NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Technology and Climate Trends 

in PV Module Degradation

Preprint

Dirk C. Jordan, John H. Wohlgemuth, 

and Sarah R. Kurtz

Presented at the 27th European Photovoltaic Solar Energy 

Conference and Exhibition

Frankfurt, Germany

September 24–28, 2012

Conference Paper

NREL/CP-5200-56485

October 2012
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1

TECHNOLOGY AND CLIMATE TRENDS IN PV MODULE DEGRADATION

D.C. Jordan*, J.H. Wohlgemuth, S.R. Kurtz

National Renewable Energy Laboratory (NREL), 1617 Cole Blvd., Golden, CO 80401

*Tel: 1-303-384-6762; Fax: 1-303-384-6790; email: dirk.jordan@nrel.gov

ABSTRACT: To sustain the commercial success of photovoltaic (PV) technology it is vital to know how power output 

decreases with time. Unfortunately, it can take years to accurately measure the long-term degradation of new products, but 

past experience on older products can provide a basis for prediction of degradation rates of new products. An extensive 

search resulted in more than 2000 reported degradation rates with more than 1100 reported rates that include some or all IV 

parameters. In this paper we discuss how the details of the degradation data give clues about the degradation mechanisms and 

how they depend on technology and climate zones as well as how they affect current and voltage differently. The largest 

contributor to maximum power decline for crystalline Si technologies is short circuit current (or maximum current) 

degradation and to a lesser degree loss in fill factor. Thin-film technologies are characterized by a much higher contribution 

from fill factor particularly for humid climates. Crystalline Si technologies in hot & humid climates also display a higher 

probability to show a mixture of losses (not just short circuit current losses) compared to other climates. The distribution for 

the module I-V parameters (electrical mismatch) was found to change with field exposure. The distributions not only 

widened but also developed a tail at the lower end, skewing the distribution.

Keywords: Degradation Rates, PV Module, Performance, Photovoltaic Systems, Field Testing, Mismatch

1 INTRODUCTION

The commercial success of the photovoltaic (PV) industry has 

benefited from the use of accelerated stress testing to identify infant 

mortality problems. [1] As the industry matures, there is an 

increasing interest and need to go beyond qualification testing and 

quantify wear out mechanisms that may cause slow degradation and 

lead to failures[2]. Over the years, dozens of failure mechanisms 

have been reported. Browning or discoloration of modules can be 

some of the most obvious signs of degradation, but, failures of 

electrical connections may cause greater decrease in module output.

[3] Accurate quantification of power decline over time requires an

understanding of all degradation mechanisms including 

delamination, broken interconnects or cells, corrosion, broken glass, 

ground faults, and increases in shunting.[2] Some types of wear-out 

processes cause a steady loss of power (e.g. browning of 

encapsulant materials); others may show stable performance 

followed by a catastrophic failure (e.g. broken glass, followed by 

module failure because of the loss of integrity of the package); 

while some may show early drop in performance followed by more 

stable performance (e.g. light-induced degradation). [4]

At the system level, not all degradation mechanisms are 

equivalent (current and voltage degradation affect system 

performance differently), and not all modules degrade at the same 

rate with a single underperforming module affecting the entire 

string. Thus, understanding not only the degradation rates but, also, 

the width and shape of the distribution is important. The widths of 

these distributions determine the electrical mismatch and has been 

investigated for fielded arrays. [5] The broadening of I-V 

parameters after field exposure has been noted before. [6,7].

A previous study summarized more than 2000 degradation rates, 

concluding that the average degradation rate was < 1%/y for most 

products manufactured after the year 2000, with some statistical 

variation for some technology types, especially for products 

manufactured before the year 2000. [8]

This study builds on the previous study by analyzing the 

changes reported for the various device parameters - short-circuit 

current (Isc), open-circuit voltage (Voc) and fill factor (FF) - and 

how these vary with technology and climate zone. The possible 

implications of these results are discussed, especially in terms of 

how the module degradation may affect system performance and 

what degradation mechanisms may be dominating the field 

experience, providing a basis for prioritization of design of 

accelerated tests.

Figure 1: Histogram of published degradation rates (blue bars) with 

an extreme value distribution fit (red line) (a), and partitioned by 

technology and date of installation (b). The number in each 

category indicates the number of data points. The 95% confidence 

interval is denoted by the diamonds with the mean as the crossbar.

2 RESULTS

An extensive search resulted in more than 2000 PV degradation 

rates (Rd) quoted in publications and locations worldwide. The 
2

number of cited rates is large enough to allow grouping for 

installation before and after the year 2000, and by technology. Fig. 

1 (a) provides an updated histogram with an extreme value 

distribution fit that will be discussed in the second subsection. Fig. 

1 (b) shows the maximum power degradation partitioned by 

technology and date of installation. [8] The number in each 

category indicates the number of data points. The 95% confidence 

interval is denoted by the diamonds with the mean as the crossbar. 

Crystalline Si technologies (x-Si) appear to have remained steady at 

rates of approximately 0.5%/year for installations before and after

the year 2000. However, thin-film technologies showed a 

significant move towards stability for post 2000 installations.

More than half of these compiled rates (ca.1100) contain 

information on at least some I-V parameters in addition to 

maximum power (Pmax) such as maximum-power-point current 

(Imax) and maximum-power-point voltage (Vmax), Isc, Voc and 

FF. The paper is organized in 3 subsections analyzed by technology, 

distribution and climate.

2.1 I-V Parameter Degradation

Figure 2: Degradation rates of the maximum-power-point values 

for power, current and voltage for mono-Si (a), multi-Si (b). As a 

guide for the eye, dashed lines indicate no degradation. A negative 

degradation implies improvement.

Figure 2 shows the annualized degradation rate for Pmax, Imax

and Vmax partitioned by technology. Despite the scatter for mono-

(a) and multi-Si (b), the predominant decline appears to be in 

current not in voltage. This is an important consideration for proper 

inverter sizing. Similar data for thin-film technologies are not 

shown because of the small number of data points.

Of further interest is how IV parameter degradation differs by 

technology. Fig. 3 shows Pmax, Isc, Voc and FF degradation for 

crystalline Si technologies (a) and (b) and thin-film (c). Due to the 

low number of data points the thin-film technologies amorphous 

silicon (a-Si), copper indium gallium (di)selenide (CIGS) and 

cadmium telluride (CdTe) have been overlaid on one plot. Mono-Si 

and multi-Si display a similar pattern in which the highest Pmax 

degradation is most closely correlated with Isc, followed by FF and 

finally Voc, which degrades little. Typical observed Isc degradation 

can be attributed to delamination, discoloration and cracked 

individual cells while a smaller percentage can be attributed to 

light-induced degradation and soiling. [9,10] Significantly less 

degradation comes from FF, typically associated with corrosion and 

solder-bond breakage. The pattern differs for thin-film technologies

in Fig. 3 (c) despite a clustering effect by technology. All three thinfilm technologies show a significantly higher FF degradation 

(compared with crystalline Si technologies), often associated with 

light-induced degradation of a-Si and an increase in series 

resistance in CIGS. [11]

Figure 3: Pmax, Isc, FF and Voc degradation rates for mono-Si (a), 

multi-Si (b), and thin-film (c). The thin-film part is an overlay of aSi (filled blue diamonds), CIGS (filled green triangles), and CdTe 

(filled red squares). As a guide for the eye, no degradation is 

indicated by a dashed line. The numbers at the top indicate the 

number of data points.

2.2 I-V parameter distribution

In this section we will discuss the I-V parameter distributions 

that determine the electrical mismatch.

Figure 4 shows how the width (a) and shape (b) of the 

individual I-V parameter distributions change with field exposure 

time for x-Si technologies. The coefficient of variation - standard 
3 

deviation divided by the mean of the distribution (CoV) – is used as 

the metric for the width (a). The shape of the distribution is 

characterized by the skewness, a unitless measure of the asymmetry 

of a distribution (b). [12] A skewness around zero indicates a 

normal distribution. The I-V parameters Pmax, Isc, Voc and FF are 

differentiated by color and the circle size is indicative of the number 

of modules in a particular study. Studies with less than 8 modules 

are not included in this graph; the largest study included data for 

almost 800 modules.

Figure 4: Bubble plot of (a) coefficient of variation (standard 

deviation divided by mean) and (b) the skewness of the distributions

versus field exposure. I-V parameters are color-coded. The sizes of 

the circles indicate the number of modules in the study and the 

filling of the circles provides information on the shape of the 

distribution.

Some general trends can be distinguished from the CoV graph. 

1) It appears that the CoV increases with field exposure indicating 

that the distribution tends to get wider. 2) The Voc CoV is the 

lowest of the displayed I-V parameters changing the least with field 

exposure. 3) Figure 4 (b) illustrates that the distributions not only 

tend to get wider but that they also change shape with field 

exposure. Most module distributions start normal but sometimes 

non-normality at the beginning is caused by a sharper peaked 

distribution. [13] Non-normality can also be caused by the binning 

of the manufacturer. As the field exposure increases, the negative 

skewness indicates that a more pronounced tail, sometimes 

accompanied by an outlier, at the lower end of the investigated I-V 

parameter is starting to develop. [14,15,16,17,18] It is important to 

understand that the calculated CoV could change significantly 

depending on whether the outlier is included in the calculation of 

the CoV. Furthermore, the quality of the I-V measurements could 

contribute to the shape of the distribution. Nevertheless, this lower 

tail can be understood in terms of the different failure mechanisms 

for a PV module listed by some of the authors. [19] In a complex 

system, such as a PV module, these different failure mechanisms 

are characterized by different distributions. The most dominant 

failure mechanism will cause the eventual module failure. Such a 

situation often can be characterized by an extreme value distribution 

and mirrors in shape the distribution of the overall degradation rates 

in Fig. 1. [20] The implications of this distribution deformation, the 

widening and skewing, is that it can significantly impact system 

performance since the lowest performing module will impact the 

performance of the string. For a system containing 10 modules per 

string and a total of 10 strings, and assuming 0.5%/year degradation 

over 25 years, the inclusion of a few poorly performing modules 

may increase the average observed system degradation rate from 

~0.5%/year to ~0.7%/year, as the CoV increases from 2% to 8%. In 

addition, if the skewness increases from ca. zero to -2.6, the average 

observed system degradation is closer to 0.8%/year. [21] The 4 

studies that show significant positive skewness above +1 have a 

small sample size indicated by the small circle.

2.3. Climate

I-V parameter degradation is likely to depend on the local 

conditions and climate zone. Figure 5 shows a Köppen-Geiger map 

overlaid with the geographical distribution of reported degradation 

rates (black circles). [22] The size of the circle indicates the number 

of reported degradation rates at a given location.

Figure 5: Geographic distribution of reported degradation rates 

overlaid on a Köppen-Geiger climate map with the equator and the 

tropic of Cancer and Capricorn. The size of the circle is indicative 

of the number of degradation rates at a given location.

One conclusion from the map is that no reported degradation 

rates exist today in many of the Köppen climate zones. Köppen was 

particularly interested in the interaction of climate and flora. Hence, 

his classification scheme is based on temperature and precipitation 

categories. [23] Undoubtedly, temperature and precipitation, and 

more specifically, humidity, are also relevant parameters for PV 

performance. However, additional parameters that may influence 

PV performance and longevity such as altitude, thermal cycling, 

snow load and air salinity may be just as or more important. [24] 

While such a better classification scheme is being sought, the 

Köppen-Geiger scale provides a common basis for climate 

discussions. Due to the lack of information in some climate zones 

some sensible consolidation such as combining tropical climates 

(Af & Aw) with the continental hot and humid climate (Cfa) had to 

be made. [25] 

500 100 10

Number of 

Modules Coefficient of Variation (%)

(a)Distribution Skewness

(b)
4

Figure 6: IV parameter degradation for mono-Si (open diamonds) 

and multi-Si (filled triangles) by climate zones based on KöppenGeiger classification. The 95% confidence interval is denoted by the 

diamonds with the mean as the crossbar.

The IV parameter degradation distribution by climate zone for 

mono-Si and multi-Si is shown in Fig. 6. For most climate zones Isc 

degradation is the largest contributor to Pmax degradation. For the 

desert climate the Isc degradation exceeds the Pmax degradation 

while the Voc shows a small improvement. A large proportion of 

these data points come from a one-point study, with the risk of 

larger errors as discussed above. It is also possible that the high 

temperatures of the desert climate led to EVA browning which 

would manifest itself in high Isc and low FF degradation as has 

been recently shown for an arid steppe climate. [26] Furthermore, it 

is interesting to note that in the polar climate a larger FF 

degradation is observable than in the other climates. A possible 

explanation could be that snow load led to cracking of the front 

glass and or individual cells or that the cold temperatures 

particularly in the winter led to interconnect breakage due to the 

brittleness of EVA at lower temperatures. [27]

Figure 7: I-V parameter distribution partitioned by climate with Si 

data above and thin-film data below for each climate zone. The 95% 

confidence interval is denoted by the diamonds with the mean as the 

crossbar.

Figure 7 shows a direct comparison between crystalline and 

thin-film technologies by climate zones wherever both are 

available. Especially in the maritime and hot & humid climate the 

FF has a more pronounced degradation rate for thin-film most likely

due to moisture ingress. Significant variation in the data can be 

caused by different module type, age, construction, which includes 

encapsulation, front- and back-sheet, electrical set-up (open-circuit, 

short-circuit, load resistor, grid-tied), and measurement uncertainty 

etc. [28]
5

Figure 8: Correlation of Pmax with Isc degradation for hot and 

humid climates (a) and all other climates (b). Mono-Si is indicated 

by open diamonds and multi-Si by filled inverted triangles.

A final analysis was conducted to determine the correlation of 

Pmax degradation with the various I-V parameters (Figs 8-10). 

Because degradation mechanisms are different in different climates, 

the analysis was split between hot & humid climates (a) and all 

other climates (b). As guide to the eye, perfect correlation is 

indicated by the solid red line and no correlation is indicated by the 

dashed red lines. In the hot and humid climate most data are 

distributed between the perfect correlation and no correlation lines. 

In all other climates most data follow distinctly the all Isc 

degradation rate line with two exceptions. An additional line shifted 

towards higher Pmax degradation can be seen for multi-Si and for 

mono-Si. Each of these two lines comes from a study in which only 

one data point was taken because no baseline measurements were 

available Although it is possible that these data reflect accurate 

measurements, it has been shown that there is an increased 

probability that using data sheet values for the initial measurement 

resulted in misleading data [29,30]. This illustrates (a) the 

importance of multiple measurements and (b) in the taxing hot & 

humid climate some decline comes from Voc and FF, in addition 

Isc. Figure 9 shows some data indicating substantial Voc 

degradation in hot and humid climates, mostly caused by substring 

failure. [28] Figure 10 illustrates some modules with significant FF 

degradation in the hot and humid climate caused by increased series 

resistance. [4,10,28]

The “tail” of modules with significant FF degradation in all 

other climates, Fig. 10 (b) is shifted towards the higher Pmax 

degradation. Again, these data are from a study with one 

measurement only. The same study shows significant corrosion of 

interconnections and gridlines. [18] The location of this study is in 

the Mediterranean climate close to the ocean, possibly highlighting 

the importance of atmospheric corrosiveness.

Figure 9: Correlation of Pmax with Voc degradation for hot and 

humid climates (a) and all other climates (b). Mono-Si is indicated 

by open diamonds and multi-Si by filled inverted triangles.

Figure 10: Correlation of Pmax with FF degradation for hot and 

humid climates (a) and all other climates (b). Mono-Si is indicated 

by open diamonds and multi-Si by filled inverted triangles.
6

3 DISCUSSION

The observations can help understand which degradation 

mechanisms may be dominating for each climate zone and for each 

technology type.

Specifically, the observation that the majority of crystalline 

silicon modules degraded most in Isc could be explained by 

discoloration of the encapsulant. The evidence supporting this 

theory includes: 1) that several of the reports specifically mentioned 

discoloration. [13,15,17,26,28] 2) That discoloration is known to 

cause a slow decrease in power production, and 3) The 

discoloration is known to be accelerated at higher temperatures, 

consistent with the observation of the highest Isc degradation in the 

desert. The decrease in Isc might also be explained by delamination

and the associated loss of transmission of light through the 

encapsulant-glass interface. Some of the reports specifically noted 

delamination. [10,16,28] If delamination occurs, there may 

eventually be moisture ingress and corrosion of the internal parts of 

the module. Another common cause of loss of Isc can be broken 

cells. The effect of cell breakage may be delayed because current 

can continue to flow until all of the metal connections also break. 

At that point, there may be a more abrupt drop in the current output, 

but because this may only affect the photocurrent in one part of the 

module, we expect that loss of current from one broken cell will 

reduce the fill factor, not the Isc.

An understanding of the degradation mechanisms for the thinfilm modules is complicated by the diversity of thin-film 

technology. The general observation that the fill factor decreases 

more than the other module parameters differentiates the situation 

for thin-film products from that of silicon, but does not lead to clear 

conclusions about the dominant wear-out mechanisms.

The relatively small changes in open-circuit voltage simplify the 

system design since the match between the system voltage and the 

desired input voltage of the inverter may not change much over the 

lifetime of the system. A more careful evaluation of the decreases 

in fill factor for the thin-film modules may lead to changes in 

voltage; this question may benefit from additional investigation. 

Similarly, as noted above, the 0.5%/yr decrease in module 

efficiency may correspond to significantly greater degradation rates 

at the system level depending on the design of the system.

4 CONCLUSION

Literature degradation rates were statistically analyzed to 

discern trends partitioned by technology and climate. The largest 

contributor to Pmax decline for crystalline Si technologies is Isc (or 

Imax) degradation and to a lesser degree loss in FF, especially in 

hot and humid climates. These observations suggest that accelerated 

tests quantifying discoloration of encapsulant materials, 

delamination, and/or loss of photocurrent from cracked cells may 

successfully predict wear-out rates in a majority of climates. Thinfilm technologies are characterized by a much higher contribution 

from FF also particularly for humid climates. Development of 

accelerated tests to quantify wear out in thin-film modules is more 

challenging because of the range of mechanisms affecting the 

various thin-film products. Finally, studies with a significant 

number of identical modules show not only an increased 

distribution width with increasing field exposure but also 

development of a tail at the lower end of the distribution 

characterized by an extreme value distribution. The inclusion of a 

few poorly performing modules may increase the probability 

significantly of a lower system performance.

5 ACKNOWLEDGMENTS

The authors would like to thank Chris Deline from NREL and 

Joshua Stein from Sandia for the review of the manuscript. This 

work was supported by the U.S. Department of Energy under 

Contract No. DE-AC36-08-GO28308 with the National Renewable 

Energy Laboratory.

 

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