Conference Paper

NREL/CP-520-45986

June 2009

Evaluation of High-Temperature 

Exposure of Photovoltaic Modules

Preprint 

S. Kurtz, D. Miller, M. Kempe, and N. Bosco

National Renewable Energy Laboratory

K. Whitefield

Miasole

J. Wohlgemuth

BP Solar

N. Dhere

Florida Solar Energy Center

T. Zgonena

Underwriters Laboratories Inc.

To be presented at the 34th IEEE Photovoltaic Specialists Conference

Philadelphia, Pennsylvania

June 7–12, 2009
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4 

EVALUATION OF HIGH­TEMPERATURE EXPOSURE OF RACK­MOUNTED
 

PHOTOVOLTAIC MODULES
 

1 2 1 5 3 1 

National Renewable Energy Laboratory, Golden, CO 80401 

Sarah Kurtz, Kent Whitfield, David Miller, James Joyce, John Wohlgemuth, Michael Kempe, Neelkanth Dhere, Nick 

Bosco,1 and Timothy Zgonena5 

1 

2 Miasolé, Santa Clara, CA, 95051
­ 3 BP Solar, Frederick, MD, 21703
­ 4 Florida Solar Energy Center, Cocoa, FL, 32922
­ 5 Underwriters Laboratories Inc., Northbrook, IL, 60062
­

ABSTRACT

Photovoltaic (PV) modules operate in an extreme environment and are exposed to radiation, humidity, and hot 

and cold thermal extremes. This paper focuses on polymeric-material degradation during PV-module operation at 

high ambient temperatures, high solar irradiance and low 

wind speed. The 2004 version of the IEC 61730 specification requires all polymeric materials used in a photovoltaic module to have a Relative Thermal Index (RTI) or 

Relative Thermal Endurance Index (RTE) at least 20°C 

greater than the maximum material temperature measured 

during the temperature test conducted at 40°C ambient. 

There is currently an international debate regarding this 

requirement. This paper explores the thermal exposure of 

photovoltaic modules in the field as a technical basis for 

this debate. For the hottest cities, the thermal exposure is 

found to be equivalent to aging at a constant temperature 

of 42-53°C, with maximum temperatures of 75°C. 

Establishing the long-term safety of photovoltaic (PV) 

modules depends on screening the modules with appropriate accelerated-aging tests. Past studies elucidate how 

PV modules may be tested to more quickly identify failure 

modes [1–5]. McMahon documented how to calculate the 

acceleration factor for accelerated thermal testing if the 

temperature dependence of the aging is quantified [6]. 

Procedures for determining the RTI or RTE quantify 

degradation of critical material properties at multiple 

temperatures, providing a basis to extrapolate the 

material’s thermal capability to timeframes beyond the 

experiment timeframe. 

This study documents only the thermal environment 

and the test requirements associated with high temperatures. The effects of humidity and ultraviolet (UV) light, 

although critically important, are not treated here to allow a 

more detailed examination of the thermal environment. 

This study investigates thermal exposure including: (1) 

long-term, cumulative exposure, and (2) short-term, hightemperature exposure. Long-term material degradation 

and the short-term effects of a phase change of a material 

elicit different physical processes that must be examined 

separately. This paper documents measured and modeled PV-module temperatures and evaluates these in the 

context of the requirements for accelerated testing. 

THERMALLY ACTIVATED LONG­TERM AGING

Material aging accumulates from numerous stresses 

including UV exposure, moisture, and mechanical loads. 

Most aging processes are accelerated with temperature. 

The thermal history experienced by a PV module can degrade some polymeric components even without addition 

of UV, moisture, or other stress. This analysis focuses on 

the role of temperature in aging, but also acknowledges 

the importance of other stresses. 

If a single mechanism determines aging within the 

temperature range of interest, then the rate of aging of a 

material can be approximated by the Arrhenius equation: 

− Ea 
 Rate ∝ , (1) 
 exp

 kT  INTRODUCTION

where Ea is the effective activation energy of the aging 

process, k is Boltzmann’s constant, and T is the sample 

temperature. 

The temperature dependence of the rate of a chemical reaction, as calculated from Eq. 1, is very dependent 

on the activation energy, Ea. Relative reaction rates are 

plotted as a function of inverse temperature in Fig. 1 for 

three values of Ea. In general, reaction rates are dependent on multiple mechanisms and may follow Eq. 1 over 

only a narrow temperature range, because different degradation mechanisms become important at higher temperatures [7]. Nevertheless, chemists usually use Eq. 1 to 

model the thermal dependence of reaction rates. 

As shown in Fig. 1, for two processes with the same 

reaction rate at 50°C, the rate at 100°C will be about 1000

times faster for a process with Ea = 2 eV relative to a 

process with Ea = 0.6 eV. For all three cases, increasing 

temperature accelerates the reaction rate, but accurate 

prediction of the acceleration factor requires accurate 

knowledge of Ea. In general, the linear behavior shown in 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 1 is observed only when a single reaction mechanism 

is dominant and when no phase transitions occur in the 

same temperature range. 

The RTI1 is based on application of eq. 1. Four timetemperature data points are typically used to establish a 

linear relationship of good correlation, thus providing a 

basis to extrapolate the material’s thermal capability to 

timeframes beyond the experiment timeframe, something 

a single-temperature accelerated test cannot do. The 

samples are examined periodically to detect a 50% 

change in the characteristic of interest. [8] 

10 -5 

10 -4 

10 -3 

10 -2 

10 -1 

10 0 

10 1 

10 2 

10 3 

10 4 

10 5 

Reaction rate (relative units) 

100° C 50° C 20° C

0.6 eV 1.1 eV 

2 eV 

-3 

2.6 2.8 3.0 3.2 3.4 3.6x10 

Inverse temperature (1/K) 

Fig. 1. Relative reaction rates for chemical processes with 

Ea = 0.6, 1.1, or 2 eV plotted versus the inverse of the 

reaction temperature. 

The dependence on temperature, i.e., Ea, is effectively determined as part of the RTI/RTE determination for 

each characteristic of each material. In general, Ea is not 

known a priori, but may be estimated from known values 

for similar materials. A review of aged polymeric materials 

[9] found that the most frequently observed Ea was about 

1.1 eV. For this study, we vary Ea between 0.6 and 2 eV, 

consistent with the range of Ea values reported in Ref. [9] 

for 170 measured values of Ea. 

Definition of equivalent temperature

The operating temperature for a module varies 

throughout its life. If aging of a module follows Eq. 1, it is 

possible to define an equivalent temperature, Teq, to 

represent the aging that would have taken place if the 

module had been aged for the same time, but at a conUL 746B defines “relative thermal index” as “an 

indication of the material’s ability to retain a particular 

property (physical, electrical, etc.) when exposed to 

elevated temperatures for an extended period of time. It is 

a measure of the material’s thermal endurance. For each 

material, a number of relative thermal indices can be 

established, each index related to a specific property and 

a specific thickness of the material.” 

stant (equivalent) temperature. Teq can be calculated from 

the thermal history of the module using Eq. 2: 

t2 −Ea 1 −Ea exp( ) = ∫ exp( )dt , (2) kT kT (t) eq t

1 − t2 t

1

where t is time and the limits of integration are times t1 and 

t2. This approach to defining an equivalent temperature is 

described elsewhere [6,10]. For a given moduletemperature history, the equivalent temperature can be 

calculated from Eq. 2 using numerical integration. From 

the compilation [9] of common values for Ea, analysis was 

performed for three values of Ea. 

The data history used to calculate Teq may be modeled using weather data (ambient temperature, irradiance, 

and wind speed) from a hot location, or may be taken from 

actual module data. Here, we present calculations based 

on both modeled and actual data. 

Equivalent temperature for multiple locations

For this study, we used two module-temperature 

data sets: one measured in Florida and one measured in 

Colorado. Both sets of modules were deployed in openrack configurations with latitude tilt and 15-min-averagetemperature monitoring on the backs of the modules. The 

Florida modules were deployed at the Florida Solar 

Energy Center. These were Shell Solar modules with a 

glass-CIGS-glass construction. The Colorado modules 

were deployed at NREL and were (glass-silicon-backsheet) ASE GP300DG Si ribbon modules. Teq values for 

the Florida dataset are summarized for each month of one 

year in Table 1 and Fig. 2. 

Module-temperature data were not identified for all 

critical locations. To extend the study to some very hot 

locations, we used Typical Meteorological Year (TMY), 

specifically TMY3, data [11] to estimate the module temperature from the irradiance and the wind speed using Eq. 

3 [12], 

T = Tamb + Irradiance × exp[−3.473− 0.0594 ×WS], (3) m

where Tm is the module temperature (°C), Irradiance is the 

plane-of-array (POA) irradiance (W/m2), Tamb is the ambient temperature (°C), and WS is the wind speed (m/s). 

The weather data were either measured or taken from the 

TMY3 database. When taken from the TMY3 database, 

the POA irradiance was calculated [13] from 

cos[AOI] Irradiance = GHI × , (4) cos[zenith]

where GHI is the global horizontal irradiance (W/m2) data 

in the TMY3 database, the AOI angle is the angle between 

the sun and the latitude-tilt POA, and the zenith angle is 

the angle between the sun and the zenith. Equation 4 is 

exact for the direct beam; conversion of the diffuse part of 

GHI to the diffuse irradiance on the POA requires knowl1
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                   

 

 

 

 

 

 

 

 

 

 

 

 

 

       

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

edge of the ground albedo. The more exact calculation 

changes Teq by < -1° or +1°C for albedo of 0 or 1, respectively. Equation 3 is designed to predict the module temperature for a rack-mounted, glass-glass module; it predicts the monthly average of the module temperature 

within 2°C for the data in Table 1 and Fig. 2. Equation 3 

tends to slightly overestimate the module temperature, 

especially on clear nights when the modules radiate energy into space, causing them to cool 1°–2°C below ambient temperature. A comparison of about 10 days of 

measured and modeled module-temperature data is 

shown in Fig. 3. The measured data shown in Fig. 3 are a 

subset of the dataset from Florida. The modeled moduletemperature data are carefully compared with the actual 

measured module-temperature data in Figs. 2 and 3 and 

Table 1—to gain confidence in Eq. 3 and to be able to 

apply the methodology to any location for which we have 

weather data, but no module-temperature data. 

Table 1. Summary of thermal history over one year for a module deployed in Florida in an open-rack-mounted configuration. The modeled module-temperature data were derived from the measured weather data using Eq. 3. Teq was calculated from Eq. 2 using the indicated values of Ea. All temperatures are °C. 

Month Ambient T Measured Module T Modeled Module T Equivalent T

Ave. Max. Ave. Max. Ave. Max. Ea = 0.6 eV Ea = 1.1 eV Ea = 2 eV 

Jan 18.0 33.6 21.1 58.3 23.1 63.8 26.1 30.8 37.0 

Feb 15.8 27.2 19.7 58.1 21.6 57.8 26.4 32.0 38.2 

Mar 19.6 30.7 25.0 62.0 26.3 59.4 32.0 37.5 43.4 

Apr 21.0 31.4 26.8 61.6 27.9 60.1 33.8 39.2 44.7 

May 23.8 30.2 29.5 60.0 30.2 58.7 34.8 39.5 44.9 

Jun 26.0 34.4 31.8 70.6 31.9 63.7 37.3 42.3 48.5 

Jul 26.6 34.4 32.3 69.1 31.8 62.3 38.7 44.3 50.5 

Aug 28.4 40.7 33.5 66.5 35.1 69.9 39.8 44.9 50.3 

Sep 26.2 34.1 32.2 69.7 32.9 65.7 38.6 44.0 49.9 

Oct 23.0 33.2 28.4 67.1 29.6 63.3 35.0 40.3 46.2 

Nov 18.5 29.3 22.7 61.3 24.2 56.3 28.6 33.6 39.6 

Dec 20.1 28.4 23.2 56.4 24.3 54.3 27.2 31.4 37.3 

Year 22.3 40.7 27.2 70.6 28.2 69.9 33.3 39.5 46.4

80 

60 

40 

20 

Temperature (°C) 

Average ambient

 Ave. module - measured

 Ave. module - modeled

 Equivalent module

 Maximum module 

Required RTI/RTE rating 

Measured max. temp. 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Fig. 2. Thermal history from Table 1, including Teq

calculated for Ea = 1.1 eV. The lines across the top 

estimate the expected maximum operating temperature 

and the associated required RTI/RTE rating. 

70 

60 

50 

40 

30 

20 

Temperature (°C) 

Modeled

 Measured 

Time

Fig. 3. Comparison of measured module-temperature 

data with analytical data (Eq. 3) for 10 days in Florida. 

to be relatively independent of location, i.e., site-specific 

climate. 

Hottest locations

In Fig.4, Teq values calculated for the measured 

data in Florida and Colorado and modeled from the 

average weather data [11] for Luxor (Egypt), Riyahd 

(Saudi Arabia), and Kuwait International Airport (Kuwait) 

are plotted as a function of Tamb. The open symbols show 

monthly averages; the solid symbols indicate the annual 

average. The relationship between average ambient temperature and the equivalent module temperature appears 

As shown in Fig. 4, the calculated Teq shows a clear 

correlation with the average ambient temperature. Although the local sunshine, wind speed, and other variables affect the equivalent temperature, these effects are 

relatively small. Data for average ambient temperatures 

are readily available for many locations around the world. 

In Table 2, Bangkok, Thailand, has the highest average 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

           

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

50 

40 

30 

20 

50 

40 

30 

20

Equivalent temperature (°C) 

55 

50 

45 

40 

35 

30 

Ea = 0.6 eV 

Ea = 1.1 eV 

Ea = 2 eV

 Florida

 Colorado

 Kuwait

 Saudi

 Egypt 

0 10 20 30 

Average ambient temperature (°C) 

Fig. 4. Teq vs. monthly average ambient temperature. The 

solid symbols represent the average for the entire year. 

The data for Florida (Table 1) and Colorado were collected 

for rack-mounted systems; the data from the Middle East 

were modeled from weather conditions using Eq. 3. 

ambient temperature of 28.1°C. Other reports indicate 

that 29°C may be the highest documented average temperature for any city [14]. 

Teq values from Fig. 4 are summarized in Table 3. 

Additional uncertainty is associated with the mounting 

configuration and the difference between measured temperature of the module and the temperature of its component materials. Roof-mounted modules are expected to 

operate at higher temperatures. Teq for roof-mounted systems shows stronger dependence on total solar irradiance. 

Similarly, tracked systems experience higher average 

module temperatures. This will be treated in more detail 

elsewhere. 

Table 3 shows that Teq for the hottest locations in the 

world vary between 42° and 53°C, depending on the Ea

used in the analysis. For more moderate climates, values 

of Teq range between about 30° and 40°C. The 

temperatures in Table 3 are substantially lower than the 

maximum temperatures for the modules on the hottest 

days. Because some polymers may age in this temperature range, it may be useful to accelerate this degradation 

by the application of a higher temperature. 

Currently, modules are stressed at 85°C and 85% 

relative humidity for 1000 hours as part of the IEC 61215 

and IEC 61646 qualification tests [16]. The methodologies 

Table 2. Summary of annual average ambient temperature 

for some familiar locations [15] 

Location Average temperature (°C)

Bangkok, Thailand 

Jakarta, Indonesia 

28.1 

26.8 

Lagos, Nigeria 26.5 

Karachi, Pakistan 26.0 

New Delhi, India 25.0 

Kinshasa, Zaire 24.5 

Miami, FL, USA 24.0 

Hong Kong 23.1 

Baghdad, Iraq 22.7 

Phoenix, AZ, USA 22.6 

Tucson, AZ, USA 21.1 

Cairo, Egypt 21.0 

Tripoli, Libya 20.4 

Tel Aviv, Israel 19.4 

Sao Paulo, Brazil 18.3 

Lima, Peru 18.2 

San Diego, CA, USA 17.8 

Sydney, Australia 17.5 

Tehran, Iran 16.7 

Mexico City, Mexico 15.9 

Lisbon, Portugal 15.9 

Shanghai, China 15.4 

Rome, Italy 15.4 

Tokyo, Japan 14.5 

Madrid, Spain 14.2 

Istanbul, Turkey 14.1 

San Francisco, CA, USA 13.9 

Bogota, Colombia 13.1 

Beijing, China 11.8 

London, UK 11.7 

Paris, France 11.0 

Berlin, Germany 8.9 

Table 3. Summary of average ambient temperatures and 

Teq values from Fig. 4 using Ea = 0.6, 1.1, and 2 eV for 

rack-mounted PV modules. All temperatures are °C. 

Location Average

ambient

Equivalent module temperature

0.6 eV 1.1 eV 2 eV

Hot location 29 42 47 53 

Kuwait 26.5 38.9 45.2 52.1 

Saudi Arabia 26.2 40.2 46.2 52.9 

Egypt 24.8 39.3 45.3 52.2 

Florida 22.3 33.3 39.5 46.4 

Colorado 11.2 26.1 34.1 41.9 

for determining RTI and RTE have subtle differences that 

are difficult to define because of flexibility in how the 

standards are applied. Both IEC 60216-5 and UL746B 

use simultaneous aging with a reference material to 

identify the aging, or correlation, time, which is usually 

between 20,000 h and 100,000 h. [8] Although these times 

are shorter than desired for PV module lifetime, UL uses 

this approach, based on years of observations that 

materials that retain their properties for 100,000 hours in 

the field can be regarded as stable for most applications. 

Table 4 compares the time and temperature that would be 
 

 

 

 

 

 

 

   

 

 

 

       

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

           

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

needed to simulate 20,000-100,000 hours in the field for 

the Tamb of 29°C, the hottest average temperature 

documented for any city in the world. 

Table 4. Extrapolated Teq for a location with an average 

Tamb of 29°C (see Table 3) and the corresponding test 

conditions that would be needed to simulate 20,000 or 

100,000 hours at that temperature. 

Ea

(eV)

Teq

(°C) 

Time at 85° C 

(h) 

Temperature for 

1000 hour test 

(°C) 

Correlation time (h) 20,000 100,000 20,000 100,000 

0.6 42 ± 5 1410 7040 91 ± 7 125 ± 8 

1.1 47 ± 5 290 1450 73 ± 6 89 ± 7 

2.0 53 ± 5 35 173 67 ± 6 76 ± 6 

Discussion of long­term thermal aging

Table 4 suggests that, for aging mechanisms with 

large Ea, a test condition of 1000 hours at 85°C (as is used 

in the damp-heat test) applies similar stress to that applied 

during the RTI/RTE testing. For aging mechanisms with 

small Ea, the damp-heat test does not apply as much 

stress as the RTI/RTE testing. 

Many aging mechanisms are accelerated in the 

presence of moisture, which has not yet been considered. 

A prior study suggested that an increase of 1% in relative 

humidity accelerates aging about the same as 1°C rise in 

temperature for degradation associated with corrosion 

[17], but it is not likely that this correlation would hold for 

aging of all polymeric materials. For cooler locations the 

numbers in Table 4 would be decreased. Thus, the dampheat test appears to be more stringent than the RTI/RTE 

test when Ea = 2.0 eV and less so when Ea = 0.6 eV. A 

quantitative comparison of the different test methods 

depends on Ea, the correlation time, and the failure criteria 

(the RTI depends on 50% degradation; whereas the IEC 

damp-heat test checks module performance). If RTI/RTE 

testing predicts adequate stability, then for Ea = 2 eV, the 

damp-heat test should also predict adequate stability, but 

when Ea is small, there is added risk if damp-heat is used 

alone. 

SHORT­TERM, HIGH­TEMPERATURE EXPOSURE

If a PV module experiences a temperature that 

causes a dramatic phase transition of a component material, the module may degrade very rapidly. The physical 

process driving such changes is very different from the 

thermally activated aging described by Eq. 1, as discussed 

above. Thermally activated degradation is a cumulative 

process that depends on the entire thermal history, 

whereas changes caused by exposure to temperatures 

near a phase-transition temperature may occur very 

quickly and may be dependent on a single high-temperature event. Thus, the evaluation of the possibility of a 

phase transition focuses on the high end of the temperature distribution curve. The very highest temperatures are 

expected on the hottest days of the year, when the sun is 

brightest. One set of temperature data is shown in Fig. 5 

for the same Florida dataset described above in Table 1. 

Hottest module temperatures for multiple locations

The Florida dataset is shown along with similar data 

for other locations in Fig. 6. The multiple datasets are the 

same as those used for calculating the data in Fig. 4. The 

module-temperature data are expected to be strongly dependent on module mounting configuration. To better 

explore this, the module temperatures were modeled with 

a modified version of Eq. 3, using coefficients that are 

known to approximate the temperatures observed for direct roof mounting [12]: 

Tm = Tamb + Irradiance× exp[−2.98− 0.0471×WS] (6) . 

The distribution of module temperatures for the roofmounted configuration is shown in Fig. 7. The module 

temperatures calculated for 40°C ambient and 1000 W/m2 

from Eq. 3 for open-rack mount and Eq. 6 for direct roof 

mount are 70.0°C and 90.8°C, respectively. These are 

similar to temperatures typically measured for modules 

using the IEC 61730 temperature test. 

0.16 

0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

0.00 

Relative frequency 

0 10 20 30 40 50 60 70

Temperature (°C) 

600 

500 

400 

300 

200 

100 

0 

Number of hours during 1 year

 January

 February

 March

 April

 May

 June

 July

 August

 September

 October

 November

 December

 Entire year 

Fig. 5. Distribution of module temperatures measured for 

a module rack-mounted in Florida for each month of one 

year (left axis) and the sum of these (thick line, right axis). 

20 

15 

10 

5 

0 

Hours/year 

62 64 66 68 70 72 74 76

Temperature (°C) 

Florida 

Saudi Arabia 

Egypt 

Kuwait 

Rack mounted 

Colorado 

Fig. 6. Distribution of rack-mounted module temperatures 

for five sites (Florida data are the same as in Fig. 5.) 

Modeled data are used for the three Middle East sites. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

    

 

 

 

          

 

 

     

 

 

         

 

     

 

     

 

 

    

 

 

 

 

 

 

    

 

 

 

 

 

    

 

 

 

 

       

 

 

 

 

 

 

 

 

 

        

    

20 

15 

10 

5 

0 

Hours/year 

Roof mounted Saudi Arabia 

Egypt 

Kuwait 

84 86 88 90 92 94 96 98 

Temperature (°C) 

Fig. 7. Distribution of module temperatures modeled for a 

roof-mounted configuration for three Middle East locations. 

CONCLUSIONS

Regarding long-term thermal aging, we conclude that 

rack-mounted modules in the hottest parts of the world 

age as though they are constantly maintained at a temperature of about 42°–53°C. Other conclusions include: 

1.	­ Based on data in Table 4, for large (small) values of 

Ea, the thermal stress aspect of the damp-heat test is 

less (more) severe than the long-term aging test 

conditions to determine RTI/RTE. 

2.	­ The addition of humidity during the damp-heat test 

may affect the numbers in Table 4. 

3.	­ To fully understand the relationship between the 

RTI/RTE and damp-heat tests, the Ea values must be 

quantified. Furthermore, the combined effects of 

temperature, moisture, UV, and time need to be 

considered. 

4.	­ This study should be extended to roof-mounted systems. 

Regarding short-term exposure to very high temperatures, we conclude that rack-mounted modules experience temperatures as high as 75°C and roof-mounted 

modules experience temperatures as high as 96°C. Modules may experience even higher temperatures at the 

junction box, or if cells are shaded. Some new encapsulant materials may not be able to withstand these high 

temperatures. It may be valuable to add a test that exposes the module to a high temperature for a short time 

(~1 h) to determine whether a radical phase change of a 

material may have a detrimental effect on the module. 

ACKNOWLEDGEMENTS

We thank J. Thies and G. Fechtmann for useful 

conversations about RTI and C. Osterwald and B. Sekulic 

for the data for the ASE system. This work was supported 

by the U.S. Department of Energy under Contract No. 

DOE-AC36-08GO28308 with the National Renewable 

Energy Laboratory. 

REFERENCES

[1] C.R. Osterwald, and T.J. McMahon "History of 

Accelerated and Qualification Testing of Terrestrial 

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17, 2009, pp. 11–33. 

[2] J.H. Wohlgemuth, et al. "Using Accelerated Tests and 

Field Data to Predict Module Reliability and Lifetime," 

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4EP1.2, pp. 2663-2669. 

[3] J.H. Wohlgemuth, et al. "Long Term Reliabilty of 

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[4] J.H. Wohlgemuth, D.W. Cunningham, A.M. Nguyen, 

and J. Miller "Long Term Reliability of PV Modules," Proc.

20th European PVSEC, Barcelona, Spain, 2005, p. 1942. 

[5] A. Desombre "Methodology for a Reliability Study of 

Photovoltaic Modules," Proc. 3rd ECPVSEC, 1980, pp. 

741–745. 

[6] T. McMahon "Accelerated Testing and Failure of Thinfilm PV Modules," Prog. in PV 12, 2004, pp. 235–248. 

[7] J.E. Pickett, D.A. Gibson, S.T. Rice, and M.M. Gardner 

"Effects of Temperature on the Weathering of Engineering 

Thermoplastics," Poly. Deg. Stab. 93, 2008, pp. 684–691. 

[8] ANSI/UL, "UL 746B Standard for Polymeric materials -

Long Term Property Evaluations," 2001; IEC 60216-5 

"Electrical insulating materials − Thermal endurance 

properties − Part 5: Determination of relative thermal 

endurance index (RTE) of an insulating material." 

[9] R. Dixon "Thermal Aging Predictions from an Arrhenius Plot with only One Data Point", IEEE Transactions on

Electrical Insulation EI­15, (4), 1980, pp. 331-334. 

[10] UL746C, "Polymeric Materials - Use in Electrical 

Equipment Evaluations." 

[11] http://apps1.eere.energy.gov/buildings/energyplus/, as described in: S. Wilcox and W. Marion, "Users Manual 

for TMY3 Data Sets" Technical Report NREL/TP-581-

43156, revised May, 2008. 

[12] D.L. King, W.E. Boyson, and J.A. Kratochvil, 

"Photovoltaic Array Performance Model," Sandia National 

Laboratories, SAND2004-3535, (2004). 

[13] Solar Engineering of Thermal Processes (John Wiley 

& Sons, Inc., 1991, 2nd edn. 1991), p. 25. 

[14]	­http://www.trivia-library.com/a/10-hottest-cities.htm 

[15]	­http://www.worldclimate.com/ 

[16] IEC 61215, "Crystalline silicon terrestrial photovoltaic 

(PV) modules - Design qualification and type approval," 

IEC 61646, "Thin-film terrestrial photovoltaic (PV) modules 

- Design qualification and typ approval." 

[17] D.H. Otth and R.G. Ross "Assessing Photovoltaic 

Module Degradation and Lifetime from Long Term 

Environmental Tests," Proc. 29th Inst. of Environmental

Science Annual Meeting, 1983, pp. 121–126. 
F1147-E(10/2008) 

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June 2009

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Conference Paper

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4. TITLE AND SUBTITLE

Evaluation of High-Temperature Exposure of Photovoltaic Modules: 

Preprint

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

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6. AUTHOR(S)

S. Kurtz, K. Whitfield, D. Miller, J. Wohlgemuth, M. Kempe, 

N. Dhere, N. Bosco, and T. Zgonena

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NREL/CP-520-45986

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PVB76503

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National Renewable Energy Laboratory

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14. ABSTRACT (Maximum 200 Words)

The 2004 version of the IEC 61730 specification requires all polymeric materials used in a photovoltaic module to 

have a Relative Thermal Index (RTI) at least 20°C greater than the module operating temperature measured at 40°C 

ambient. The RTI reflects changes in key properties (e.g., dielectric strength) after long-term exposure at elevated 

temperature. The RTI determination elucidates the thermal aging more thoroughly than a single-temperature 

accelerated test, but requires substantial time and may not measure the characteristics that are most relevant to the 

safe use of a module. There is currently an international debate regarding whether the RTI requirement should be 

retained or whether other tests would suffice without introducing a new safety risk. This paper explores the thermal 

exposure of photovoltaic modules in the field so as to provide a technical basis for this debate. Given our current 

knowledge, for rack-mounted modules, the damp-heat test is a reasonable substitute for the RTI measurement for 

the majority of materials. Improved knowledge about aging processes is necessary to provide the technical basis for 

an improved test.

15. SUBJECT TERMS

PV; modules; relative thermal index; high-temperature exposure; ultraviolet light; 

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