Lifetime and Degradation Science Of

LIFETIME AND DEGRADATION SCIENCE OF POLYMERIC ENCAPSULANT IN PHOTOVOLTAIC SYSTEMS: INVESTIGATING THE ROLE OF ETHYLENE VINYL ACETATE IN PHOTOVOLTAIC MODULE PERFORMANCE LOSS WITH SEMI-GSEM ANALYTICS

NICHOLAS R. WHEELER

Submitted in partial fulﬁllment of the requirements

For the degree of Doctor of Philosophy

Department of Macromolecular Science and Engineering

CASE WESTERN RESERVE UNIVERSITY

January, 2017 Case Western Reserve University We hereby approve the thesis document1 of

NICHOLAS R. WHEELER

for the degree of

Doctor of Philosophy

Dr. Roger H. French

Committee Chair, Adviser Date Department of Materials Science and Engineering Dr. Ozan Akkus

Committee Member, Faculty Date Department of Mechanical and Aerospace Engineering Dr. Michael Hore

Committee Member, Faculty Date Department of Macromolecular Science and Engineering Dr. Timothy J. Peshek

Committee Member, Faculty Date Department of Materials Science and Engineering Dr. Laura S. Bruckman

Committee Member, Faculty Date Department of Materials Science and Engineering

Defense Date: August 25, 2016

1We certify that written approval has been obtained for any proprietary material contained therein. Table of Contents

List of Figuresv

Acknowledgements ix

Abstractx

Chapter 1. Introduction1

World Energy Demands1

Lifetime and Degradation Science for PV2

Chapter 2. Literature Review4

Carrisa Plains Disaster4

Subsequent PV Studies6

Modern PV Module Degradation Perspective7

Confocal Raman Spectroscopy for PV Materials9

EL Images for Localized PV Performance 11

I-V Curves for System Level PV Performance 13

Chapter 3. Experimental Methods 15

Mini-Module Samples 15

Exposure Equipment and Procedures 18

Evaluation Equipment and Procedures 19

Data Analytics & Environment 24

Statistical Methods 32

iii Chapter 4. Experimental Results 36

UL PV Module Study - Degradation Pathway Modeling 36

SDLE Mini-Modules Pilot Study 43

Screen Printed Silver (SP-Ag) Corrosion L&DS Dataset 45

Chapter 5. Discussion & semi-gSEM Modeling 52

Hypothetical Degradation Mechanisms 52

Interpretation of Variable Trends 54

Exploratory Data Analysis - Pairwise Plotting 66

Semi-gSEM Modeling of Degradation Pathways 72

Domain Knowledge - Observed Causal Mechanisms 82

Sample Construction Geometry Comparison 85

PV Module Change Point Phenomenon 86

Chapter 6. Conclusions 88

Chapter 7. Future Research 90

Appendix A. CV 92

0. Presentations 92

0. Publications - Proceedings 93

0. Publications - Refereed 94

Appendix B. Free and Open Source Software Tools 95

Analytical software packages 95

Preparation of this document 96

Appendix. Complete References 97

iv List of Figures

1.1 Comparative Nameplate Capacity Chart from DOE Report2

1.2 Mesoscopic Evolution Model3

2.1 Carrisa Plains Power Plant Performance Degradation5

2.2 Raman Spectroscopy of EVA in Literature9

2.3 Test Figure for EL Images 11

2.4 Test Figure for I-V Curves 13

3.1 SDLE Mini-Module Construction 16

3.2 DPVS Mini-Module Gridline Geometry 17

3.3 Environmental Test Chambers 18

3.4 Confocal Raman SNR Study 19

3.5 Confocal Raman Procedure 20

3.6 Gage R&R 21

3.7 EL Imaging Procedure 22

3.8 I-V Curve Tracing Procedure 23

3.9 Computational Environment - FOSS Tools 24

3.10 Confocal Raman Analytics 25

3.11 EL Image Processing 26

3.12 EL Image Quantiﬁcation 27

3.13 I-V Curve Tracing Analytics 28

3.14 I-V Curve Calibration 29

v 3.15 Delta Format 30

3.16 Data Pipeline 31

3.17 Pairwise Plotting 32

4.1 L&DS Model Needs and SEM Technique 36

4.2 UL PV Module Dataset Experimental Summary 38

4.3 UL Study Changepoint 39

4.4 UL PV Module Dataset Semi-gSEM Results 41

4.5 Confocal Raman Pilot Study 43

4.6 EL & I-V Pilot Study 44

4.7 Confocal Raman Cell Edge CHb/CO Peak Ratio 46

4.8 Confocal Raman Cell Center CHb/CO Peak Ratio 46

4.9 Confocal Raman Cell Edge Degradation Peak Ratios 47

4.10 Confocal Raman Cell Center Degradation Peak Ratios 47

4.11 EL Image Pixel Sums 48

4.12 EL Mean Threshold Sum Ratios 48

4.13 EL Mean Threshold Count Ratios 49

4.14 EL Area Ratios 49

4.15 I-V PMax Results 50

4.16 I-V Fill Factor Results 50

4.17 I-V Corrected PMax Results 51

5.1 Spectral Shape Discussion 54

vi 5.2 EVA CO Peak Discussion 55

5.3 Degradation Feature Discussion 56

5.4 Polymer Luminescence Discussion 57

5.5 Glass Peak Discussion 58

5.6 Glass Peak Discussion 59

5.7 Edge vs Center and Agg vs NAgg Comparison 60

5.8 Image Pixel Sum Discussion 61

5.9 Image Threshhold Variables Discussion 62

5.10 Area Sum Ratio Discussion 63

5.11 EL Gridline Geometry Discussion 63

5.12 PMax Discussion 64

5.13 Fill Factor Discussion 65

5.14 Confocal Raman CHb/CO Ratios Pairwise Correlaton 66

5.15 Confocal Raman Degradation Ratios Pairwise Correlaton 67

5.16 EL Pairwise Correlaton 68

5.17 I-V Pairwise Correlation 69

5.18 I-V Pairwise Correlation Revised 69

5.19 Aggressive Geometry Pairwise Functional Forms 70

5.20 Non-Aggressive Geometry Pairwise Functional Forms 71

5.21 Aggressive semi-gSEM Model 72

5.22 Aggressive semi-gSEM Degradation Path 74

5.23 Aggressive semi-gSEM Model Predictions 75

vii 5.24 Aggressive semi-gSEM Model Error 76

5.25 Non-Aggressive semi-gSEM Model 77

5.26 Non-Aggressive semi-gSEM Degradation Path 79

5.27 Non-Aggressive semi-gSEM Model Predictions 80

5.28 Non-Aggressive semi-gSEM Model Error 81

viii Acknowledgements

0.1 Acknowledgements

Thanks to fellow SDLE researchers Junheng Ma, Abdulkerim Gok, Mohammad Hos-

sain, Yang Hu, Ian Kidd, Pei Zhao, Yifan Xu, Wenyu Du, Mohamed Elsaeiti, Heather

Lemire, Luke Revitsky, Maria Kim, and Reena Patel, for cooperation and coordinated

eﬀorts during this work. Special thanks and much gratitude to SDLE researchers

Nikhil Goel and Davis Zabiyaka, for their loyal and sustained help and support with

IV and EL data collection, and to Justin Fada for his hard work and dedication to

creating and developing the democratized EL camera setup utilized extensively in

these experiments.

Thanks to industrial collaborators Carl Wang and Ethan Wang (from Underwrit-

ers Laboratories) for facilitating access to the dataset from their comprehensive PV

module degradation experiments, and Andreas Meisel, Thomas Dang, Christopher

Alcantara, and Mason Terry (from Dupont Photovoltaic Systems / Silicon Valley

Technology Center) for providing high quality PV Mini-Module samples for the stud-

ies featured in this work.

And special thanks to my thesis adviser, Roger French, and additional thesis

committee members, Timothy Peshek, Laura Bruckman, Ozan Akkus, and Michael

Hore, many of whom have played a role in inﬂuencing and advancing this research

directly.

ix Lifetime and Degradation Science of Polymeric Encapsulant in Photovoltaic Systems: Investigating the Role of Ethylene Vinyl Acetate in Photovoltaic Module Performance Loss with Semi-gSEM Analytics

Abstract

NICHOLAS R. WHEELER

0.2 Abstract

The lifetime performance and degradation behavior of photovoltaic (PV) modules is of

the utmost importance for the success and growth of solar energy as a major resource

for fulﬁlling growing worldwide energy needs. While PV reliability has been a concern

for some time, existing qualiﬁcation testing methods do not reﬂect a cohesive picture

of the science behind module degradation, and are not capable of accurately predicting

module lifetime performance. Towards these goals, a statistical methodology, semi-

gSEM, was developed and applied to investigate the response of full sized PV modules

to accelerated stress conditions.

The results of this initial study indicated that a correlation exists between system

level power loss and the buildup of acetic acid resulting from the hydrolytic degra-

dation of ethylene-vinyl acetate (EVA) polymer encapsulant. To further explore this

proposed mechanistic pathway, a study was designed and conducted to characterize

x the degradation of mini-module samples under damp heat accelerated stress condi-

tions. Mini-module samples featured two construction geometries that diﬀered in the

thicknesses of screen-printed silver conductive lines (SP-Ag) to assess the impact of

gridline size on damp heat induced degradation.

Samples were measured non-destructively at many points along their degradation

pathway, using techniques that gathered both chemical and electrical information.

The semi-gSEM analytical method was applied to this dataset to highlight degra-

dation pathways and mechanisms observed in the experimental results. An EVA

encapsulant spectroscopic degradation feature was found to be statistically related to

quantiﬁed degradation features of simultaneously measured EL images. In turn, the

EL image degradation was found to be statistically related to I-V curve parameters

describing system level power loss.

The degradation pathway observed was attributed to EVA encapsulant degra-

dation leading to metallization corrosion and ultimately system level power loss in

the PV mini-module samples. Mini-module samples with thinner SP-Ag conduc-

tive lines were observed to be more severely damaged by the metallization corrosion

process. This represents a valuable step in exploring the often misunderstood role

of EVA degradation in PV module performance loss under damp heat conditions,

and demonstrates novel methodologies for building a more integrated picture of PV

module degradation as a whole.

xi 1

1 Introduction

Predicting the lifetime performance and reliability of photovoltaic (PV) power

systems is an important and growing global need that this work seeks to contribute

to. Lifetime and degradation science1 is a scientiﬁc perspective and approach to

investigating these concerns in long-lived (30+ years) systems, and it is especially

applicable to PV technologies.

1.1 World Energy Demands

A recent report from the US Department of Energy Oﬃce of Basic Energy Sciences was released with the goal of identifying priority research directions for accelerating

the development and growth of clean energy technology.2 This report features a wide

and comprehensive overview of many scientiﬁc challenges and basic research needs

facing the advancement of energy sciences and technologies worldwide. A common

theme throughout the report is that global demands for energy are increasing, and

there is a strong need to transition towards sustainable and clean energy alternatives.

The role of PV power systems in addressing these critical energy supply issues

is rapidly expanding, however it has still not surpassed older, more well-established

energy technologies (Figure 1.1). In order to compete eﬀectively against fossil fuel

alternatives, PV solar electricity will require signiﬁcant advances in the areas of per-

formance, cost, and reliability.3 There is a strong need in PV research to facilitate these improvements through scientiﬁc advances that enable more informed design and investment choices in the PV power industry. Introduction 2

Figure 1.1. Comparing the nameplate capacity of various energy technologies, it can be seen that at the time of the report (2010) PV had not surpassed other energy technologies2

1.2 Lifetime and Degradation Science for PV

Research eﬀorts at the Solar Durability and Lifetime Extension (SDLE) Research

Center all contribute towards the advancement of lifetime and degradation science

(L&DS), a unique and novel scientiﬁc approach to reliability engineering studies.

The fundamental philosophies of this approach have been explained in detail in a

recent article published in the journal Current Opinion of Solid State and Materials

Science.1 Central to the approach are large scale epidemiological studies on material

systems, towards the end goal of creating predictive and diagnostic mesoscopic evolu-

tion models of the systems which describe their functional dynamics and degradation

characteristics. These mesoscopic evolution models (Figure 1.2) highlight the key

degradation mechanisms and modes that impact the performance of a system over

its useful life cycle. Introduction 3

Figure 1.2. An example mesoscopic evolution model of acrylic polymer material for concentrating photolvoltaic applications.1

The L&DS approach is ideally suited for investigating the performance properties of PV power systems, and is capable of yielding valuable information for system design and performance prediction. Applying L&DS principles to PV system studies is an attractive opportunity to increase the performance, reliability, proﬁtability, and ultimately the widespread adoption of these clean energy systems. The purpose of this work is to demonstrate this by using the data science techniques indicated by the

L&DS approach to elucidate the EVA degradation pathhway under damp heat and how this results in module power loss. 4

2 Literature Review

The risk posed to PV technologies by reliability issues was highlighted in the

eighties by the high proﬁle failure of a particular power plant in Carissa Plains,

California. In the wake of this engineering disaster, there was an increase in research

interest into the degradation of PV modules and their components. At ﬁrst these

studies tended to focus on particular aspects of the observed degradation which were

later found to be irrelevant and coincidental. Only recently has a more detailed picture

of PV module system degradation begun to emerge in literature, but there is still a

trend towards researching mechanisms and modes in isolation without relating them

to one another. A more cohesive approach that knits together the active degradation

pathways in these systems is needed to acheive widespread grid price parity goals.3

2.1 Carrisa Plains Disaster

Built from the years 1983 to 1985, the Carrisa Plains power plant was the largest

PV installation of its time at an initial rating of 5.2 MW, made infamous by its un-

expected and drastic performance degradation, the subsequent interest in what went wrong, and the scientiﬁc studies thereof.4–6 Although plant eﬃciency remained steady

for the ﬁrst two years of operation, it declined dramatically in the subsequent years Literature Review 5

Figure 2.1. The Carissa Plains power plant exhibited much higher performance losses than estimated.4,5

at the rate of 8% to 12% per year to a 1990 rating of 3.2MW (representing approx- imately a 40% loss in just four years, far past the acceptable degradation limit of

1%/year). Figure 2.1 illustrates the projected versus actual performance degradation rates. Initial studies attributed this power loss to coincidently observed ‘EVA browning’, and furthermore attributed the encapsulant discoloration to the presence of low concentration mirrors present on 90% of the modules in the installation, though the modules were only rated for ordinary one sun (non-concentrated) use.

These initial assumptions, made with haste in an effort to explain a very concern- ing and costly engineering failure of a promising and upcoming energy technology, indicated that EVA browning was the culprit, based on little evidence. It was observed that the mirrors induced severe mismatch among the cells of the modules, and that removal of the concentrating mirrors actually resulted in efficiency gains in severely discolored modules. The assumption was then made that this was likely due to the discoloration itself, and this thought guided future research efforts to solve the problem of EVA browning, while the true complexity of the many factors influencing

PV module performance degradation continued to be unknown. Literature Review 6

2.2 Subsequent PV Studies In the wake of the outcome of the Carissa Plains power plant installation, additional further-reaching studies were pursued to examine the state of PV reliability in total.

Although these studies included the suspicious EVA browning cause of the Carissa

Plains power plant, they also identiﬁed a host of other potential causes of performance degradation, and widened the popular perspective to include factors from outside the

PV modules alone, like wiring and data acquisition systems.7,8 The dominant research thrust into PV reliability well into the 90’s, however, was strongly concerned with the discoloration of EVA encapsulant materials, especially under conditions like those seen in the Carissa Plains installation. Abundant research was performed into the degradation of EVA in PV modules,9,10 with special focus towards EVA discoloration,11–14 the rates of EVA discoloration,15,16 and relating PV system degradation to EVA discoloration.17 Some PV module reliability studies speciﬁcally focused on

EVA encapsulated modules,18 while other studies explored alternate encapsulants to

EVA,19,20 some with the explicit purpose of seeking non-browning encapsulants.21

The relative abundance and nature of these publications seem to indicate that quite a lot of effort was going towards addressing the concerns raised specifically by the Carissa Plains installation. It is very interesting to note that all this was done without solid confirmation of the discoloration of the EVA encapsulant being a truly causal factor of module performance degradation. One study of recovered Carissa

Plains modules even showed that performance losses were directly attributable to faulty solder joints, and when these problems were corrected the modules recovered signiﬁcant portions of their electrical performance,22 but even this did not seem to slow down research eﬀorts into EVA discoloration. Literature Review 7

2.3 Modern PV Module Degradation Perspective

It is now well embedded in the public consciousness that reliability is a significant concern when it comes to the feasibility of PV power systems as a competitive energy source.23 PV degradation studies challenging the assumptions of EVA browning began to emerge in the early 2000’s, due to increasing availability of long term fielded modules for analysis.24,25 These studies identified alternate likely causes for PV system power loss that were well grounded in physical and chemical mechanisms, and indicated possibilities for further research.

This improvement in study technique continues to the present day, where there is a focus on identifying the causal factors and implementing rigorous investigative methodologies.26 One important feature of modern PV reliability studies is a push towards accelerated testing, which makes practical sense due to the long time-spans of the degradation processes of interest. Accelerated tests are thought to be the key to service life prediction of PV technologies under development,27 and there is

a strong desire to concretely relate between accelerated lab testing and the degrada-

tion observed under real world use of these systems.28 This is not without risk, and

accelerated testing is recognized as potentially hazardous due to the possibility of

activating unrealistic degradation modes with the more aggressive stress conditions,

rather than just activating the realistic modes to a greater extent as intended.29

An overarching goal of modern PV reliability research is to establish accurate

models, for example those that appropriately deﬁne recommended conditions for ac-

celerated tests,30 or define the lifetime performance of PV module systems for the purposes of forecasting.31 To expand knowledge in the field and facilitate goals like these, specific research thrusts into a wide variety of chemical and physical PV degradation Literature Review 8 mechanisms, and modes of PV module failure, have been conducted and captured into extensive reviews32,33 which paint a broad picture of PV system degradation issues that scientific investigation can help to address. An interesting quality of these mechanisms and modes, is that they appear to be treated mostly in isolation from a system level perspective. There exists a need for a cohesive way to knit these dis- parate elements of degradation into linked pathways leading from causal stressors to impacted system level properties of interest.

One such degradation pathway that has become of particular interest to this work is the moisture related corrosion of PV metallization, speciﬁcally screen-printed silver conductive lines (SP-Ag), leading to overall PV system performance loss. It is indicated that EVA hydrolysis is related to module performance degradation through the generation of free acetic acid by EVA hydrolysis34–37, which happens on the same time scale as module power loss.38 Additional studies show that the onset of module power loss can be delayed with the use of an EVA with lower vinyl acetate content39

indicating that the performance degradation is driven by acetic acid availability.

The acetic acid is shown to facilitate metallization corrosion by reacting with

additives in the silver paste used to print SP-Ag onto PV cells.40 This reaction results

in the formation of precipitates that deteriorate conductivity between the SP-Ag and

the silicon surface of the PV cell.41 This is particularly interesting in the light of investigations into the microstructure of SP-Ag showing that increased wetting of the silicon surface is highly important for lowering SP-Ag contact resistance42, and indicates that this could be an important mechanism by which EVA degradation leads to system level PV module performance loss. Literature Review 9

Figure 2.2. Raman spectroscopy of EVA degradation in literature.41,43

2.4 Confocal Raman Spectroscopy for PV Materials It is hypothesized that metallization corrosion in PV modules exposed to moisture is

enhanced by acetic acid generated by the hydrolytic decomposition of EVA. During

hydrolysis, the vinyl acetate segments of EVA copolymer are converted into polyvinyl

alcohol and the volatile small molecule acetic acid. This chemical change can be

observed through measurement techniques that are sensitive to changes in amount of

acetate side group in the EVA copolymer.

Spectroscopic techniques for determining the vinyl acetate monomer composition

percentage in manufactured EVA copolymer material are well established in litera-

ture44,45, and even standards exist for doing so.46 These methods work by probing a

spectral range of irradiance for features that are representative of the chemical car-

bonyl linkage bonding the acetate group to the polymer backbone. IR spectroscopy, which operates by measuring the relative absorbance of infrared radiation in a sam-

ple, can be used, but this method requires either direct contact with the sample being

measured, or an optically clear transmission path through the entire sample. This

can require destructive dissassembly to access the target material being measured.

An alternative spectroscopic technique is Raman spectroscopy, which operates by

collecting light that has been scattered inelastically by the Raman eﬀect, where some Literature Review 10

of the energy of the incident radiation is transfered to induce rotovibrational modes

in the material being measured.47 This technique has the added bonus of functioning with the collector located at a distance from the sample, meaning direct sample

contact is not required. Thus a material can be measured in situ within a functioning

system without destructive disassembly being necessary to uncover it.

Raman Spectroscopy has been featured in literature for assessing vinyl acetate

content in EVA48 and the degree of crosslinking in EVA used as an encapsulant in

PV module assemblies.49,50 One proposed use is as a measurement technique sensitive

and rapid enough to inspect the degree of cure in manufactured PV modules as they

come oﬀ the production line.51

Raman Spectroscopy has also been used in PV degradation studies, where it shows

promise for mechanistic investigation by capturing speciﬁc EVA chemical features43

(Fig. 2.2). Unfortunately the resulting spectra are often aﬀected by a degradation

related luminescence that obscures meaningful peaks.40,43,52,53 In these cases, the size

of luminescence features are used to indicate degradation, rather than attributing

spectral changes to speciﬁc mechanisms. EVA degradation luminescence in PV mod-

ules under damp heat appears to be sharply related to distance from the cell edge,

indicated the importance of moisture ingress to this degradation process.54

Raman spectroscopy can be operated confocally as well, and in this case a micro-

scope with an adjustable depth of focus is used to collect the raman signal from a

small targetted volumetric area. This specialized variant of the Raman Spectroscopy

technique is rarely seen in literature associated with EVA encapsulant in PV module

assemblies, although it is an excellent ﬁt for targetting speciﬁc material components within an assembled system. Literature Review 11

Figure 2.3. Left: Various patterns of PV cell degradation in response to damp heat exposure are qualitatively observed.41 Right: Quantitative methods are sometimes applied to EL image analysis.55

2.5 EL Images for Localized PV Performance

A key concept to link component level material degradation to the system level elec-

trical performance in PV modules undergoing degradation is the local electrical per-

formance across the surface area of the photovoltaic cell components. Changes in the

overall system electrical performance are attributable to observable changes in the

electrical performance of sub system components like cells, through processes such as

increased resistance to electrical ﬂow from corrosion impacting the cell metallization.

The local electrical performance of PV cells in a module assembly can be observed

though the technique of electroluminescence imaging, where a PV module is placed

under forward bias conditions using a controlled voltage to cause electrical current

ﬂow into the system.56 This allows recombination of opposing charges to occur in the

semiconductor material and results in the emission of sub-band gap (IR) photons in Literature Review 12

cell surface areas with electrical connectivity. This is directly inverse to the typical

photovoltaic process for voltage generation from incoming photons, and the resulting

image highlights regions of the semiconductor material where there are problems with

the transmission of electrical current and photocurrent generation.

Areas of PV cells where electrical connectivity has been comprimised, for example

by cell fractures or increased electrical resistance, show up in EL images as dark spots

indicating the absense of charge carrier recombination. In this way, EL images can

function as a diagnostic tool for pinpointing the source of PV module system level

electrical performance loss.57–59

EL imaging is commonly seen in literature associated with degradation studies, where it is often used qualitatively to draw general conclusions about broad modes

of degradation eﬀects observed in PV modules that have undergone laboratory stress

testing or ﬁeld use. EL images in this context are a powerful indicator of the presence

cell damage from physical or chemical eﬀects.40,41,60

Quantitative analysis of EL images is more rarely seen, but a variety of approaches

are under development. Many image quantiﬁcation eﬀorts go towards calculating local

electrical parameters like series resistance from EL images61–64, and sometimes more

generalized image analysis methods are applied like ﬁnding the percentage of ‘active

area’ above a deﬁned threshold value55.

Image quantiﬁcation is necessary to statistically relate this data to other numerical

measurement types, such as those describing chemical processes or overall system

level electrical performance. With careful attention being paid to what exactly is

being quantiﬁed, EL images contain a wealth of valuable mechanistic information for

connecting other observed degradation processes. Literature Review 13

Figure 2.4. Standard I-V curve parameters65 are commonly used to study PV module degradation in response to damp heat66.

2.6 I-V Curves for System Level PV Performance

I-V (current-voltage) curve tracing is a well established measurement technique for

evaluating the overall electrical performance of photovoltaic cells and cell assemblies

such as those found in modules.67 An I-V curve measurement is obtained by sweep-

ing a PV system across a varying resistive load while it is exposed to illumination,

therefore generating a range of current ﬂows via the photovoltaic eﬀect. This creates

a series of measurements that spans the distance between the maximum amount of

current the system can produce when there is no resistance (short circuit current, where there is no voltage potential), to the maximum voltage the system can produce when it is at eﬀectively inﬁnite resistance (open circuit voltage where no current is

ﬂowing). From these curves, parameters are derived that give insight into what elec-

trical load is ideal for achieving the maximum the power output (maximum power

point, PMax), as well as the ‘squareness’ of the shape of the curve (ﬁll factor, FF).65 Literature Review 14

When I-V curves are collected from ﬁelded modules, such as those in active service

as part of a PV power plant, the measurement is sensitive to variance in natural sun-

light irradiance conditions due to environmental eﬀects like atmospheric path length

and cloud cover. Despite this uncontrolled variation, analysis of the I-V parameters

of PV power plants in a time series across their service life is often done to assess

the performance of these systems for diagnostic purposes.68–71 Through the careful

treatment of seasonality eﬀects at various timescales, like day and night cycles and weather related phenomena, gradual changes in system level electrical properties of

PV modules and arrays throughout service lifetime can be quantiﬁed for the purposes

of lifetime performance prediction and forecasting.31,72,73

I-V curve measurements are also used in controlled laboratory experiments, both

for quantifying the initial performance of manufactured PV cells and modules,74–76

as well as assessing PV module durability in response to qualiﬁcation testing and

degradation studies.38,66,77 This is done with equipment that is specially calibrated to produce highly uniform controlled irradiance conditions throughout the duration of the measurement.

By avoiding the environmental fluctuations inherent in natural lighting conditions, laboratory measured I-V curves more closely reflect real changes in PV system properties. These measurements can be recorded as the systems degrade in response to controlled environmental conditions, to quantify changes in PV system level electrical performance due to the impact of specific defined stressors. 15

3 Experimental Methods

Here, the experimental methods used to generate and analyze the data for this work are discussed, including descriptions of the fabricated and obtained samples,

exposure and evaluation equipment, and experimental and analytical procedures.

3.1 Mini-Module Samples

Commerical PV modules typically contain 60 cells or more per module, and common

standard dimensions are 65 inches by 40 inches. For the purposes of research, where

assemblies of this size would be prohibitive for laboratory study, the features of a

full sized module can be recreated in a smaller format. These smaller assemblies are

called ‘Mini-Modules’, and feature 4 PV cells in a 15 inch square module area.

3.1.1 SDLE Mini-Module Samples

Mini-modules fabricated at the SDLE center were used for exploratory studies, and

to establish initial proceduress for the measurement techniques. These samples were

manufactured with a P Energy panel laminator using the equipment settings of 12

minutes total lamination time at 120◦C and a pressure of 400mB (Figure 3.1).

PPG Starphire PV glass was used, supplied by the company Replex Plastics, who

also supplied the EVA sheet used as encapsulant material. Backsheet material was

TPE (Tedlar-PET-EVA multilayer ﬁlm) in construction, and was supplied by DuPont.

PV cells were purchased commercially from Q-Cells, and stringing and tabbing was

done manually by hand soldering in the SDLE lab. Experimental Methods 16

An SDLE mini-module is shown in Figure 3.1. The samples’ overall dimensions are 15 inches by 15 inches, and about 6.5 mm in total depth. Along the depth of the modules, the glass thickness is 2mm, and the EVA above the cell surface varies around an average thickness of 0.5mm.

Figure 3.1. A mini-module sample created at the SDLE research center, and the P Energy panel laminator settings developed and used for mini- module fabrication.

3.1.2 DPVS Mini-Module Samples

PV metalization corrosion experiments were performed using four cell mini-module samples provided by Dupont Photovoltaic Solutions (DPVS). These samples have been manufactured in two styles, which diﬀer in the thickness of the screen-printed silver conductive lines (SP-Ag), due to the amount of silver paste used during the fabrication of the modules’ cell assemblies.

The thicker SP-Ag samples represent a standard approach, and these samples are identiﬁed as having an ‘non-aggressive’ construction. The thinner SP-Ag samples are a potentially riskier approach that saves on material cost while maximizing PV active cell surface area, and these samples are identiﬁed as having an ‘aggressive’ construction. The mini-modules are constructed by DPVS from SDLE supplied PPG Experimental Methods 17

Starphire PV glass. The remaining materials used to construct the mini-modules were

provided by DPVS, including the cells, EVA encapsulant, and backsheets. Laminated

mini-modules were shipped back from DPVS along with the junction boxes, which were then attached at the SDLE center using DPVS provided silicone caulk. Junction

box leads were terminated with standard PV connectors, which were used to interface with the measurement equipment.

Figure 3.2. The SP-Ag geometry of DPVS mini-modules. A. Non-Aggressive Gridlines B. Aggressive Gridlines (Atomic force microscopy ﬁgure provided by Andreas Meisel, DPVS)

The DPVS mini-modules’ overall dimensions are 15 inches by 15 inches. The two

styles of mini-module diﬀer in the thickness of their screen-printed silver conductive

lines (SP-Ag). As seen in Figure 3.2, the ‘Non-Aggressive’ style modules (A.) feature

a SP-Ag thickness of around 80 microns, while the ‘Aggressive’ style modules (B.)

feature a SP-Ag thickness of around 40 microns. Aggressive style module construction

is a potentially riskier approach because if the degradation mechanism of metallization

corrosion proceeds through a reduction of gridline cross sectioinal area, then starting with a smaller area makes the aggressive gridlines more vulnerable. Experimental Methods 18

3.2 Exposure Equipment and Procedures

Exposure equipment and procedures were used to create controlled stressor conditions

for the L&DS study, to cause the samples’ performance to degrade as a result of

observable and identiﬁable mechanisms of interest.

3.2.1 ZPH8 Environmental Test Chamber - Damp Heat Exposure

Figure 3.3. The ZPH8 environmental testing chamber was used to ex- pose DPVS mini-module samples to damp heat conditions

One style of environmental test chamber was used in these experiments. This

chamber was purchased from Cincinnati Sub-Zero (CSZ), and its model designation was ZPH8 (Figure 3.3). Capable of tightly controlling the temperature and relative

humidity within its 2 foot by 2 foot volumne, the ZPH-8 environmental test chamber

maintained speciﬁed environmental conditions for the purposes of stress testing sam-

ples. For these experiments, ‘damp heat’ conditions, based oﬀ of test 10.13 of IEC

61215 Ed.278, were used. This standard test condition features a constant tempera-

ture of 85◦C, with humidity maintained at 85% relative humidity (r.h.). Transitioning

between damp heat and ambient lab conditions to load and unload samples was done with a 38 minute linear ramp. This fell within the rate speciﬁcations of the standard

and was acheivable by the equipment. Experimental Methods 19

3.3 Evaluation Equipment and Procedures

Several pieces of analytical equipment were used in combination with developed and

deﬁned evaluation procedures to measure PV mini-modules and quantify the chemical

and physical attributes of modes and mechanisms of degradation.

3.3.1 Confocal Raman Microscopy

To measure the chemical attributes of the PV mini-module samples, confocal Raman

microscopy was utilized to measure hydrolytic degradation in the EVA encapsulant.

Figure 3.4. A SNR study optimized the Raman measurement procedure.

SNR Optimization Study. To capture spectra with the least amount of measure-

ment contributed variability, a signal to noise ratio (SNR) study was performed. The

two parameters to optimize were the total number of spectra to average during a

measurement, n, and scan time per spectra, t. First, 60 n = 1 spectra were taken with a scan time of t = 10 seconds, and the SNR (peakheight/sd) at several peaks of interest of n = 2 to n = 30 averaged spectra were examined. Secondly, the SNRs of a linear region of collected spectra with varying n and t values were plotted to ﬁnd an

optimal combination of n and t. For this application it was concluded that optimal confocal Raman measurement parameters were n = 5 and t = 40. Experimental Methods 20

Confocal Raman Microscopy Measurement Procedure. Confocal Raman mi-

croscopy measurements were taken at two positions above the module cell surfaces.

These two positions were the cell edge, featuring a relatively small diﬀusion path

length to reach either the backsheet or module edge, and the cell center, with a rela-

tively large path length. Cell edge and center positions of the top left cell (cells facing

up, junction box behind the bottom two cells) were always measured (Figure 3.5).

Confocal Raman microscopy spectra are comprised of wavelength shifted scattered

light collected from a precisely sized and positioned volumetric area, the size of which

is proportional to the strength of the gathered signal. Larger areas collect more light,

at the cost of possibly incorporating signal from undesired components. For the

measurements in this study, a magniﬁcation level of 50x maximized the volumetric

area of EVA material, while minimizing the inclusion of glass and silicon cell surfaces.

Repeatable positioning was acheived by ﬁrst centering the volume on a gridline at the

cell surface, then precisely moving 200 microns in the y and z directions (Figure 3.5).

Figure 3.5. Two cell locations were measured with confocal Raman microscopy, with precise displacement away from the cell grid line. Experimental Methods 21

3.3.2 EL and I-V for Electrical Properties

To measure the physical attributes of the samples, EL imaging and I-V curve tracing

techniques quantiﬁed changes in local and system level electrical performance.

Figure 3.6. EL and I-V measurement procedures were optimized with successive Gage R&R studies to mimize variation.

Gage R&R for Minimizing Measurement Variability. As seen with confocal

Raman microscopy, the EL imaging and I-V curve tracing procedures were developed with no prior experience in the research group. This necessitated identifying optimal

measurement procedures and parameters to minimize variation contributions from the

measurement techniques themselves. SNR studies suited the spectroscopic technique,

and Gage R&R studies optimized the EL imaging and I-V curve tracing techniques.

Gage R&R studies are a standard industrial technique for assessing contributing

factors to a measurement system’s total variability.79 Repeat measurements of a group

of samples are taken by multiple operators, and an ANOVA model is applied to

distinguish sample variation from measurement technique variation originating from

either between operators (reproducibility), or within them (repeatability). Successive

Gage R&R studies utilizing 10 samples, 3 operators, and 3 measurement repeats were

performed to reﬁne the EL imaging and I-V curve tracing procedures (Figure 3.6). Experimental Methods 22

EL Imaging - Measurement Procedure. The EL images for these experiments

are being captured using a Samsung model NX100 camera, which has been modiﬁed

to remove the two IR ﬁlters and an ultrasonic cleaner from the primary 14.6 megapixel

image sensor.80 30 second long exposure images were taken in a dark box within a dark room to create bright images with light originating only from the samples being measured. The entire setup was located on a motion dampening optical table.

Each measurement session proceeded as follows. For each measurement, the PV

mini-module sample was loaded onto the sample ﬁxture with the cells facing the

camera and the junction box leads pointed downwards. The module’s PV connectors were attached to the power supply, and the opaque plastic of the dark box was sealed

shut along velcro strips. All room lights were turned oﬀ, and the power supply was

energized in current control mode, providing 6A current to the module. As soon as

the power supply voltage stabilized (within 1 second), image taking was intitialized with a wired remote button on the camera. After the 30 second exposure, the lights were turned back on and the next sample swapped in. This operation was performed

for all of the samples three times by two operators, for a total of six measurements

at each exposure round.

Figure 3.7. EL images were taken on a motion dampening table. Experimental Methods 23

I-V Curve Tracing - Measurement Procedure. Two pieces of equipment were

used in these experiments to measure I-V Curves. The light source that created

controlled illumination conditions was an Apollo Solar Simulator made by All Real

(Taiwan), a Class AAA solar simulator as per IEC60904-1 ed2.0 requirements.81 The

I-V curves themselves were measured with a Daystar Suitcase I-V Curve Tracer.

Each measurement session proceeded as follows. One hour prior to beginning

measurements, the lamp was turned on and allowed to warm up with the system

shutter closed. For each measurement, the PV mini-module sample was attached to

the Daystar via PV connector cables, and a thermocouple was placed on the backsheet

in the bottom left quadrant (cells facing down, junction box along top edge). With

all attachments made, the sample was loaded into the All Real Solar Simulator and

‘charged up’ by the Daystar, prior to opening the shutter and performing the I-V curve

sweep. The shutter remained open for just enough time to take the measurement,

to avoid undue temperature climb in the instrument. With the shutter closed, the

sample was removed and swapped for the next. This operation was performed for all

of the samples three times by two operators, for a total of six measurements at each

exposure round.

Figure 3.8. Solar simulator setup with Si reference cell shown. Experimental Methods 24

3.4 Data Analytics & Environment The work shown here was created entirely with free and open source software (FOSS)

tools. The majority of the analytics and computational work was performed using

functions written in the R coding language. These were assembled together into

a data pipeline which automated the entire computational process from extracting variables from the raw data through to the statistical analytics.

Figure 3.9. Several free and open source software tools were used.

3.4.1 Computational Environment

The foundation of the collection of FOSS tools used for this work was the Kubuntu

operating system. The software used to generate the contents of this document is

cross platform, and runs on this Linux distribution. Within Kubuntu, the console

program ‘Konsole’ was heavily used for writing and running several types of code.

The workﬂow used for coding was to edit a plain text ﬁle using Vim, while an

adjacent Konsole tab held the means to quickly run the code being edited. This

enabled fast keyboard-only switching between code modiﬁcation and execution. Work

could proceed remotely via ssh, even without a graphical connection. This workﬂow was used to develop R82, Python83, and LATEX code in Git repositories. R code was executed from within the R Console environment from a command

line session in Konsole, in preference to using RStudio84. Python code was executed

from the command line, and custom Python scripts were used to handle compiling

LATEX code (and cleaning up unnecessary auxiliary ﬁles afterwards). Experimental Methods 25

Figure 3.10. Peak heights were found after baselining the region of interest.

3.4.2 Confocal Raman Data Analytics

For the confocal Raman microscopy measurements, the parameters of interest were ratios between the heights of known peaks (section 2.443) and observed features in the

spectra. Of interest were peaks between 1250-1300 cm−1 and 1375-1450 cm−1 (EVA

CH peaks, known here as ‘CHa’ and ‘CHb’), a peak between 1680-1750 cm−1 (EVA

C=O peak), and a broad feature found to be related to degradation processes with a maxima between 1330-1350 cm−1. Hypothetically, the CH peaks should be conserved throughout the degradation process, while the C=O peak decreases as EVA hydrolysis proceeds, resulting in a change in the ratio between the CH and C=O peaks.34–37

The R package ‘HyperSpec’85 was used to extract spectra from the native binary

.spc ﬁle type, as numeric ordered pairs. The extracted spectra were baselined using the R package ‘baseline’.86 It was found that a linear ﬁt provided the best spectral correction in the wavenumber region of interest. Parameters were calculated by seeking maxima within wavenumber ranges, and calculating their ratio (Figure 3.10). Experimental Methods 26

Figure 3.11. A sequence of processing steps was applied to each of the as-captured EL images to individually locate, align, and isolate each of the four PV cells encapuslated within the mini-module assembly.

3.4.3 EL Image Data Analytics

Several parameters of interest were extracted from the EL image data, which begin initially as .JPG image ﬁles. The R package ‘EBImage’87 from bioconductor was used to read in the image ﬁles and work with them as numeric matrices. EBImage was chosen in favor of other image handling R packages like ‘jpeg’88 due to its rotation function, which was used extensively to align the PV module images.

Algorithms were developed in R to isolate single cell images from the original full module images. This was a multi-step process that involved coarsely roating the entire module into alignment, dividing the image into quadrants containing each cell, rotating the quadrants into alignment, and cropping down to the ﬁnal isolated cell images (Figure 3.11). Although the original image is initially grayscaled to work with simpliﬁed single brightness values, the rotation and cropping steps throughout the process are recorded and reapplied to the original color version of the image. Experimental Methods 27

Figure 3.12. Image analysis techniques of summing, threshholding, and ratio’ing were used to quantify EL image degradation parameters.

After isolating and aligning the cells, several parameters were extracted from

grayscaled original images, as well as grayscaled isolated cell sub images, to repre-

sent the local electrical properties. Grayscaling was done using the simplest possible

method of calculating the mean of the combined value of the three color channels.

The ‘total image sum’ parameter was calculated by summing grayscaled pixel values

from the entire original image. Image threshold ratios were also calculated using the

entire original image, for both pixel sums and pixel counts, using the image mean as

the threshold value, with the over threshold value as the numerator.

Cell area ratios were obtained by calculating the ratio of the sums of equal rect-

angular areas ‘near’ to those ‘far’ from the vertical busbars. The rectangular areas were deﬁned algorithmically, relative to the location of the cell busbars, with their

sizes set to a percentage of the cell image dimensions (Figure 3.11). The ﬁnal value

is calculated as the mean of the ratios of 6 area pairs (one pair for each side of the

three busbars) for each cell, for all four cells in one image of a module. Experimental Methods 28

Figure 3.13. The calculated I-V curve parameters ‘PMax’ (power at maximum power point), ‘FF’ (Fill Factor, the ‘squareness’ of the I-V curve), Isc (short circuit current), and Voc (open circuit voltage) are well-known and widely used diagnostics of PV electrical performance.

3.4.4 I-V Curve Data Analytics

I-V Curve measurements were exported by the measurement equipment as XML ﬁles

containing ordered pair curves and their calculated parameters as nodes in the XML

structure. The R package ‘XML’ was used to access the nodes within these ﬁles as

elements of list objects, in order to extract the values of interest as single numeric values. Extracted parameters were the maximum power point (PMax), ﬁll factor

(FF), short circuit current (Isc), and open circuit voltage (Voc) (Figure 3.13).

Irradiance and temperature normalization was explored in an attempt to account

for variation originating from environmental conditions during measurement. Tem-

perature climb on the sample platform during a measurement session was recorded via a coincident thermocouple reading during each measurement. Irradiance variation

due to lamp ﬂuctuations or aging decline was recorded via a coincident pyranometer

reading during each measurement. Experimental Methods 29

Figure 3.14. A calibration study related the pyranometer reading to a well characterized reference cell, enabling the irradiance correction of I-V curves measured at non-ideal lamp bulb brightnesses.

Calibration studies were performed to relate the pyranometer reading to the true

irradiance in W/m2 through a calibrated reference cell. Lamp power was varied in a controlled manner while recording the coincident readings of the pyranometer and the reference cell. The reference cell’s reading at 1000W/m2 was a known calibrated value, allowing the pyranometer reading to be related back to irradiance in W/m2.

This irradiance value was used to ﬁrst ﬁnd a normalized Isc value, which in turn was used to normalize the PMax value, using the following equation.67

P mp1 P mp2 = ln( Isc1 ) Isc1 Io ∗ Isc Isc2 ln( 2 ) Io

Temperature normalization was applied afterwards, using a set of coeﬃcients cor-

responding to the PV cells used in the mini-module construction. These coeﬃcients were provided by our Dupont collaborators, who manufactured the cells and lami-

nated the mini-modules. The coefficient used to normalize PMax was −4.5mW/◦C, and the coefficient used to normalize fill factor was −0.0015/◦C. Experimental Methods 30

Figure 3.15. Delta format can make obscured trends apparent, as seen with these simulated data (A) represented in delta format (B).

3.4.5 Delta Format

The quantitative values of the measurements at any given exposure round are unim-

portant; rather it is the degree of change on a per round basis, relative to the starting

point, that gives insight into the degradation that has occured during that round.

To calculate this relative change value, each measurement must be compared to its

sample’s average starting value to calculate the diﬀerence.

This can be thought of as representing the data as the diﬀerence between each

sample’s original properties, and its properties after each round of degradation. This

format has the advantage of being insensitive to variability in the initial state of

samples - only the degree of change is represented. As seen in Figure 3.15, although variability exists between the starting values of the samples, their rate of progres-

sion through the degradation process, independant of starting value, is much more

comparably meaningful. Experimental Methods 31

Figure 3.16. Data handling steps were automated into a pipeline.

3.4.6 Data Pipeline

Computational tasks, accomplished through the use of developed R functions, were arranged to automate the data handling and analytics process. A defined folder structure received the raw data files and stored the outputs of the functions at each step along the process. New raw data files were placed into the folder structure as each measurement round completed, and the entire computational process selectively re-run to update the analysis with the next round of results.

Data handling began with extracting the parameters of interest from the raw data of each measurement type. For confocal Raman microscopy, spectra were extracted as numeric ordered pairs from the raw .spc binary ﬁle, baselined, and peak ratios calculated. For EL images, algorithms ﬁrst isolated and aligned the individual cell sub-images, and the sums, threshold ratios, and area ratios were then calculated. For

I-V curves, the curve parameters were ﬁrst extracted from the raw XML ﬁle, and then irradiance and temperature normalization was applied.

For all three measurement types, the extracted numerical parameters from each measurement round were saved into a ‘.csv’ text ﬁle. These text ﬁles were recombined into a single dataframe containing all measurement types and all rounds. Retain normalization and delta formatting were performed, as previously described. Lastly, statistical methods were applied to the combined and normalized dataframe. Experimental Methods 32

3.5 Statistical Methods Statistical analytics used in these studies consisted of exploratory data analysis using

pairwise scatterplot matrices, and a statistical modeling technique developed at the

SDLE center along the course of this work, semi-gSEM analysis. 3.5.1 EDA with Pairwise Plotting The pairwise scatterplot matrix is a commonly applied statistical technique for rapidly

evaluating relationships between variables in a dataset. A square grid of plots is

constructed, with the number of plots in the x and y directions being equal to the

total number of variables being explored. Variable names are placed from top left to

bottom right along the diagonal, and the oﬀ-axis intersections of each pair of variables

are illustrated with a scatterplot and additional information about the variables’

relationship. In this work, scatterplots were placed in the bottom left half of the

matrix, and color coded correlation values and semi-gSEM functional form best ﬁts

(as determined by adjusted R2 value) were placed in the top right half (Figure 3.17).

Figure 3.17. Scatterplot matrices are a useful EDA tool for exploring correlations and functional form ﬁts between measured variables. Experimental Methods 33

3.5.2 Semi-gSEM Analysis

To evaluate the results of the L&DS study, the novel statistical analytical technique

semi-supervised generalized structural equation (semi-gSEM) modeling (based on the well established technique structural equation modeling89) was developed and applied.

This process entails utilizing exploratory data analysis (EDA) as well as domain knowledge guidance to identify a set of variables representing speciﬁc mechanisms and modes of degradation of interest within the system under study. These variables are analyzed to reveal pathways of statistically signiﬁcant relationships that link applied stressors to mechanisms of degradation and system level responses of interest.

The semi-gSEM technique has been used for several studies, and extensively presented and published by the SDLE research group.90–110 Although several principles of application are under development, this work features ‘Principle 1’, which exam- ines univariate relationships between the variables under study. Starting backwards from the system level parameter of most interest to the researcher, the model ﬁt of every other variable as a predictor, using a deﬁned set of mechanistically appropriate functional forms, is evaluated on the basis of the calculated adjusted R2 parameter.

The ‘best ﬁtting’ functional form for each variable is examined, and if the adjusted

R2 of the ﬁt is above 0.2 the predictor variable is considered to be strongly related.

This ﬁtting processes is repeated for each related variable, until all variable rela-

tionships have been quantifed, or no more strong relationships are found. The result

is a network of quantiﬁed relationships between the variables, where the pathways

of strongest sequential relationships are of particular interest. Domain knowledge is

applied to evaluate whether these pathways represent known mechanisms of degra-

dation, and so indicate probable causal paths of mechanistic degradation. Experimental Methods 34

3.5.3 Error Propagation and Statistical Power

The ﬁnal results of the semi-gSEM modeling process are a series of predictive equa-

tions describing the state of the system level parameter of interest, and various sub-

system degradation processes, throughout the course of the modeled degradation.

These equations, composed of variables and coupling coeﬃcients, can be evaluated

statistically by using the methods of error propagation and post-hoc power analysis.

Error propogation is the practice of carrying the error values of individual com-

ponent terms through to a ﬁnal calculated result, and is performed as follows.

∆X p if X = A + B then = (∆A)2 + (∆B)2 X r ∆X ∆A ∆B if X = A ∗ B then = ( )2 + ( )2 X A B r ∆X 2 ∗ ∆A 3 ∗ ∆B if X = A2 ∗ B3 then = ( )2 + ( )2 X A B Post hoc statistical power analysis, which uses a study’s outcome to retrospectively

estimate its statistical power, is calculated as follows.

( ) ∆ P ower = Φ −Z + 1−α/2 p 2 2 σ1/n1 + σ2/n2

W here :

n1 = sample size for group 1 n2 = sample size for group 2 ∆ = |µ2 − µ1| = absolute diﬀerence between two means σ1, σ2 = variance of mean 1 and 2 α = probability of type I error (usually 0.05) z = critical Z value for a given α Φ() = function converting a critical Z value to power 35

4 Experimental Results

4.1 UL PV Module Study - Degradation Pathway Modeling

Figure 4.1. SEM modeling corresponds to L&DS modeling needs108

Upon beginning the development of a methodology for modeling degradation pathways in systems under investigation, there was not a deﬁned approach to the creation

of predictive and diagnostic mesoscopic evolution models. Structural equation model-

ing (SEM) was chosen as a well-established statistical method which looked to accom-

plish similar results to the goals of L&DS research. As an overarching requirement,

there was a need to describe the degradation of a system level performance charac-

teristic of interest as a sum of various related contributing factors. The compatibility

of this concept with the SEM technique is illustrated in Figure 4.1, where the rela-

tionships between variables in an example SEM model distill to a system of equations Experimental Results 36 which describes the system under study. To begin testing the hypothesis that the pre-

existing SEM methodology was a good ﬁt for the needs of the L&DS approach, an

appropriate dataset was essential. The required characteristics of this L&DS dataset were very speciﬁc; multiple coincident observations of several variables of interest de-

scribing the system under investigation at diﬀerent levels, at multiple points of time

over the course of the system degradation. A comprehensive set of experiments on full

size PV modules had recently been performed by a research group from Underwriter’s

Laboratories (UL), and this dataset was graciously provided to the SDLE research

center for the development of the L&DS modeling technique. The details of this

dataset are well described by a publication from the UL researchers38, as well as our

own.108 As a general summary, PV modules were exposed to damp heat (85 ◦C and

85%r.h.) and UV preconditioning conditions, and their performance characteristics

measured periodically as they changed in response to the stressful conditions. Ad-

ditionally, selected modules were destructively disassembled at speciﬁc time points

over the course of exposures to measure characteristics of the sub-system compo-

nents. Figure 4.3 illustrates the exposure route for the samples, and a destructively

disassembled module.

Soon after beginning the process of SEM analysis on this UL dataset, various

non-idealities (with respect to L&DS objectives) were found with both the analytical

method as well as the dataset itself. Although the UL dataset was comprehensive,

the statistical technique required complete cases of multiple coincident observations

across all variables that left large portions of the dataset unusable for modeling.

An additional assumption of the traditional SEM technique was that it could only

represent linear relationships between variables, and the resulting series of equations Experimental Results 37

Figure 4.2. The exposure routes for the UL PV degradation study show sample loss due to the destructive disassembly of modules. This caused the entire UV preconditioning exposure to be unusable due to insuﬃ- cient data at the point of signiﬁcant degradation.108

took the form of additive linear models. This conﬂicted with domain knowledge of the

natural phenomena under investigation, where many years of scientiﬁc inquiry have

established the importance of exponential, logarithmic, and second order processes.

Despite these challenges, the dataset was cleaned and curated to bring it into a

suitable format for the analysis to proceed. To more speciﬁcally address the research

needs of the L&DS approach, modiﬁcations to the traditional SEM technique were

implemented, and a new analytical methodology developed. This methodology was

named ‘semi-gSEM’, which stands for semi-supervised generalized structural equa-

tion modeling. Semi-supervised refers to the interplay between domain knowledge

guidance and numerical results in the model development process. Generalized refers

to the inclusion of non-linear functional forms to describe the relationships between variables. The details of the initial development of the methodology are detailed in a

journal article in IEEE Access.108 Experimental Results 38

Figure 4.3. The electrical properties of PV modules under damp heat in the UL study showed interesting changepoint behavior, with rapid power loss manifesting around 2800 hours108. This corresponds to results reported elsewhere in literature, where the time scale of this degradation onset was shown to be related to EVA vinyl acetate content.39

This novel methodology was developed in tandem with its application to the UL

PV module dataset, and the results obtained were very interesting from a L&DS perspective. Many insights were generated that continue to guide further studies of this type, the most important of which is ensuring adequate data is collected to describe an observed degradation process. In the UL PV module dataset, it was found that the UV preconditioning testing stress conditions were not aggressive enough to cause suﬃcient degradation in the samples within the period of the study where adequate samples remained (due to losses each round from destructive testing). This made the UV results from the UL study not usable, due to insuﬃcient response, although the damp heat results compensated for this. Two degradation pathways of interest, originally hypothesized from domain knowledge, were indicated in the relationships between the measured variables for PV modules under damp heat testing. Experimental Results 39

The ﬁrst indicated pathway was a strong coupling from applied stressors to both

IR signals in the EVA encapsulant and measured free acetic acid content, and a strong

coupling of these two indicators of EVA hydrolysis to power loss observed in measured

I-V curves. The reduction in the I-V curve PMax parameter demonstrated striking

changepoint behavior (Fig. 4.3), indicating the temporal evolution of diﬀerent modes

of degradation throughout the exposure. This changepoint behavior is conﬁrmed by

other literature studies, where it has been shown to be directly inﬂuenced by the

percentage of vinyl acetate in the EVA encapsulant.39 It is hypothesized that this

relationship between EVA hydrolysis and module power loss is due to the inﬂuence

of acetic acid on metallization corrosion processes, although no measurements aiming

to observe corrosion directly were present in the UL study.

The second indicated pathway was a strong coupling from the stressors to IR

signals in the PET backsheet indicating hydrolysis and PET chain scission. This

is hypothesized to correspond to a decrease in PET mechanical properties (via em-

brittlement) and subsequently to a decrease in the wet insulation resistance of the

modules (via mechanical failure and fracture of the PET insulating layer), although

this was not adequately observed in this particular dataset due to the high degree of variation inherent to the wet insulation resistance measurement.

Of these two degradation pathways, the focus of this work is on the ﬁrst; the

suspected link between observed EVA hydrolysis and module power loss by way of

hypothesized metallization corrosion. The further development of the UL dataset

ﬁndings into PV module system models incorporating these insights is detailed in

published conference proceedings from several public presentations.93,106 A graphical representation of these published results is shown in Figure 4.4. Experimental Results 40

Figure 4.4. Results from applying the semi-gSEM methodology to the UL PV module dataset reveal evidence in the data of two active degradation pathways that matched well with domain knowledge108 Experimental Results 41

Once being developed into a usable technique, the semi-gSEM methodology has since been adopted throughout the SDLE center as a tool for conducting analysis of

L&DS studies. The ﬁrst of these was applying the technique to a dataset of acrylic degradation generated as part of a concentrating photovoltaics study.111 Code enabling an automated approach to applying the technique to a compatibly structured dataset, initially developed by the author of this thesis, has been further developed collaboratively into an easily transmissible R package. This package has been featured in a CWRU data science class DSCI 351-451, where students have applied the technique to various datasets and interpreted the results as part of their coursework.

Participation in the collaborative development of the semi-gSEM methodology and assisting the multi-displinary eﬀorts of many other SDLE researchers has been a major focus of this thesis work from the beginning. A pioneering role was played in the early development of the ﬁrst iteration of the method as it was applied to the

UL data, and through the initial creation of R scripts and functions to automate the proceedure and make it scaleable for rapid analyses of large datasets.

This was done toward the ultimate goal of applying the method towards an ex- panded analysis of polymeric degradation within PV module systems. Initial findings utilizing the UL dataset guided the work towards the specific degradation pathway of damp heat induced EVA hydrolysis and the delayed onset of losses in system level electrical performance properties. A fuller fledged L&DS study, digging deeper into the connection between these two phenomena, was an attractive next step. To plan and execute this study effectively, exploratory pilot studies were required. These proved invaluable for developing the necessary laboratory procedures and measurement techniques, all of which were new to the SDLE research center. Experimental Results 42

4.2 SDLE Mini-Modules Pilot Study

SDLE constructed mini-modules were used to establish each measurement technique afterwards applied to the SP-Ag corrosion study. One of these mini-modules was exposed to over 1000 hrs of damp heat conditions (85◦C 85%r.h.) to verify that measurable degradation could be caused in a feasible amount of time. The results of these exploratory experiments were used to plan the L&DS SP-Ag corrosion study.

4.2.1 Exploratory Confocal Raman Measurements

Figure 4.5. Pilot study confocal Raman results, exploring the geometry of PV mini-modules with a vertical axis scan positioned over a gridline.

Scouting confocal Raman studies show that diﬀerent material layers of the PV module construction are separately visible to the instrument depending on focal depth and measurement positioning (Figure 4.5). This demonstrates that targeted chemical spectra can be obtained for sub-level components within these systems without physical disassembly, which is extremely important for maintaining the sample population throughout the course of the study. Experimental Results 43

Figure 4.6. Pilot study EL (left) and I-V (right) results, showing an SDLE mini-module degraded after 1000+ hours damp heat exposure.

4.2.2 Exploratory EL Image Measurements An EL image of a damp heat degraded SDLE mini-module showed noticeable dark

areas in the middle of the cell furthest from the conductive busbars (Figure 4.6). This was consistent with the hypothetical degradation mechanism of SP-Ag corrosion in

response to acetic acid generated by hydrolyzed EVA: Reduction of eﬀective cross-

sectional area in the silver paste gridlines due to the formation of non-conducting

precipitates leads to increasing resistance to current ﬂow and consequently a degra-

dation of overall module electrical properties.

4.2.3 Exploratory I-V Curve Measurements I-V curve measurements for a degraded versus a non-degraded SDLE mini-module

show an appreciable diﬀerence in the overall system electrical performance properties

(Figure 4.6). This indicated a reduction in the overall module electrical properties

resulting from degradation processes induced by damp heat exposure. Experimental Results 44

4.3 Screen Printed Silver (SP-Ag) Corrosion L&DS Dataset

A missing piece that arose during the UL PV dataset analysis was the link between the

observed EVA hydrolysis and the observed module power loss. To extend the results

of the study, investigation of the hypothetical mechanism of metallization corrosion

by generated acetic acid was an attractive next step. It is suspected that the missing

link between EVA hydrolysis and PV module power loss is metallization corrosion,

and that this occurs in the presence of moisture and acetic acid.

A recent published study indicates at least one mechanism by which this could

occur is through the oxidation and subsequent metal salt formation of SP-Ag lead

additives with acetic acid.40 41 A dataset featuring coincident observations of EVA chemistry, metallization conductivity, and system level PV module electrical properties would be valuable for investigating metallization corrosion in this context.

Pilot studies, seen in the previous section, indicated that metallization corrosion could be created by damp heat conditions and observed with instruments in the available lab facilities. A study was planned to induce this degradation eﬀect in a population of mini-module samples of two diﬀerent construction geometries. Chemical and electrical properties were measured along the course of the degradation to create a L&DS dataset for further investigating this degradation phenomena.

Four samples of each construction were exposed to damp heat conditions for 3400 hours of total stress dose. Details of sample construction, exposure conditions, and measurement techniques are described in chapter 3. Measurements were taken with all three measurement techniques at 0, 168, 336, 672, 1008, 2016, 2904, and 3400 hours. One unexposed sample of each construction was also measured at each step to function as a standard. The resulting L&Ds dataset is shown on the following pages. Experimental Results 45

4.3.1 Confocal Raman for in-Situ EVA Degradation

Cell Edge CH/CO Peak Ratio (Fig. 4.7). The EVA CHb and CO peaks (see

subsection 3.4.2), relative to the degradation feature, decrease in magnitude with

damp heat exposure. The ratios of the CHb to CO peaks, while appearing higher in

the exposed samples than the retains, do not show strong increasing trends.

Figure 4.7. Confocal Raman CHb/CO peak ratios at cell edge.

Cell Center CH/CO Peak Ratio (Fig. 4.8). Similar trends in the EVA CHb and

CO peaks at the cell center, comparable to those at the cell edge, are observed. The

magnitude of relative peak decreases are smaller at the cell center, and the CHb/CO

ratios also do not show a strong increasing trend.

Figure 4.8. Confocal Raman CHb/CO peak ratios at cell center. Experimental Results 46

Cell Edge Degradation Peak Ratios (Fig. 4.9). The size of the degradation feature, compared to all three EVA peaks (see subsection 3.4.2), increases with damp heat exposure. Using the EVA CHb peak as the reference peak appears to produce the most consistent values between samples at any given measurement round.

Figure 4.9. Confocal Raman degradation peak ratios at cell edge.

Cell Center Degradation Peak Ratios (Fig. 4.10). Similar trends in the EVA

CHb and CO peaks at the cell center, comparable to those at the cell edge, are observed. The cell center degradation peak ratios increase more slowly along the course of degradation, reaching smaller ﬁnal values at the last measurement step.

Figure 4.10. Confocal Raman degradation peak ratios at cell center. Experimental Results 47

4.3.2 EL Imaging for Local Electrical Properties

Image Pixel Sum (Fig. 4.11). The sum of the grayscaled pixels in the EL images

of exposed samples decrease in the last measurement round, relative to the retains.

Figure 4.11. EL image pixel sum results.

Mean Threshold Sum Ratio (Fig. 4.12). The ratios of the sums of the above-

mean to below-mean pixels in the exposed samples’ EL images follow a similar trend,

decreasing after the ﬁnal round of DH exposure relative to the retains.

Figure 4.12. EL image mean threshold sum ratio results. Experimental Results 48

Mean Threshold Count Ratio (Fig. 4.13). The ratios of the counts of the above- mean to below-mean pixels in the entire original images decrease compared to the retains after the 2000hr step, with the largest decrease occuring during the ﬁnal step.

Figure 4.13. EL image mean threshold count ratio results.

Area Ratio (Fig. 4.14). The ratios of the sums of equal rectangular areas far from the busbars, relative to those near the busbars, increase with DH exposure, relative to the baseline retain samples. The area ratios of aggressive sample constructions reach a higher ﬁnal average value than the area ratios of non-aggressive samples.

Figure 4.14. EL image area ratio results, showing strong increasing trends which diﬀer between module construction geometries. Experimental Results 49

4.3.3 I-V Curve Tracing for System Level Electrical Properties

As-Measured PMax (Fig. 4.15). The PMax measurements, independant of the irradiance or temperature at the time of measurement, intially increases then decreases over subsequent measurement rounds for both exposed and unexposed samples.

Figure 4.15. I-V curve PMax results, as measured.

Fill Factor (Fig. 4.16). The ﬁll factors are seen to typically decrease in DH exposed samples, relative to the non-aggressive baseline retain samples.

Figure 4.16. I-V curve ﬁll factor results. Experimental Results 50

Corrected PMax (Fig. 4.17). The temperature and irradiance corrected PMax measurements show an initial sharp decrease and then increase back to near the starting point, followed by a small increase and then a sharp decrease during the last two measurement rounds. The initial sharp decrease coincides with high pyranometer readings, and the last three rounds of measurement coincident with low pyranometer readings. Corrected PMax could not be calculated for measurement round 1 (168hrs), as pyranometer values were not collected due to instrument error.

Figure 4.17. I-V curve PMax results, corrected for temp. & irradiance. 51

5 Discussion & semi-gSEM Modeling

This chapter will further explore the data results in the previous chapter, with

respect to literature degradation mechanisms, and building an informed semi-gSEM

degradation pathway model.

5.1 Hypothetical Degradation Mechanisms

The degradation pathway of interest is system level power loss, caused by an increase

in gridline resistivity due to metallization corrosion processes, facilitated by the gen-

eration of acetic acid from hydrolytic decomposition of EVA encapsulant under damp

heat conditions. This can be broken down into a series of hypothetical steps, sup-

ported by reported observations from both literature as well as the initial UL study

of full sized PV modules degrading under damp heat conditions.

The ﬁrst step in this hypothetical process is the generation of acetic acid due to the

hydrolytic decomposition of EVA encapsulant in the presence of moisture and heat.

EVA is well documented in literature to hydrolytically decompose to form ethylene vinyl alcohol (EVOH) copolymer and acetic acid in the presence of moisture34–37, and

this is even utilized as a synthetic route to EVOH copolymer. The initial UL study

featured two comparable variables related to the hydrolytic decomposition of EVA within full sized PV modules. The ﬁrst, an ATR-FTIR (attenuated total reﬂectance,

fourier transform infrared) spectroscopy measurement of a peak ratio corresponding

to carbonyl content, was found to be a more reliable variable than the second, acetic

acid content as measured by gas chromatography. This was thought to be related to

the volatility of acetic acid, given that the destructive dissassembly exposed the EVA Discussion & semi-gSEM Modeling 52

sample to be measured to atmosphere for an unquantiﬁed time period, It is important

to note, however, that acetic acid content was observed by gas chromatography to

increase with damp heat exposure time, and that this was correlated with the IR

signal indicating EVA hydrolysis. This indicates that the hypothetical EVA hydrolysis

mechanism can be expected to proceed within the mini-module environment of the

follow up study.

The next step in the hypothetical pathway is the participation of acetic acid in a

metallization corrosion process impacting the resistivity of the screen printed silver

gridlines. This is the missing step in the initial UL study, where no measured variables were found that linked the observed EVA hydrolysis to the observed overall system

electrical performance loss. A reported example in literature41 shows the formation

of lead acetate needle particles as precipitates on the surfaces and cell interfaces of

screen printed silver gridlines, in response to damp heat degradation of PV module

samples. This can be attributed to the acetic acid generated by EVA hydrolysis

reacting with lead additives in the silver gridline paste material. Hypothetically, an

accumulation of these degradation byproduct precipitates would interfere with the

ability of the gridlines to pick up and conduct charge carriers from the silicon cell, which would present as an increase in gridline resistivity.

Lastly, the overall system electrical properties hypothetically decrease as a re-

sult of increased gridline resistances leading to power loss through an increase in

recombination events from uncollected charge carriers. This ﬁnal outcome of the hy-

pothetical degradation pathway was observed in the UL study in the form of losses

in the maximum power output generated by exposed devices, which was observed to

be statistically related to the observed EVA hydrolysis. Discussion & semi-gSEM Modeling 53

5.2 Interpretation of Variable Trends

5.2.1 Confocal Raman Microscopy

Figure 5.1. Confocal Raman spectra of an example mini module, with increasing exposure horizontally. First row as-measured, second row normalized to CHb peak, third row normalized to degradation feature.

Spectral Shape (Fig. 5.1). The overall signal strength of the measured confocal

Raman spectra decreased with increasing exposure to damp heat stress conditions.

This coincides with decreased visibility observed in the confocal microscope video feed during measurement alignment. The decrease in signal strength was not observed in the retained samples, indicating that this is a degradation related phenomenon.

An increase in the size of a broad feature, located between the EVA CHa and

CHb peaks, can be seen to occur with increasing degradation When the spectra are normalized to this feature, it appears to maintain its shape while the EVA peaks decrease in size around it. The origin of this feature is unclear, but it is likely to be related to decreasing signal strength and degradation induced polymer luminescence. Discussion & semi-gSEM Modeling 54

Figure 5.2. Normalized to EVA CHb peak. CO peak is highlighted. EVA CO Peak (Fig. 5.2). The EVA CO peak was expected to decrease with

proceeding EVA hydrolysis due to the conversion of acetate side groups into free

acetic acid within the polymer matrix. This hydrolytic degradation process occurs via the consumption of a molecule of water to cleave the carbonyl linkage containing

the CO group, reducing the presence of this functional group in the material.34–37

Literature studies show that the amount of acetic acid generation needed to cause a

module power loss is around 1% of the EVA vinyl acetate side groups.39 Unfortunately,

the decreasing Raman signal strength renders the spectra insensitive to this small

degree of change at higher levels of degradation. Discussion & semi-gSEM Modeling 55

Figure 5.3. Normalized to EVA CHb peak. Deg. feature is highlighted.

EVA Degradation Peak (Fig. 5.3). The broad feature located between the two

EVA CH peaks was not hypothesized to be important, although in the experimental

results it was found to be strongly related to damp heat exposure. The exact nature

of this feature is unclear, although it could be related to degradation induced polymer

luminescence obscuring the Raman spectra. This eﬀect is well reported in literature, where it is used as an indication of polymer degradation.

The feature also happens to match the location and shape of the Raman spectra

of the front side glass of the module, measured during exploratory pilot studies. Discussion & semi-gSEM Modeling 56

Figure 5.4. Top: Polymer degradation luminescence of EVA.53 Bottom Right: Raman spectra of EVA in PV modules under damp heat showing formation of broad luminescence feature.40 Bottom Left: Formation of degradation feature in Raman spectra of this study.

Polymer Degradation Luminescence (Fig. 5.4). A recent publication in press details the formation of polymer luminescence as a generalized result of high levels of polymer degradation.53 This widely stimulated luminescence also emits widely, causing broad features that can interfere with precise spectral measurements.

Interestingly, the reported EVA luminescence results, induced by thermal degradation, correspond well to published EVA damp heat degradation Raman results.40 A Discussion & semi-gSEM Modeling 57

broad luminescence emission can be seen increasingly inﬂuencing the Raman spectra

of EVA in a PV module under damp heat, proceeding along with longer exposure

times. When placed on absolute wavelength scale, the broad feature is centered in

the 550-570 nm region, where a 512 nm stimulated emission is expected.

The Raman measurements in this work utilize a longer wavelength of incident

laser, which means they are not directly comparable to the aforementioned results.

The incident laser for the measurements reported here is 785 nm, which places the

absolute wavelength of our region of interest at 860-900 nm. It is possible that polymer

degradation luminescence emissions could both be stimulated and interfere with the

Raman spectra at these higher wavelengths, although they fall outside the reported

EVA degradation luminescence data.

Glass Peak Contribution. An alternate or complimentary explanation for the ob-

served degradation peak is related to the Raman spectra of the frontside glass material

of the PV mini-module samples. A confocal Raman measurement of only the glass

material yields a spectra that closely matches the location and shape of the degrada-

tion feature observed to appear in response to degradation (Fig. 5.5).

Figure 5.5. Raman spectra of frontside glass only. Discussion & semi-gSEM Modeling 58

This feature is notably broader and larger in magnitude than typical Raman

shifted emissions, suggesting that it is related to luminescence of the glass material in

response to the incident laser light. The measured glass-only spectra matches up well with the EVA spectra, though it overcuts the spectra at earlier measurement steps,

and undercuts the spectra at later measurement steps (Fig. 5.6). This suggests that

if the glass feature is contributing to the EVA spectra, it is doing so minimally before

degradation begins, and is only one of multiple contributors to spectral distortion at

later stages of degradation.

Figure 5.6. Glass-only spectra subtraction. Discussion & semi-gSEM Modeling 59

Figure 5.7. Exposure increases from top to bottom. Left to Right: Aggressive Cell Edge, Center, Non-Aggressive Cell Edge, Center.

Cell Edge vs. Center (Fig. 5.7). The size of the degradation feature can be seen

to increase more rapidly in the spectra of the cell edge, versus that of the cell center.

This is consistent with the expected results, and corresponds to increased moisture

availability at the cell edge leading to enhanced hydrolytic degradation processes.

Gridline Geometry Comparison (Fig. 5.7). The geometry of the cell gridlines

is not expected to inﬂuence the degradation of the EVA, but there is a slightly larger

observed increase in the degradation feature for aggressive samples. This is unlikely

to reﬂect an actual diﬀerence in the degradation of the EVA material, and is more

likely related to some inﬂuence of the sample geometry on the measurement process.

The previously observed increase in degradation occuring at the cell edge compared

to the cell center holds for non-aggressive samples as well. Discussion & semi-gSEM Modeling 60

5.2.2 EL Imaging

Figure 5.8. EL images, column sums, and brightness distributions.

Image Pixel Sum (Fig. 5.8). The image pixel sum can be seen to ﬂuctuate due

to overall brightness variation coming from an uncontrolled internal camera process.

Additionally a change in image color occurs in measurement rounds 5-7, and appears

to be due to a disproportionate decrease in the brightness of the red channel.

The decrease in pixel sum during the ﬁnal measurement round can be seen to

come from an overall decrease in the image brightness, as well as a changes in the

spatial features of the image. Column sum plots indicate that dark spots are forming

near the busbars, and that the leftmost cells in this sample are especially dim. Discussion & semi-gSEM Modeling 61

Figure 5.9. Images colored around mean threshold value. Blue indicates pixels above threshold, and red indicates pixels below.

Mean Threshold Sum Ratio (Fig. 5.9). The sum ratio can be seen to be sensitive

to ﬂuctuations in the overall image brightness,with the numerator term being heavily

inﬂuenced by the position of the center of the brightness distribution. When the image

gets brighter, the pixels above the mean all get brighter, increasing the numerator

term. The dim pixels below the mean may grow brighter also, but this increase is

comparatively too small to preserve the ratio. During the ﬁnal measurement round,

the ratio falls dramatically due to both the decrease in overall image brightness, as well as an increase in the number of pixels falling below the mean.

Mean Theshold Count Ratio (Fig. 5.9). The count ratio can be seen to be less

sensitive to image brightness ﬂuctuations. This indicates that pixels are not crossing

the mean image brightness, until the last round where this ratio is impacted. Discussion & semi-gSEM Modeling 62

Figure 5.10. Area sum ratio reﬂects observed image features.

Area Sum Ratio (Fig. 5.10). The increase in the area sum ratio can be seen to be due to the formation of dark features that grow outwards from the cell busbars.

Geometry Comparison (Fig. 5.11). The dark features near the busbars are still

present on cells with non-aggressive gridline geometries, but form more slowly.

Figure 5.11. Dark near-busbar features form more slowly on cells with non-aggressive gridline constructions. Discussion & semi-gSEM Modeling 63

5.2.3 I-V Curve Tracing

Figure 5.12. Variations in the lamp brightness impacted the parameters of the measured I-V curves, and corrected PMax results show apparent over-correction at high and low irradiance values.

Maximum Power Point as a System Performance Metric. The I-V curves in this study were impacted by variations in the lamp brightness of the solar simulator used for the measurement process. As described previously, temperature and irradiance correction was implemented to adjust for these variations, but they still impacted the ﬁnal results. In particular, large diﬀerences between the lamp brightness and the ideal 1000 W/m2 of 1 sun illumination could not be adequately corrected (Fig. 5.12). Discussion & semi-gSEM Modeling 64

It can be seen in the I-V curve results that the correction process over-corrects for especially large or small irradiances which are too far from the ideal. Thus it can be expected that the blue calculated corrected PMax values have been over-corrected to be too low, and the red values to be too high (Fig. 5.12). With this in mind, the expected degradation trend is still apparent.

Figure 5.13. Fill Factor inﬂuenced by slope changes near Isc and Voc.

Fill Factor Discussion (Fig. 5.13). Shifting I-V curves to a common Isc value reveals initial ﬁll factor changes from slope change near Voc (Voc increases), and later

ﬁll factor changes due to slope changes near Isc (curve sags at maximum power point). Discussion & semi-gSEM Modeling 65

5.3 Exploratory Data Analysis - Pairwise Plotting

5.3.1 Pair-Wise Scatter with Correlation

Confocal Raman Pairwise Correlation. The strongest correlations are seen between the CH/Deg and CO/Deg peak ratios, indicating that these normalized peak heights increase and decrease together in the dataset with variations in the size of degradation feature. In response to hours of damp heat, the normalized peak heights show negative trends, while the peak ratio shows a weak increasing trend.

The normalized CO peak height is always more strongly correlated to the CH/CO ratio than the normalized CH peak height, indicating that changes in the CO signal are driving changes in the peak ratio. Correlation trends appear to be consistent between the two cell gridline geometries (Fig. 5.14).

Figure 5.14. Confocal Raman CH/CO ratio pairwise correlation. Discussion & semi-gSEM Modeling 66

Normalizing the degradation feature to the three EVA polymer peaks yields vari-

ables that strongly correlate to hours of damp heat exposure. The variables also

correlate well to eachother, indicating that any of the EVA peaks could be used to

normalize the degradation feature (Fig. 5.15).

Of the three EVA peaks, the CHb peak stands out as consistently having the

highest correlation to hours of damp heat. The CHa peak is almost equivalent, and

in most cases these two variables correlate on a one to one basis.

The correlations to hours of damp heat are lower for the edge variables compared

to the center variables. This is likely due to an exponential trend in the edge variables, which is not captured by linear correlation.

Figure 5.15. Confocal Raman deg. ratio pairwise correlation. Discussion & semi-gSEM Modeling 67

EL Pairwise Correlation (Fig. 5.16). Strong correlations are seen between ex-

posure hours and area sum ratio, although a distinctly non-linear trend is apparent.

Correlations are not similarly present between exposure hours and total pixel sum,

indicating that the sum of the entire image is not a good indicator of stress response.

The total pixel sum is well correlated to both the sum and count threshold ratio variables, although only the count threshold ratio variable is correlated to hours of

damp heat exposure. The count threshold ratio is well correlated to the area sum

ratio, and could be used interchangeably as an indicator for degradation, although

the area sum ratio has a more meaningful underlying explanation that corresponds

to a speciﬁc observable eﬀect in the images.

Figure 5.16. EL Pairwise Correlation Scatter Plot. Discussion & semi-gSEM Modeling 68

I-V Pairwise Correlation (Fig. 5.17). The strongest correlation exists between hours and as-measured PMax, but as discussed earlier this apparent trend is caused by decreasing lamp power output. Fill factor shows similar correlations to both as- measured and corrected PMax. Correlations to corrected PMax can be improved by removing the round 2 data (Fig. 5.18), where a lamp change caused the irradiance to be higher than the correction formula could reasonably compensate for.

Figure 5.17. I-V variables pairwise correlation.

Figure 5.18. I-V pairwise correlation without round 2. Discussion & semi-gSEM Modeling 69

5.3.2 Pair-Wise Scatter with semi-gSEM Functional Form Fits

Aggressive Geometry (Fig. 5.19). Strong relationships are seen linking hours of

damp heat exposure to both the EVA degradation at cell center, and the EL area

sum ratio. The EVA degradation at cell edge is shown to be less strongly related to

hours of damp heat, and the EL area sum ratio, likely due to measurement variation.

EVA degradation at cell center shows a strong relationship to the EL area ratio, which in turn shows a relationship to corrected PMax. Altogether this highlights a

sequence of strong functional form relationships linking stress to system level response.

Figure 5.19. Pairwise functional form plots for aggressive sample geometry. Discussion & semi-gSEM Modeling 70

Non-Aggressive Geometry (Fig. 5.20). The data from non-aggressive gridline geometry samples shows very similar functional form ﬁts to the previously shown geometry. Interestingly, the EVA degradation at cell edge appears to be more strongly related to hours of damp heat exposure and EL area ratio than seen previously. The cell center degradation still shows a stronger trend to the EL area sum ratio.

The same sequence of relationships exists for non-aggressive samples. Hours of damp heat exposure leads to increasing EVA degradation at cell center, which leads to increasing EL area ratio, which leads to decreasing corrected PMax.

Figure 5.20. Pairwise func. form plots for non-aggressive sample geometry. Discussion & semi-gSEM Modeling 71

5.4 Semi-gSEM Modeling of Degradation Pathways 5.4.1 Semi-gSEM Model of Aggressive Mini-Modules (Fig. 5.21)

The semi-gSEM diagram of the aggressive gridline geometry samples shows the degra-

dation pathway observed in the functional form plots. The pathway of strongest

statistical functional form ﬁts leads from hours of damp heat exposure, to the EVA

degradation at cell center (AR2 = 0.92), to EL area sum ratio (AR2 = 0.8), to the

corrected PMax (AR2 = 0.64). A strong relationship exists between the EL area sum

ratio and the ﬁll factor, as well (AR2 = 0.64).

Figure 5.21. SgSEM model of aggressive sample geometry. Variables in yellow boxes described in previous sections (hrsDH = hours of damp heat exposure, degRatioEdge & degRatioCenter = confocal Raman spectroscopy deg/CHb peak ratios at cell edge and center, elAreaRa- tio = EL image area sum ratio, PMax = I-V curve maximum power point, fillFactor = I-V curve squareness). Strongest pathway observed from hrsDHdegRatioCenterelAreaRatioPMax. Discussion & semi-gSEM Modeling 72 values. 2 AR -0.0172 0 NA 0.22758 0 NA -0.00477 -0.13757 1.04346 0.63553 0.21866 2e-05 -0.25747 -10.13919 17.5251 0.00628 0.0027 0.06437 0.64 0.92 0.8 2) 0.68 -0.02506 0.92441 -0.29265 0.45557 0.00011 0.19074 2) ^ ^ 2) 0.79 0.02674 5.45096 -11.34555 0.23402 0 9e-05 ^ 2) 0.55 0.10148 5.59761 -11.50246 0.01526 0.00081 0.0156 ^ 2) ^ 2) 0.92 -0.03 2.20e-05 4.54e-08 0.23 0.58 4.18e-04 ^ 2) 0.49 0.0868 4.57298 NA 0.00848 0 NA 2) 0.82 0.02869 -0.00012 0 0.06799 0.00027 0 ^ ^ 2) 2) 0.49 -0.2726 -3.58365 NA 0.01277 2e-05 NA ^ 2) 0.71 0.00657 0.02838 NA 0.56752 0 NA 2) 0.61 -0.39072 0.00048 0 0.01833 0.07258 0.00274 ^ 2) 0.64 0.0576 0 NA 0.13646 0 NA ^ ^ ^ 2) 0.5 -0.38 -11.94 NA 2.65e-04 1.73e-05 NA ^ 2) 0.59 0.08914 0 NA 0.58301 0 NA ^ SQuad degRatioCenter˜I(hrsDH Quad degRatioCenter˜hrsDH+I(hrsDH Quad elAreaRatio˜degRatioCenter+I(degRatioCenter sQuad degRatioCenter˜I(elAreaRatio Exp degRatioCenter˜exp(elAreaRatio) 0.62 -1.38 1.45 NA 3.45e-07 4.51e-08 NA Quad PMax˜elAreaRatio+I(elAreaRatio SQuad PMax˜I(elAreaRatio Exp PMax˜exp(elAreaRatio) 0.59 3.33 -3.66 NA 1.52e-05 1.29e-06 NA values for the quadratic functional forms are higher, indicating a closer model fit to the measured 2 AR V1 V2 FF Model AR2 Intercept Coeff1 Coeff2 IntP PC1 PC2 The univariate relationships between the variables used in the semi-gSEM analysis can be described by the values shown 11b degRatioCenter hrsDH degRatioCenter hrsDH 23 degRatioCenter4 degRatioCenter degRatioEdge5 degRatioCenter elAreaRatio6 degRatioEdge Quad fillFactor7 degRatioEdge degRatioCenter˜degRatioEdge+I(degRatioEdge 8 hrsDH degRatioEdge Quad9 degRatioCenter˜elAreaRatio+I(elAreaRatio degRatioCenter degRatioEdge10 elAreaRatio elAreaRatio SL Exp11 elAreaRatio fillFactor elAreaRatio degRatioEdge˜exp(degRatioCenter) degRatioCenter˜fillFactor Quad SQuad hrsDH degRatioEdge˜I(hrsDH degRatioEdge degRatioCenter degRatioEdge˜elAreaRatio+I(elAreaRatio Exp SL degRatioEdge˜exp(fillFactor) elAreaRatio˜degRatioEdge Quad 0.69 elAreaRatio˜hrsDH+I(hrsDH -0.71118 0.78861 NA 0.51 0.06461 0 -0.14772 NA 0.23 0.45122 0 -0.26586 0.47 0.04997 NA -0.00966 0 0.26293 NA NA 1e-05 NA 0.00317 0.5919 NA 1e-05 NA 11b elAreaRatio degRatioCenter 11c elAreaRatio1213 elAreaRatio degRatioCenter 14 PMax15 PMax fillFactor PMax hrsDH SQuad elAreaRatio˜I(fillFactor degRatioEdge degRatioCenter SL SQuad PMax˜I(degRatioCenter Quad PMax˜degRatioEdge PMax˜hrsDH+I(hrsDH 0.19 -0.31961 -0.99608 NA 0.03283 0.01269 NA 16 PMax16b PMax16c PMax1718 PMax19 fillFactor elAreaRatio elAreaRatio 20 fillFactor21 elAreaRatio fillFactor fillFactor hrsDH fillFactor degRatioEdge degRatioCenter SL elAreaRatio Exp SL SQuad fillFactor˜exp(degRatioCenter) fillFactor˜degRatioEdge fillFactor˜I(hrsDH SL PMax˜fillFactor fillFactor˜elAreaRatio 0.52 2.71157 -2.75226 NA 0.22 -0.11536 -1.98078 NA 6e-05 0.64 0 0.57 -0.13886 -0.26285 0.61575 -8.51018 0.42766 0.00393 NA NA NA NA 0.30419 0 0.00848 0 NA NA values, the P-values of the exponential functional formslikely are to lower. be This falsely indicates positive, that which the could exponential indicate functional that forms are exponential less functional forms are preferrable despite lower above. The relationships sequenced in theare strongest highlighted path in from bold. the The incident strongest stressor functionalThe to exponential forms the functional of form system these is level relationships shown parameter also are (where of simplenote possible) interest quadratic that for (SQuad) these although or relationships, the for quadratic. comparison’s sake. It is interesting to Discussion & semi-gSEM Modeling 73

Figure 5.22. Aggressive Degradation Path. Following the strongest relationships between variables describing aspects of the aggressive gridline geometry PV mini module system, the pathway of degradation is indicated as shown in Figure 5.22. The coupling coeﬃcients between variables along the pathway are described by the following equations. Semi-gSEM Model Formula: Aggressive Module Geometry Step 1: EVA Degradation Predicted by Hours Damp Heat Exposure. degRatioCenter ∼ hrsDH [SQuad]

2 degRatioCenter ∼ I ∗ (hrsDH )

2 degRatioCenter ∼ intA + IA ∗ (hrsDH ) intA=-1.720e-02, IA=5.165e-08 degRatioCenter = (−1.720e−02) + (5.165e−08 ∗ (hrsDH2)) Step 2: Metallization Corrosion Predicted by EVA Degradation. elAreaRatio ∼ degRatioCenter [Quad]

2 elAreaRatio ∼ degRatioCenter + I ∗ (degRatioCenter )

2 elAreaRatio ∼ intB + CB ∗ degRatioCenter + IB ∗ (degRatioCenter ) intB=-4.774e-03, CB=-0.138, IB=1.043 elAreaRatio = (−4.774e−03) + (−0.138 ∗ degRatioCenter) + (1.043 ∗ (degRatioCenter2)) Step 3: PMax Loss Predicted by Metallization Corrosion. P Max ∼ elAreaRatio [Quad]

2 P Max ∼ elAreaRatio + I ∗ (elAreaRatio )

2 P Max ∼ intC + CC ∗ elAreaRatio + IC ∗ (elAreaRatio ) intC=-0.257, CC=-10.140, IC=17.525

P Max = (−0.257) + (−10.140 ∗ elAreaRatio) + (17.525 ∗ (elAreaRatio2)) Discussion & semi-gSEM Modeling 74

All-Together: PMax Loss Predicted by Hours Damp Heat Exposure. P Max ∼ hrsDH [Substitution]

2 1. degRatioCenter ∼ intA + IA ∗ (hrsDH )

2 2. elAreaRatio ∼ intB + CB ∗ degRatioCenter + IB ∗ (degRatioCenter )

2 3. P Max ∼ intC + CC ∗ elAreaRatio + IC ∗ (elAreaRatio )

2 2 2 P Max ∼ intC + CC ∗ (intB + CB ∗ (intA + IA ∗ (hrsDH )) + IB ∗ ((intA + IA ∗ (hrsDH )) )) + IC ∗ ((intB + CB ∗ (intA + IA ∗ (hrsDH2)) + IB ∗ ((intA + IA ∗ (hrsDH2))2))2)

The sequential steps along the aggressive degradation pathway combine via algebraic substitution to create equations predicting changes in all mechanistic variables.

The ﬁnal substituted equation describes system level power loss as a function of damp heat exposure, and is a system level model composed of sensible sub-models which all describe speciﬁc sub-system mechanistic degradation processes.

Figure 5.23. Aggressive model predictions. Discussion & semi-gSEM Modeling 75

Aggressive semi-gSEM Model Error Propagation.

Error propagation is applied, using the standard error (∆) of the coupling coeﬃcients. Table of Aggressive semi-gSEM Model Coeﬃcient Values

Coeﬃcient Value P-Value Std Err. (∆) 1 intA -1.72054e-02 0.22758 1.39591e-02 2 IA 5.16522e-08 1.34e-18 2.64678e-09 4 intB 2.67404e-02 0.23402 2.20014e-02 5 CB 5.45096 3.98465e-07 0.83748 6 IB -11.34555 9.27067e-05 2.50293 7 intC -0.25747 6.28074e-03 8.62957e-02 8 CC -10.13919 2.70052e-03 3.04523 9 IC 17.52510 6.43748e-02 9.05667

s ∆degRatioCenter r ∆IA 2 ∗ ∆hrsDH = ∆intA2 + ( ( )2 + ( )2)2 degRatioCenter IA hrsDH v u s s ∆elAreaRatio u ∆CB ∆degRatioCenter ∆IB 2 ∗ ∆degRatioCenter = t∆intB2 + ( ( )2 + ( )2)2 + ( ( )2 + ( )2)2 elAreaRatio CB degRatioCenter IB degRatioCenter

s ∆P Max r ∆CC ∆elAreaRatio r ∆IC 2 ∗ ∆elAreaRatioCenter = ∆intC2 + ( ( )2 + ( )2)2 + ( ( )2 + ( )2)2 P Max CC elAreaRatio IC elAreaRatio

Figure 5.24. Aggressive model error. Discussion & semi-gSEM Modeling 76

5.4.2 Semi-gSEM Model of Non-Aggressive Mini-Modules (Fig. 5.25)

The semi-gSEM diagram of the non-aggressive gridline geometry samples shows the

degradation pathway observed in the functional form plots. The pathway of strongest

statistical functional form ﬁts leads from hours of damp heat exposure, to the EVA

degradation at cell center (AR2 = 0.78), to EL area sum ratio (AR2 = 0.76), to the corrected PMax (AR2 = 0.64). A slightly weaker relationship exists between the EL area sum ratio and the ﬁll factor, as well (AR2 = 0.57).

Figure 5.25. SgSEM model of non-aggressive sample geometry. Vari- ables in yellow boxes described in previous sections (hrsDH = hours of damp heat exposure, degRatioEdge & degRa- tioCenter = confocal Raman spectroscopy deg/CHb peak ratios at cell edge and center, elAreaRatio = EL image area sum ratio, PMax = I-V curve maximum power point, fillFac- tor = I-V curve squareness). Strongest pathway observed from hrsDHdegRatioCenterelAreaRatioPMax. Discussion & semi-gSEM Modeling 77 2 AR -0.00414 1.7388 NA 0.46849 0 NA -0.14178 -17.26165 42.69574 0.11121 1e-05 0.00192 -0.00098 0 NA 0.93529 0 NA 0.78 0.79 0.64 2) 0.81 0.02689 0.62948 2.65517 0.08754 0.01205 0.00557 ^ 2) 0.77 -0.0083 0.82291 -0.42904 0.54599 1e-05 0.16146 ^ 2) 0.62 0.27734 -4.5668 -22.2214 0.15289 0.1286 0.05296 2) 0.76 0.01146 3.63652 -8.94195 0.34589 0 4e-05 2) 0.76 0.01146 3.63652 -8.94195 0.34589 0 4e-05 ^ ^ ^ 2) 0.83 0.02636 5.71241 -14.95133 0.0755 0 0 ^ 2) 0.57 0.21814 -37.83811 86.38474 0.28763 6e-05 0.00998 2) 0.48 0.07854 -0.04321 0.0108 0.00155 0.15213 0.17346 ^ ^ 2) , the quadratic functional form also shows lower p values, ^ 2) 0.77 -2.09e-03 2.73e-06 2.31e-08 0.91 0.93 0.03 2 ^ 2) 0.77 0.03724 -6e-05 0 0.16257 0.255 0.0013 ^ 2) 2) 0.62 0.01302 -5e-05 0 0.29308 0.04069 0.00084 ^ 2) 0.45 -0.30053 0.16904 -0.03215 0.00374 0.17053 0.30632 ^ AR ^ 2) 2) 0.54 -0.18471 -14.39868 NA 0.05908 0 NA ^ 2) 0.68 -0.36393 6e-04 0 0.01319 0.01346 0.00024 ^ ^ 2) 0.5 -0.38 -11.94 NA 2.65e-04 1.73e-05 NA ^ 2) 0.44 0.2559 0 NA 0.29786 2e-05 NA ^ 2) relationship, the ‘SQuad’ functional form is the strongest on the basis of the ^ SQuad degRatioCenter˜I(hrsDH Quad degRatioCenter˜hrsDH+I(hrsDH SQuad elAreaRatio˜I(degRatioCenter Quad degRatioCenter˜elAreaRatio+I(elAreaRatio Exp degRatioCenter˜exp(elAreaRatio) 0.55 -1.25 1.29 NA 1.97e-06 7.02e-07 NA Quad PMax˜elAreaRatio+I(elAreaRatio SQuad PMax˜I(elAreaRatio Exp PMax˜exp(elAreaRatio) 0.59 3.33 -3.66 NA 1.52e-05 1.29e-06 NA V1 V2 FF Model AR2 Intercept Coeff1 Coeff2 IntP PC1 PC2 The univariate relationships between the variables used in the semi-gSEM analysis can be described by the values shown 11b degRatioCenter hrsDH degRatioCenter hrsDH 23 degRatioCenter4 degRatioCenter degRatioEdge5 degRatioCenter elAreaRatio6 degRatioEdge Quad fillFactor7 degRatioEdge degRatioCenter˜degRatioEdge+I(degRatioEdge 8 hrsDH degRatioEdge Quad9 degRatioCenter˜elAreaRatio+I(elAreaRatio degRatioCenter degRatioEdge elAreaRatio elAreaRatio SL Quad fillFactor degRatioEdge˜degRatioCenter+I(degRatioCenter degRatioCenter˜fillFactor Quad Quad hrsDH degRatioEdge˜elAreaRatio+I(elAreaRatio degRatioEdge˜hrsDH+I(hrsDH Quad degRatioEdge˜fillFactor+I(fillFactor Quad elAreaRatio˜hrsDH+I(hrsDH 0.58 0.04637 -0.06133 NA 0.00209 0 NA 1011 elAreaRatio elAreaRatio degRatioEdge degRatioCenter Exp elAreaRatio˜exp(degRatioEdge) 0.59 -0.21935 0.2155 NA 0 0 NA 11b elAreaRatio11c elAreaRatio12 degRatioCenter 13 elAreaRatio degRatioCenter 14 PMax15 PMax fillFactor PMax hrsDH SL degRatioEdge degRatioCenter elAreaRatio˜fillFactor SL SQuad PMax˜I(degRatioCenter Quad PMax˜degRatioEdge PMax˜hrsDH+I(hrsDH 0.48 0.01415 -0.02879 NA 0.53 -0.13196 -2.6408 0.08625 NA 1e-05 NA 0.20516 1e-05 NA 16 PMax16b PMax16c PMax17 PMax elAreaRatio elAreaRatio elAreaRatio fillFactor Quad PMax˜fillFactor+I(fillFactor 1819 fillFactor20 fillFactor fillFactor hrsDH degRatioEdge degRatioCenter Exp Quad fillFactor˜degRatioCenter+I(degRatioCenter fillFactor˜exp(degRatioEdge) SQuad fillFactor˜I(hrsDH 0.48 5.01528 -4.78752 NA 3e-05 1e-05 NA 21 fillFactor elAreaRatio Quad fillFactor˜elAreaRatio+I(elAreaRatio indicating a lower probability of false positive error and making this an attractive functional form decision. above. The relationships sequenced inare the highlighted strongest in bold. path from TheFor the strongest the incident functional elAreaRatio˜I(degRatioCenter stressor forms to of these thevalue. relationships system are For level the simple parameter purposes quadratic of (SQuad) of interest functional or model form quadratic. comparison, was however, used it instead. is desireable With for only the a functional slightly forms lower to be identical, so the ‘Quad’ Discussion & semi-gSEM Modeling 78

Figure 5.26. Non-Aggressive Degradation Path. Following the strongest relationships between variables describing aspects of the non-aggressive gridline geometry PV mini module system, the pathway of degradation is indicated as shown in Figure 5.26. The coupling coeﬃcients between variables along the pathway are described by the following equations. Semi-gSEM Model Formula: Non-Aggressive Module Geometry Step 1: EVA Degradation Predicted by Hours Damp Heat Exposure. degRatioCenter ∼ hrsDH [SQuad]

2 degRatioCenter ∼ I ∗ (hrsDH )

2 degRatioCenter ∼ intA + IA ∗ (hrsDH ) intA=-9.791e-04, IA=2.389e-08 degRatioCenter = (−9.791e−04) + (2.389e−08 ∗ (hrsDH2)) Step 2: Metallization Corrosion Predicted by EVA Degradation. elAreaRatio ∼ degRatioCenter [Quad]

2 elAreaRatio ∼ degRatioCenter + I ∗ (degRatioCenter )

2 elAreaRatio ∼ intB + CB ∗ degRatioCenter + IB ∗ (degRatioCenter ) intB=1.146e-02, CB=3.637, IB=-8.942 elAreaRatio = (1.146e−02) + (3.637 ∗ degRatioCenter) + (−8.942 ∗ (degRatioCenter2)) Step 3: PMax Loss Predicted by Metallization Corrosion. P Max ∼ elAreaRatio [Quad]

2 P Max ∼ elAreaRatio + I ∗ (elAreaRatio )

2 P Max ∼ intC + CC ∗ elAreaRatio + IC ∗ (elAreaRatio ) intC=-0.142, CC=-17.262, IC=42.696

P Max = (−0.142) + (−17.262 ∗ elAreaRatio) + (42.696 ∗ (elAreaRatio2)) Discussion & semi-gSEM Modeling 79

All-Together: PMax Loss Predicted by Hours Damp Heat Exposure. P Max ∼ hrsDH [Substitution]

2 1. degRatioCenter ∼ intA + IA ∗ (hrsDH )

2 2. elAreaRatio ∼ intB + CB ∗ degRatioCenter + IB ∗ (degRatioCenter )

2 3. P Max ∼ intC + CC ∗ elAreaRatio + IC ∗ (elAreaRatio )

2 2 2 P Max ∼ intC + CC ∗ (intB + CB ∗ (intA + IA ∗ (hrsDH )) + IB ∗ ((intA + IA ∗ (hrsDH )) )) + IC ∗ ((intB + CB ∗ (intA + IA ∗ (hrsDH2)) + IB ∗ ((intA + IA ∗ (hrsDH2))2))2)

The sequential steps along the non-aggressive degradation pathway combine via algebraic substitution to create equations predicting changes in all mechanistic variables. The ﬁnal substituted equation describes system level power loss as a function of damp heat exposure, and is a system level model composed of sensible sub-models which all describe speciﬁc sub-system mechanistic degradation processes.

Figure 5.27. Non-Aggressive model predictions. Discussion & semi-gSEM Modeling 80

Non-Aggressive semi-gSEM Model Error Propagation.

Error propagation is applied, using the standard error (∆) of the coupling coeﬃcients. Table of Non-Aggressive semi-gSEM Model Coeﬃcient Values

Coeﬃcient Value P-Value Std Err. (∆) 1 intA -9.79109e-04 0.93529 1.19585e-02 2 IA 2.38876e-08 1.34040e-11 2.26744e-09 4 intB 1.14569e-02 0.34589 1.19570e-02 5 CB 3.63652 2.09880e-08 0.47705 6 IB -8.94195 4.33858e-05 1.86025 7 intC -0.14178 0.11121 8.58648e-02 8 CC -17.26165 1.28025e-05 3.18805 9 IC 42.69574 1.92237e-03 12.31838

s ∆P Max r ∆CC ∆elAreaRatio r ∆IC 2 ∗ ∆elAreaRatioCenter = ∆intC2 + ( ( )2 + ( )2)2 + ( ( )2 + ( )2)2 P Max CC elAreaRatio IC elAreaRatio

Figure 5.28. Non-Aggressive model error. Discussion & semi-gSEM Modeling 81

5.5 Domain Knowledge - Observed Causal Mechanisms

In a well known hydrolysis reaction, a molecule of water splits the carbonyl bond to

produce EVOH copolymer (in this case a hydroxyl side group), and a molecule of

free acetic acid.34–37 This represents a reduction in the amount of carbonyl linkages

bonding acetate side groups to the vinyl acetate segments of the EVA copolymer.

As the acetic acid, which now contains the carbonyl functionality, diﬀuses out of the

system or is consumed as a reactant in corrosion processes40,41, the confocal Raman

peak magnitude corresponding to the carbonyl group was expected to decrease relative

to the magnitude of the CH peak corresponding to the main chain ethylene segments.

A change in this ratio, representing a reduction in the carbonyl content of the polymer, was not observed to occur in the measured Raman spectra of this study. Instead, a loss

in relative Raman signal strength and the formation of a broad polymer luminescence

feature at later stages of polymeric degradation due to damp heat exposure, obscured

the Raman signal from the EVA material, and thus the CH/CO peak ratio.

Instead, the process of EVA hydrolytic degradation was observed in the mini-

module samples via confocal Raman microscopy as a relative increase in the magni-

tude of a degradation feature relative to EVA peaks in the spectra. Utilizing polymer

degradation induced luminescence features as an indication of degradation in EVA

encapsulant under damp heat has precedent in literature40,43,52,53, as the broad emission region can obscure many features of the spectra. The hydrolytic decomposition of EVA encapsulant in PV modules under damp heat to produce acetic acid is commonly observed38,39, and it can be inferred that the observed degradation feature is closely related to this process. Discussion & semi-gSEM Modeling 82

Metallization corrosion was observed in the mini-module samples via electrolumi- nescent imaging as the growth of dark regions on the cell surface immediately adjacent to the busbars. This represents a loss of recombination events generating photons in the silicon cell surface areas in these regions, which indicates a loss of gridline connectivity to the silicon cell surface locally within these regions. Interestingly there is not a total severing of the gridline conductivity, as there are always active regions beyond the initial dark region adjacent to the busbar. This occurs even when dark regions exist on both sides of the active region separating it from a direct path to any busbar whatsoever, suggesting that the loss of gridline connectivity to the cell surface is conﬁned to the gridline/cell interface.

The formation of these dark regions is observed to be related to the progress of the EVA hydrolysis at the cell center. This supports the participation of acetic acid in metallization corrosion processes, as reported in literature observations of acetate- containing precipitates forming on (and underneath) silver paste gridlines.40,41 It is hypothesized that this process could proceed more rapidly, or byproducts precipitate and accumulate more severely, at the busbar/gridline intersections due to increased metallization surface area in these areas. Aggressive modules had more gridlines, and thus more busbar/gridline intersections, in addition to smaller initial gridline cross sectional areas. Both of these characteristics would make the aggressive modules more vulnerable to the proposed mechanism of metallization corrosion byproducts, driven by the availability of acetic acid, undercutting the gridline/cell surface interface. Starting with smaller cross sections and more numerous vulnerable busbar/gridline intersections, dark regions would be expected to form more quickly and severely in response to damp heat exposure in the EL images of aggressive modules. Discussion & semi-gSEM Modeling 83

System level power loss was observed by irradiance and temperature corrected maximum power measurements from the I-V curve tracing technique. This loss in module level electrical properties was observed to occur after an initial slow period of incubation, corresponding to the rate of progress of the observed hydrolysis and metallization corrosion processes. Losses to system level electrical performance properties cross the previously observed change point, showing signs of power loss related directly to loss of cell surface connectivity in screen printed silver gridlines due to accumulating metallization corrosion byproducts.

All together, the observed degradation pathway is composed of three coupled degradation mechanisms, initiated by exposure to damp heat. High temperatures and moisture availability drive the hydrolytic decomposition of EVA encapsulant material, generating free acetic acid within the PV module assemblies. The acetic acid participates in a metallization corrosion process which speciﬁcally targets the gridline/cell surface interfaces of gridlines near their intersections to the cell busbars. Aﬄicted gridlines, undercut by non-conductive barriers of growing corrosion byproduct precipitates, are eventually electrically disconnected from the silicon cell surface in localized near-busbar regions, though they remain electrically connected to ‘active’ regions beyond them. Eventually ‘deactivated’ cell surface regions surrounding disconnected sections of gridline are seen to grow rapidly, driven by the increasing availability of acetic acid as moisture levels equilibrate from the edge to the center of the cell. System level electrical properties are severely impacted by a loss of photo-generated current corresponding to the increasing percentage of electrically disconnected cell surface area. In these regions, recombination events claim the photo-generated charge carriers that nearby disconnected gridlines cannot collect. Discussion & semi-gSEM Modeling 84

5.6 Sample Construction Geometry Comparison

A hypothesis in line with the proposed degradation mechanisms is that the ‘aggres-

sive’ sample geometry, featuring smaller initial gridline cross sections, will be more

susceptible to increases in gridline resistivity caused by induced metallization cor-

rosion. This would appear as comparable levels of observed acetic acid generation within the two sample types leading to a more rapid onset of metallization corrosion,

and subsequent power loss, in aggressive geometries with smaller gridlines, as opposed

to non-aggressive geometries with larger gridlines. The reasoning behind this is that

given the corrosion of diﬀerently sized gridlines, proceeding at identical rates, a faster

increase the resistivity of gridlines with smaller starting areas would be expected

due to a higher proportion of damaged to non-damaged gridline cross sectional area.

The impact of non-conductive precipitates, formed by corrosion processes, would be

more severe to smaller gridlines, as they would more rapidly build up to represent a

signiﬁcant fraction of the total eﬀective conductive area.

Measured data showed that EVA hydrolysis progress appeared to be close between

the two sample types, indicating a comparable level of acetic acid availability between

the two sample geometries. Metallization corrosion appeared to be indeed enchanced

in the aggressive gridline geometry samples, as observed by a more rapid increase in

the EL area ratio of the aggressive samples, indicating a faster rate of metallization

corrosion degradation. System level power loss appeared to be close between the two

exposed sample types at this stage of degradation, but could be expected to reﬂect

the observed EL diﬀerences at higher levels of degradation. Discussion & semi-gSEM Modeling 85

5.7 PV Module Change Point Phenomenon

A characteristic of this degradation pathway is a slow initial degradation rate, marked

by a distinct change point after which rapid degradation occurs. This change point

phenomenon was observed both in the initial UL study results and in the following

L&DS dataset collected at the SDLE research center. The proposed cause of this

phenomenon is that rapid power loss does not occur until the accumulated level of

metallization corrosion byproducts crosses a critical threshold after which gridlines

begin to be electrically disconnected from the surrounding silicon cell surface.

Initially the gridline resistivity is not signiﬁcantly impacted by negligible levels

of metallization corrosion byproduct precipitates creating relatively small losses in

the eﬀective conductive cross section along the gridlines. Eventually, the build-up

of precipitating corrosion byproducts, especially at gridline/cell surface interfaces,

becomes large enough to reduce the eﬀective conductive cross section and create a

localized barrier to current ﬂow at locations of high precipitate concentration. When

suﬃcient barriers accumulate along a gridline at the gridline/cell surface interface,

the gridline is electrically disconnected from the surrounding silicon cell surface. This

leads to system level power loss when signiﬁcant portions of the entire available silicon

cell area are electrically disconnected in this way, and excluded from participating in

the photovoltaic power generation process.

Metallization corrosion in the L&DS dataset is related to the progress of the EVA

hydrolysis reaction at the center of the PV cell, indicating that acetic acid is a rate

limiting reagent in the corrosion process. The availability of acetic acid, in turn, is

limited by the availability of moisture, which is a consumed reactant in the EVA

hydrolysis process. Water has a high solubility in bulk EVA material, but must travel Discussion & semi-gSEM Modeling 86 a long diffusion path between two impermeable surfaces to reach the center of the PV cell. EVA hydrolysis at the cell center was observed to be more strongly related to the onset of metallization corrosion indicated by the EL data. Although EVA hydrolysis at the cell edge occured more rapidly and built to higher levels, it was found to be less related to the cell-wide metallization corrosion. This suggests that the presence of acetic acid across the PV cell face, initially impacted by the limited availability of moisture due to diffusion effects and its being consumed as a reactant, is important for observable and impactful amounts of metallization corrosion to occur.

In this way the occurance of the change point, offset from the beginning of the stress exposure, is dependant on a series of effects intiated by an influx of moisture at high temperatures. First sufficient moisture must diffuse through the bulk EVA encapsulant between two impermeable surfaces across the cell face, in towards the cell center, as it is being consumed by hydrolytic degradation reactions in the EVA material. Driven by moisure availability, enough acetic acid must be generated by the EVA hydrolysis to facilitate sufficient metallization corrosion in the screen printed silver gridlines to create an impactful reduction in conductive cross section along the gridlines at the gridline/cell surface interface. Finally the changepoint is realized as a level of metallization corrosion byproducts is accumulated that exceeds a threshold, beyond which the electrical disconnection of silicon cell regions occurs directly as a result of further corrosion, creating an observable change in the rate of power loss. 87

6 Conclusions

The mechanistic degradation pathway of screen-printed silver gridline (SP-Ag)

corrosion in PV modules exposed to damp heat stress conditions was investigated

using a novel analysis methodology developed as part of this work, semi-gSEM. A

linked causal sequence of degradation modes was indicated by statistical analytics and

conﬁrmed by domain knowledge, and the underlying mechanisms were elucidated.

The degradation of EVA via hydrolysis under damp heat conditions was observed

non-destructively with confocal raman microscopy, and the corresponding formation

of acetic acid was inferred. This was shown to occur more rapidly at the cell edge,

and the cell center remained less degraded at the ﬁnal time step of this study. This

eﬀect is attributable to decreased moisture availability along the diﬀusion path to the

center of the cell, as it is consumed as a reagent in the hydrolysis reaction.

The eﬀects of metallization corrosion on the local electrical properties of PV mod-

ules were observed through the quantitative analysis of EL images. Loss of brightness

in dark regions growing outwards from the busbars, with bright regions remaining be-

tween them, indicated a localized loss of connectivity between the gridlines and the

cell surface. This is attributable to the accumulation of corrosion by-product precip-

itates reducing the eﬀective conductive cross-section at the gridline-cell interface in

these regions. This was observed to occur more rapidly in samples with aggressive

(thin) gridline construction, which began with small initial conductive cross-sections.

Acetic acid formation at the cell center was observed to be more closely linked to

cell-wide corrosion processes than acetic acid generated at the cell edge. This suggests

that acetic acid must be present across the cell surface in order for cell-wide corrosion Conclusions 88

of the SP-Ag gridlines to proceed. This indicates the participation of acetic acid

in the metallization corrosion process, either as a consumed reagent or by catalytic

enhancement of other moisture induced corrosion processes.

Metallization corrosion was observed to begin to impact the overall electrical per-

formance properties with an increased rate after an initial incubation period spent at

a lower rate. A previously observed strong relationship between EVA hydrolysis and

PV module power loss also displayed this change point behavior, and the increasing

of gridline resistances due to metallization corrosion is an apparent mechanistic link

between these two degradation modes. A critical threshold of corrosion byproduct

precipitates, which needs to be exceeded before electrical properties begin to be im-

pacted, would explain this observed phenomenon. The time required to exceed this

critical threshold would be directly impacted by the availability of moisture, the rate

of accumulation of acetic acid, and the starting cross-sectional area of the gridlines.

This challenges the traditional thinking of simple, single rate values describing linear

degradation through time of PV module electrical properties.

The linked causal sequence of degradation modes and mechanisms, shown with

statistical relationships and informed by domain knowledge, can be described as fol-

lows. Damp heat stress conditions cause the generation of acetic acid within the

mini-module samples through hydrolytic decomposition of the EVA copolymer ma-

terial. The acetic acid facilitates metallization corrosion, reducing the conductive

contact area between the screen printed silver gridlines and the silicon cell surface.

When acetic acid buildup and the resulting metallization corrosion reach a critical

threshold, regions of electrically disconnected silicon cell surface area grow outward

from near the bus bars, resulting in rapid power loss with further degradation. 89

7 Future Research

The experiments and analysis shown here establish a compelling model of PV

module degradation under damp heat, upon which further research can build. Semi-

gSEM has been designed as a methodology that beneﬁts from the steady accumulation

of additional data from further experiments, to verify and extend the predictive power

of its degradation pathway models. With that in mind, many attractive future re-

search paths are indicated that build upon the PV degradation model shown in this work to extend it into additional technologies and degradation scenarios.

Screen printed silver gridlines are standard PV cell technology, and their wide-

spread adoption make them an ideal starting point for PV degradation semi-gSEM

modeling eﬀorts. Exciting new variations on this cell construction theme, such as ‘pas-

sivated emitter rear cell’ (PERC) technology for example, hold promise for module

eﬃciency gains, but may introduce alternate degradation mechanisms and pathways

not yet captured into the current iteration of the semi-gSEM degradation pathway

model. Future research focusing on alternate cell constructions, such as PERC cells, would be an excellent direction for extending the semi-gSEM degradation pathway

model to encompass incoming cutting edge technologies.

Damp heat was chosen as the stressor for the work shown here, partially to extend

the work of the UL researchers and relate directly to the dataset they provided, but

also because of its widespread (but questionable) use for PV module qualiﬁcation

testing. It can be easily pointed out that the damp heat conditions of 85◦C 85%

relative humidity (r.h.) do not exist anywhere on the surface of the earth. It is Future Research 90

currently unknown if realistic degradation mechanisms and pathways are being ac-

tivated, which modules could be expected to exhibit over long term ﬁeld use, or if

unique and impossible routes to performance loss are being artiﬁcially created by the

unusually harsh conditions of damp heat testing. Accelerated testing is required to

evaluate module technologies on reasonable time scales, but a better understanding

of what degradation mechanisms and pathways exist in these systems is needed, to

ensure that technology is not being wildly over-designed to meet unnecessary crite-

ria, or worse, remaining ignorant of important weaknesses that actually need to be

addressed. Future research applying the semi-gSEM methodology to existing data on

ﬁelded modules over long timescales, and L&DS studies designed with outdoor ex-

posed modules experiencing real world stressors, is an essential next step to enable the

comparison of laboratory induced PV module degradation to that which is caused by

the real world. Both of these approaches are underway at the SDLE research center.

An aspect of laboratory testing that is both good and bad is the isolation of a

small number of speciﬁc stressors into simple focused exposure types. This is good

for investigating the eﬀects of one or two stressors in isolation, but it is again a

situation that is not experienced by a real world PV module in use, where there is

a constant interplay of degradation mechanisms initiated by stressors of all kinds,

creating complex pathways of performance degradation. Future research could utilize

multifactor exposure equipment that simultaneously applies both heat and moisture

(damp heat) as well as irradiance (UV) to re-create these interactions in a controlled

laboratory setting. The SPHS100 chamber at the SDLE research center is one such

piece of equipment, and its development was assisted by the author of this work with

this eventual purpose in mind. Appendix 91

Appendix A

This work was featured in public presentations and refereed publications.

Presentations

Wheeler, Nicholas R. Combining Multiple Data Types for Lifetime and Degra-

dation Science of PV Modules. Poster Presentation, CWRU Engineering Week

Banquet, February 25, 2016.

Wheeler, Nicholas R. Screen Printed Silver Corrosion in Photovoltaic Modules: A

Data Science Approach to Understanding Material Interactions in Energy Tech-

nology Systems. Talk presented at the CWRU/Tohoku University Data Science

Symposium in Life Sciences and Engineering, Case Western Reserve University,

July 30, 2015.

Wheeler, Nicholas R. Lifetime and Degradation Science of EVA and Front-Side

Silver in PV Modules. Poster Presentation, UL 2015 Annual Meeting, April 2015.

Wheeler, Nicholas R., Yifan Xu, Abdulkerim Gok, Ian Kidd, Laura S. Bruckman,

Jiayang Sun, and Roger H. French. Data Science Study Protocols for Investigating

Lifetime and Degradation of PV Technology Systems. Talk presented at IEEE

PVSC 40. Denver, CO, 2014.

Wheeler, Nicholas R., Yifan Xu, Abdulkerim Gok, Ian V. Kidd, Laura S. Bruck-

man, Jiayang Sun, and Roger H French. Developing Statistically Informed Study

Protocols. Poster presented at the Research ShowCase, Case Western Reserve,

April 18, 2014. Appendix 92

Publications - Proceedings

Wheeler, Nicholas R., Abdulkerim Gok, Timothy J. Peshek, Laura S. Bruckman,

Nikhil Goel, Davis Zabiyaka, Cara L. Fagerholm, et al. A Data Science Ap-

proach to Understanding Photovoltaic Module Degradation. In Proc. of SPIE,

9563:95630L95630L6. SPIE, 2015. doi:10.1117/12.2209204.

Wheeler, Nicholas R., Laura S. Bruckman, Junheng Ma, Ethan Wang, Carl K.

Wang, Ivan Chou, Jiayang Sun, and Roger H. French. Statistical and Domain

Analytics for Informed Study Protocols. 1–7. IEEE, 2013.

doi:10.1109/EnergyTech.2013.6645354.

Wheeler, Nicholas R., Laura S. Bruckman, Junheng Ma, Ethan Wang, Carl K.

Wang, Ivan Chou, Jiayang Sun, and Roger H. French. Degradation Pathway Mod-

els For Photovoltaics Module Lifetime Performance. In IEEE PVSC 39. Tampa,

FL, 2013. doi:10.1109/PVSC.2013.6745130.

Bruckman, Laura S., Nicholas R. Wheeler, Ian V. Kidd, Jiayang Sun, and Roger

H. French. Photovoltaic Lifetime and Degradation Science Statistical Pathway De-

velopment: Acrylic Degradation. edited by Neelkanth G. Dhere, John H. Wohlge-

muth, and Kevin W. Lynn, 88250D, 2013. doi:10.1117/12.2024717. Appendix 93

Publications - Refereed

Fada, Justin S., Nicholas R. Wheeler, Davis Zabiyaka, Nikhil Goel, Timothy J.

Peshek, and Roger H. French. Democratizing an Electroluminescence Imaging

Apparatus and Analytics Project for Widespread Data Acquisition in Photovoltaic

Materials. Review of Scientiﬁc Instruments 87, no. 8 (August 1, 2016): 085109.

doi:10.1063/1.4960180.

Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K.

Wang, Ivan Chou, Jiayang Sun, and Roger H. French. Statistical and Domain

Analytics Applied to PV Module Lifetime and Degradation Science. IEEE Access

1 (2013): 384–403. doi:10.1109/ACCESS.2013.2267611.

Roger H. French, Rudolf Podgornik, Timothy J. Peshek, Laura S. Bruckman,

Yifan Xu, Nicholas R. Wheeler, Abdulkerim Gok, et al. Degradation Science:

Mesoscopic Evolution and Temporal Analytics of Photovoltaic Energy Materials.

Current Opinion in Solid State and Materials Science, Opportunities in Mesoscale

Science, 19, no. 4 (August 2015): 21226. doi:10.1016/j.cossms.2014.12.008. Appendix 94

Appendix B

Free and Open Source Software Tools

1 Analytical software packages

The majority of digital work for this research was done in the Kubuntu Linux distribution computing environment, using a variety open source software tools. Data handling and analysis was performed using R statistical computing language and

Python. Manual graphics creation and editing was done using ImageJ, Dia, and

ImageMagick. The (free) programs utilized are as follows:

• Ubuntu Linux Operating System:

Kubuntu Linux Distribution

http://kubuntu.org/

• R Statistical Programming Language:

R: The R Project for Statistical Computing

https://www.r-project.org/

RStudio Graphical User Interface

https://www.rstudio.com/

• Python Programming Language:

Python

https://www.python.org/ Appendix 95

• Manual Graphics Creation and Editing:

ImageJ

https://imagej.nih.gov/ij/

Dia Diagram Editor

https://sourceforge.net/projects/dia-installer/

ImageMagick

http://www.imagemagick.org/

2 Preparation of this document

This document was prepared using pdfLATEX and other open source tools. The (free) programs implemented are as follows:

• LATEX implementation:

MiKTEX, TEXLive http://www.miktex.org/, https://www.tug.org/texlive/

• TEX-oriented editing environments: Vim Text Editor

https:http://www.vim.org/

• Bibliographical:

BibTEX http://www.bibtex.org/

Zotero

https://www.zotero.org/ Bibliography 96

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