibility, for example, is that theR_pis not negligible. This assumption does not necessarily contradict the theoretical correlation of equation 7a since the definition ofCi

is unknown. It is feasible thatC_iis an exponential factor, proven when deriving the full expression of the relationship betweenIappandφ. The full expression, therefore, needs to be defined mathematically. It is important to note that the DS, in figure 6.2b, does not explain the exponential trend as it appears independent of exposure time. However, the indicated exponential correlation of these results might be an indication of what to expect when deriving the full expression ofC_m.

It is clear that either way, it will be necessary to perform the experiment multiple times on the same and other modules. Only then can it be conclusively determined if it is an exponential trend or if it is only due to errors.

Notably, it cannot be concluded that the correlation holds for all degrees of degradation, as it might break down at some threshold when the degraded percentage area of the module increases.

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7 DISCUSSION

7.2 Accuracy of feature detection

The degraded modules included in the correlation estimation show no clear sign of the identified cracks affecting the EL signal. There is no significant deviation of the different module’s regression lines in figure 6.3. Furthermore, by comparing the cracks studied by Mansouri et al. (25) and the ones studied here, is it evident that there is no direct comparison possible. Therefore, the conclusion of -0.9 A/cm does not hold for all crack types.

By contrast, results from the case study of degradation estimation in section 6.2 provide some indication of a connection between degraded areas and power loss. The blue data points, of 1000 W illumination, indicate a linear trend that could imply the degradation estimation predicting with the power loss. However, the data points do not provide grounds for a conclusion because there are insufficient numbers. More data points were not possible because the level of degeneration was achieved through blunt force, which made it difficult to generate sufficient and accurate levels of degradation. A more controlled degradation process through illumination and thermal stress like a climate chamber would be preferable. The green data points, of 200 W illumination, provide grounds for concluding that there is no correlation, contradicting the blue. However, it is not likely that theI_scwas barely affected at 200 W as the whole module was shattered. Also, the blue data series measured a decrease in theIsc of over 100 % at the highest level of degradation. Here it is measured a reversI_scindicating there is some error in the measurement. The reason might be the blunt force altering the interconnectors and such of the module. Furthermore, a visual inspection of figure 6.6 makes clear that the degradation estimate is overestimating, including edges and busbars that are not degraded areas. The degradation estimate is probably influenced by imaging and processing errors.

Therefore, one might conclude that minimizing these errors with a more controlled degradation process and applying a more advanced analysis algorithm could provide a correlation between the two.

It is also essential to perform this analysis multiple times on multiple modules, as aforementioned.

7.3 Comparability with standard PV-modules

The BIPV modules analyzed in this thesis are not commercially produced, but they follow the same standard. The results will not be directly comparable with commercially available alternatives, but it is reasonable to expect the same trends. The same goes for comparing the result from these BIPV modules with standard modules. They are just a different configuration of the same cell types and, consequently, one would expect the same results. The main goal of this thesis has been to estab-

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lish the necessary procedures and tools for obtaining quantitative information from EL imaging, applicable also to other kinds and sizes of PV modules.

7.4 Image quality

The most substantial errors affecting the results originate from EL imaging errors, which affect the EL signal of images. These errors are caused by factors like background noise, camera defects, and image format losses and will alter what the final result is. To pinpoint exactly where the problems are, and avoid them as far as possible would be the preferred way to minimize these errors. An example would be to perform the EL imaging in an opaque room in order to minimize background noise. Another is creating an algorithm able to process NEF image formats directly in order to minimize the loss from image format conversion. Performing the analysis multiple times and with multiple modules would also be advantageous, as this also would decrease the error.

Development of electroluminescence imaging for quantitative image analysis of photovoltaic modules

Development of

electroluminescence imaging for quantitative image analysis of photovoltaic modules

Jonas Rydtun Winsvold

Acknowledgements

Abstract

Nomenclature

Contents

1 Introduction

2 PV

I

I

I

R

R

U

3 Electroluminescence images of PV-modules

4 Image calibration and correction

1.6 1.65 1.7 1.75 1.8 1.85 150

200 250 300 350

Focus

Tenengrad

5 Analysis

6 Results

7 Discussion