NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

NREL

Dirk Jordan

2011 Photovoltaic 

Module Reliability 

Workshop

February 16, 2011

Golden, Colorado

NREL/PR-5200-51120

Methods for Analysis of Outdoor 

Performance Data
National Renewable Energy Laboratory Innovation for Our Energy Future

Outline

• Motivation: Impact of uncertainty in degradation rates (Rd)

• Methodologies

1. IV data taken in discrete intervals 

2. Continuous data, PVUSA & Performance Ratio

3. Additional methodologies for continuous data - Classical 

Decomposition, ARIMA

• Historical Rd and what we can learn from it.

1. Methodologies

2. Number of measurements

3. Climate 

2
National Renewable Energy Laboratory Innovation for Our Energy Future

Motivation

2 examples from NREL:

Different observation lengths, seasonality etc.  Leads to different uncertainties

0

5

10

15

20

25

30

35

40

45

0 50 100

DC Power (W)

Time (Months)

Module 1

Module 2

Rd (Module 1) = (0.8 ±0.2) %/year

Rd (Module 2) = (0.8 ±1.0) %/year

Same Rd but very different uncertainty

For solar industry to keep growing we need to accurately understand & predict how 

different technologies behave/change with weather, climate and time.

Change of power output with time is degradation rate (Rd)....uncertainty is very 

important too.

3
National Renewable Energy Laboratory Innovation for Our Energy Future

Rd Uncertainty Impact on Warranty

0.00

0.05

0.10

0.15

0.20

0.25

Probability

Power Production after 10 Years (%)

Rd=(0.8±0.2) %/year

Rd=(0.8±1.0) %/year

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Probability

Power Production after 25 Years (%)

Rd(0.8±0.2) %/year

Rd=(0.8±1.0) %/year

Manufacturer Warranty often twofold: 90% after 10 years, 80% after 25 years

Probability to default warranty:

1.0 %/year uncertainty = 46%

0.2 %/year uncertainty = 4%

Probability to invoke warranty:

1.0 %/year uncertainty = 57%

0.2 %/year uncertainty = 24%

( )

( ) ∑

= +

⋅ − =

N

n n

n d

N

r

Energy Year R Energy Year

1

1

1

( ) 1 ( )

Higher Rd uncertainty significantly increases warranty risk

4
National Renewable Energy Laboratory Innovation for Our Energy Future

Degradation Rate (Rd)- Discrete Points 

1. Translation to reference conditions (IEC60891)

2. Time series to determine degradation rate

0

10

20

30

40

50

60

70

0 50 100 150 200

Pmax (W)

Time (Months)

Rd= (0.33±0.07) %/year

Quarterly taken I-V curves for degradation

0

10

20

30

40

50

60

70

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 10 20

Power (W)

Current (A)

Voltage (V)

Current

Power

Irradiance

Temperature

Isc

Voc

sc oc sc Voc I

I V

I V

P FF ⋅⋅ = ⋅ = max max max

5
National Renewable Energy Laboratory Innovation for Our Energy Future

Degradation Rate - Discrete Points 

88

90

92

94

96

98

100

102

0 50 100 150 200

Isc of initial (%)

Time (Months)

Short-circuit Current Open-circuit Voltage Fill Factor

Degradation is due to decline in Isc, (Voc & FF are stable)  clues to degradation mechanism

I-V curves provide clues to underlying failure mechanism 

Problem: 1. Labor-intensive, has to be clear sky

2. Large arrays  portable I-V tracer may not be available

3. Typically historical data not available

88

90

92

94

96

98

100

102

0 50 100 150 200

Voc of initial (%)

Time (Months)

88

90

92

94

96

98

100

102

0 50 100 150 200

FF of inital (%)

Time (Months)

Monocrystalline-Si

6
The plant was originally constructed by the Atlantic Richfield oil company (ARCO) in 1983.

Provided electricity, data & experience in the 1980s and 1990s. Plant was dismantled in the late 1990s.

National Renewable Energy Laboratory Innovation for Our Energy Future

PV for Utility Scale Application (PVUSA)

Reference conditions:

PVUSA Test Conditions (PTC): E=1000 

W/m2, Tambient=20oC, wind speed=1 m/s

Need basic weather station to collect Tambient and wind speed on top of irradiance

0

10

20

30

40

50

60

0 20 40 60 80 100 120

DC Power (W)

Time (Months)

1. Step: Translation to reference conditions (use 

a multiple regression approach)

P H (a a H a T a ws) ambient = ⋅ + ⋅ + ⋅ + ⋅ 1 2 3 4

Seasonality leads to required observation times of 3-5 years*  long time in today’s market

H= Plane-of-array irradiance

Tambient=ambient temperature

ws= wind speed

a1, a2, a3, a4= regression coefficients

Long time required for accurate Rd

2. Step: Time series to determine degradation 

rate

PVUSA Rating Methodology

*Osterwald CR et al., Proc. of the 4th IEEE World Conference on Photovoltaic Energy Conversion, Hawaii, 2006.

7

Improved PVUSA models include Sandia & BEW model**

**Kimber A. et al., Improved Test Method to Verify the Power Rating of a PV Project. Proceedings of the 34th PVSC, Philadelphia, 2009.
National Renewable Energy Laboratory Innovation for Our Energy Future

Classical Decomposition

8

Original Data

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

Signal = Trend + Seasonality + Irregular
National Renewable Energy Laboratory Innovation for Our Energy Future

Classical Decomposition

9

Original Data Trend

12-month 

centeredMoving 

800 Average

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

Signal = Trend + Seasonality + Irregular
National Renewable Energy Laboratory Innovation for Our Energy Future

Classical Decomposition

10

Seasonality

Original Data Trend

12-month 

centeredMoving 

Average

Average of 

each month 

for all years of 

observation

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

-50

-40

-30

-20

-10

0

10

20

30

40

50

0 40 80 120

DC Power (W)

Time (Months)

Signal = Trend + Seasonality + Irregular
National Renewable Energy Laboratory Innovation for Our Energy Future

Classical Decomposition

Seasonality Irregular

Original Data Trend

12-month 

centeredMoving 

Average

Average of 

each month 

for all years of 

observation

Determine Rd from Trend graph for higher accuracy

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

800

850

900

950

1000

1050

1100

0 40 80 120

DC Power (W)

Time (Months)

-50

-40

-30

-20

-10

0

10

20

30

40

50

0 40 80 120

DC Power (W)

Time (Months)

-40

-30

-20

-10

0

10

20

30

40

50

60

0 40 80 120

DC Power (W)

Time (Months)

Signal = Trend + Seasonality + Irregular

S.G. Makridakis et al., “Forecasting”, New York, John Wiley & Sons 1997.

11
National Renewable Energy Laboratory Innovation for Our Energy Future

ARIMA

−12 −1 −13 −12 − − ⋅ + ⋅ = + − ⋅ Pt Pt Pt Pt t t φ φ δ ε θ ε

AutoRegressive Integrated Moving Average (ARIMA)

ARIMA(100)(011) 

P=Power

c, δ, φ, θ =constant

ε=noise 

Use ARIMA to model data, then decompose

Box, GPP and Jenkins, G: Time series analysis: Forecasting and Control, San Francisco: Holden-Day, 1970.

2 free software packages, US Census Bureau, Bank of Spain: plug & play, sensitive to outliers!

Many statistical software packages include time series analysis (JMP, Minitab, R etc)

Developed script to make model selection less sensitive to outliers.

Model trend & seasonality component w/ Linear Combination of weighted differences & averages 

1. Built several Models  minimize noise 

component

2. Chose parsimonious model w/ aid of 

several selection criteria

12
National Renewable Energy Laboratory Innovation for Our Energy Future

Outliers

ARIMA most robust against outliers

1. Dataset from NREL

2. Introduce outliers sequentially

3. Calculate Rd & study effect on all 3 methodologies

Procedure:

600

700

800

900

1000

1100

1200

1300

0 20 40 60 80

DC Power (W)

Time (Months)

5 4 3 2 1

Compare sensitivity of 3 methods to outliers

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0 1 2 3 4 5

Degradation Rate (%/year)

Number of outliers

Traditional

Class. Decomp.

ARIMA +Decomp.

13
National Renewable Energy Laboratory Innovation for Our Energy Future

Data Shifts

1. Dataset from NREL

2. Introduce a data shift deliberately

3. Multiply shifted section with a scaling factor

4. Calculate Rd & study effect on all 3 methodologies

Procedure:

0E+0

1E+5

2E+5

3E+5

4E+5

5E+5

6E+5

7E+5

0.5 0.7 0.9 1.1 1.3 1.5

Residual Sum of Squares

Scale Factor

Compare sensitivity of 3 methods to data shifts

Example: inverter change

Correct data shifts by minimizing residual sum of squares

14
National Renewable Energy Laboratory Innovation for Our Energy Future

Data Shift Results

Residual minimization technique works on real shifts

Data shift cause: Erratic ambient Temp 

sensor.

Misleading degradation rate if Rd

calculated after shift.

Data shift correction procedure is 

successful for all 3 approaches.

Results from induced shift Real Shift – Blind test

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-6 -4 -2 0 2 4 6

Degradation Rate (%/year)

Shift of data (%)

Traditional

Class.Decomp.

ARIMA+Decomp.

700

750

800

850

900

950

1000

1050

1100

0 20 40 60 80 100 120 140

DC Power

Time (Months)

Original Data

c-12-month MA

corrected c-12-Month MA

15
National Renewable Energy Laboratory Innovation for Our Energy Future

PVUSA – Weekly Intervals

600

700

800

900

1000

1100

1200

1300

1400

1500

0 20 40 60 80

DC Power (W)

Time (Months)

600

700

800

900

1000

1100

1200

1300

1400

1500

0 100 200 300

DC Power (W)

Time (Weeks)

Monthly 

Intervals

Weekly 

Intervals

Multi-crystalline module

16
National Renewable Energy Laboratory Innovation for Our Energy Future

PVUSA – Weekly Intervals

600

700

800

900

1000

1100

1200

1300

1400

1500

0 20 40 60 80

DC Power (W)

Time (Months)

Weekly intervals  converges in less time

600

700

800

900

1000

1100

1200

1300

1400

1500

0 100 200 300

DC Power (W)

Time (Weeks)

Monthly 

Intervals

Weekly 

Intervals

-1.2

-0.8

-0.4

0

0.4

1 2 3 4 5 6 7

Degradation Rate (%/year)

Time (Years)

Traditional

Class.Decomp.

ARIMA+Decomp.

-1.2

-0.8

-0.4

0

0.4

1 2 3 4 5 6 7

Degradation Rate (%/year)

Time (Years)

Traditional

Class. Decomp.

ARIMA+Decomp.

Multi-crystalline module

17
National Renewable Energy Laboratory Innovation for Our Energy Future

Performance Ratio

*B.Marion et al., “Performance Parameters for Grid-Connected PV Systems”, Proc. 31st PVSC, Orlando, FL 2005.

PVUSA Monthly PR Daily PR

r

f

Y

Y PR = G

H Yr = 0

f P

E Y = Yf

=Final Yield

E=Net Energy output

P0=Nameplate DC rating 

Yr

=ReferenceYield

H=In-plane Irradiance

G=Reference Irradiation

*

Can apply same modeling approaches to minimize seasonality

Multi-crystalline Si system

18
National Renewable Energy Laboratory Innovation for Our Energy Future

Data Filtering

Kimber A. et al., Improved Test Method to Verify the Power Rating of a PV Project. Proceedings of the 34th IEEE PV Specialist Conference, Philadelphia, 2009.

Example on how variable Rd may be depending 

irradiance filtering (may not be representative)

Filtering interval too tight or broad  Rd may be 

substantially different and uncertainty goes up

A. Kimber paper showed uncertainty may be 

reduced by using only sunny days

Data filtering has important impact on determined Rd

Irradiance filtering interval PVUSA on 3 different modules

20

40

60

80

20

40

60

80

20

40

60

80

0 50 100 150 200

Time (Months)

Too 

broad

Too 

tight

best

Rd=(-0.45±0.15) 

%/year

Rd=(-0.44±0.21) 

%/year

Rd=(-0.51±0.11) 

%/year

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

0 200 400 600 800 1000

Rd (%/year)

Lower Irradiance Filter Cut-Off (W/m2)

mono-Si

multi-Si

a-S

DC Power (W)

19

mono-Si
30

35

40

45

50

55

60

65

70

75

80

0 50 100 150 200

Pmax (W)

Time (Months)

IEC60891-Method1

IEC60891-Method2

IEC60891-Method3

30

35

40

45

50

55

60

65

70

75

80

0 50 100 150 200

Pmax (W)

Time (Months)

St.Least Squares

Class.Decomp.

ARIMA

National Renewable Energy Laboratory Innovation for Our Energy Future

IEC 60891 PVUSA

Discrete vs. Continuous Data

Quarterly taken IV + IEC translation less uncertainty than PVUSA

PVUSA + Modeling uncertainty is comparable to IEC method

20

Rd(PVUSA SLS)= (0.47±0.12) 

%/year

Rd(PVUSA CD) = (0.34±0.04) %/year

Rd(PVUSA AR) = (0.33±0.08) %/year

Rd(IEC-M1)= (0.33±0.07) 

%/year

Rd(IEC-M2)= (0.34±0.06 %/year

Rd(IEC-M3)= (0.30±0.07 %/year
National Renewable Energy Laboratory Innovation for Our Energy Future

Time 

series

Data Type /# 

Data Pts. Data Aqc. Reference condition

Uncertainty

Outliers/

Dt.shifts

sensitivity

Impleme

ntation Comments

PVUSA SLS continuous DC, H, T, ws PTC ok? high easy

CD “ “ “ good medium easy

ARIMA “ “ “ best low difficult Software & 

training required

PR SLS continuous AC, H ---- ok? high easy

CD “ “ “ good medium easy

ARIMA “ “ “ best low difficult Software & 

training required

IV-2 SLS discrete, 2 I,V, H, T STC, 

IEC60891 ok? high easy

difficult for larger 

arrays

IV-3+ SLS Discrete, >2 “ “ best low easy “

Methodologies - Summary

SLS: Standard Least Squares, CD: Classical Decomposition

H: in-plane irradiance, T: temperature, ws: wind speed

21

Contin. Data: Class.Decomp. may be good compromise

Discrete: Better take more than 2 measurements 
National Renewable Energy Laboratory Innovation for Our Energy Future

PERT – Degradation Rates

Performance Energy Rating 

Testbed = PERT

More than 40 Modules, 

> 10 manufacturers, 

Monitoring time: 2 yrs-16 yrs

Appears that CdTe, CIGS & multi-Si improved Deg.Rate (%/year)

0

1

2

3

4

1 2 1 2 1 2 1 1 2

a-Si CdTe CIGS c-Si poly-Si

Date of Installation w ithin Technology

Pre Post Pre Post Pre Post Pre Pre Post

Pre: Installed before year 2000

Post: Installed after year 2000

D.C. Jordan et al., Outdoor PV degradation comparison, Proc. IEEE PVSC, 2010, 2694.

22

mono multi-Si

Photo credit: Warren Gretz, NREL PIX 03877.
National Renewable Energy Laboratory Innovation for Our Energy Future

Pre Post Pre Post Pre Post Pre Post Pre Post

Degradation Rates – Literature Survey

Partitioned by date of installation: Pre- & Post-2000

Red diamonds: mean & 95% confidence interval

Crystalline Si technologies appear to be the same

Thin-film technologies saw significant drop in Rd in last 10 years

Number of Rd from literature: 1364 ca. 100 publications (see end)

23
National Renewable Energy Laboratory Innovation for Our Energy Future

Shell Solar E80-C modules deployed at 

NREL. Photo credit: Harin Ullal, NREL 

PIX 14725

NREL CIGS System

4.60

4.70

4.80

4.90

5.00

5.10

Pyranometer Cal. Factor

(μV/W/m2)

Date

Pyranometer calibration PVUSA + Field I-V data

Rd

Statistical 

Uncertainty Method Interval

0.14 0.22 PVUSA Linear Fit Monthly

0.57 0.21 PVUSA Cl.Decomp Monthly

0.28 0.23 PVUSA ARIMA Monthly

0.12 0.35 Performance Ratio 

Linear Fit Monthly

0.61 0.10 PR Cl.Decomp Monthly

0.47 0.13 PR ARIMA Monthly

0.14 0.10 Performance Ratio 

Linear Fit Daily

0.28 0.22 Median ± pooled 

Standard deviation

0 20 40 60

600

650

700

750

800

850

900

950

1000

1050

Dec-05 Sep-06 Jul-07 May-08 Feb-09 Dec-09 Oct-10

Months

DC Power (W)

Time

DC Power

Corrected I-V

PTC Power Rating

Results from this array appears to support findings from literature

24

Deployed in Golden ,CO

Jan-06
National Renewable Energy Laboratory Innovation for Our Energy Future

Development of Methodologies

Outdoor IV

Indoor IV

PVUSA

PR

Outdoor IV

Indoor IV

PVUSA

PR

Pre 2000 Post 2000

Percentage of Indoor IV has increased manifold  better tools 

Percentage PR has increased  more installations, easy to collect AC data, don’t 

necessarily need an entire weather station

Percentage PVUSA decreased significantly  pronounced seasonality & sensitivity 

to outliers

PVUSA methodology use has significantly declined

25
National Renewable Energy Laboratory Innovation for Our Energy Future

More than 40% of all Rd literature take only 1 or 2 measurements

1

2

discrete

continuous

10%

31%

25%

34%

Number of measurements

40% take only 1 or 2 measurements

1 Measurement: baseline no longer 

available or were never taken  have to 

compare to nameplate rating

0

10

20

30

40

50

60

70

0 50 100 150 200

Pmax (W)

Time (Months)

Rd= (0.33±0.07) %/year

Procedure: 

1.Take quarterly I-V data set

2.Randomly pick 2 data points & calculate 

Rd  repeat many times

3. Randomly pick 3 data points & calculate 

Rd  repeat many times

4.Rd will depend on # of data points & time 

span  can create 2D map 

Rd literature – Number of measurements

26
National Renewable Energy Laboratory Innovation for Our Energy Future

Effect of number of data points and years on Rd

0

5

10

15

20

0 2 4 6 8 10

Number of Data Points

Years

Mean Rd Standard Deviation

“True Rd”=-0.33 %/year (dark blue)

The curve is very steep for small data points and short time 

span

Even between 2-3 years can come close to “true Rd” simply 

by taken a few more data points

Would like to see more data points taken

27
National Renewable Energy Laboratory Innovation for Our Energy Future

Degradation Rates around the World

Size of circle: number of 

modules/systems tested

Köppen-Geiger climate map of the world (2007)

No reported degradation rates in many climate zones

28
National Renewable Energy Laboratory Innovation for Our Energy Future

Degradation Rates around the USA

Similar picture as from around the world  some climate 

zones have not been investigated

No reported degradation rates in some climate zones

Steppe

Desert Hot & humid

Climate Zones of the USA

29
National Renewable Energy Laboratory Innovation for Our Energy Future

Rainflow Calculations

*Quantifying the Thermal Fatigue of CPV Modules_Bosco__NREL_International Conference on Concentrating Photovoltaics_2010

Steppe Climate has high damage due to thermal cycling

Steppe, Hot & humid show significantly higher damage 

than Desert & Continental climate.

*

Steppe

Hot & 

Humid

30
National Renewable Energy Laboratory Innovation for Our Energy Future

Pre 2000 All Technologies Post 2000

Analysis of all Rd by climate

Steppe Climate shows significantly higher Rd before 2000

Steppe climate significantly higher. No significant difference.

31
National Renewable Energy Laboratory Innovation for Our Energy Future

Pre 2000 Post 2000

Analysis of Rd by climate – c-Si

Similar but not as distinct trend for c-Si

Use of automated equipment, low stress ribbon effect visible...?

Steppe Climate shows significantly higher Rd before 2000

32
National Renewable Energy Laboratory Innovation for Our Energy Future

PVDAQ Project

Use data from government-funded and other projects

Performance data accessible on web page

PV Data 

Acquisition

Eliminate blank spots on the map

33
National Renewable Energy Laboratory Innovation for Our Energy Future

Conclusion

34

 Uncertainty can result in significant warranty risk

 Time series Modeling with continuous data (PVUSA, PR ..) can significantly 

reduce uncertainty

 Cont. Data: Class. Decomp. May be a good compromise between quality of 

results & ease of implementation.

 Discrete data: better practice to take more than 2 measurements.

 Analysis from literature and our own systems indicate that degradation rates 

have improved for installations after 2000.

 Have no data from many of the world’s climate zones
National Renewable Energy Laboratory Innovation for Our Energy Future

Conclusion

 Uncertainty can result in significant warranty risk

 Time series Modeling with continuous data (PVUSA, PR ..) can significantly 

reduce uncertainty

 Cont. Data: Class. Decomp. May be a good compromise between quality of 

results & ease of implementation.

 Discrete data: better practice to take more than 2 measurements.

 Analysis from literature and our own systems indicate that degradation rates 

have improved for installations after 2000.

 Have no data from many of the world’s climate zones

Need more data!

35
National Renewable Energy Laboratory Innovation for Our Energy Future

Acknowledgments

Thank you to :

Sarah Kurtz

Ryan Smith

John Wohlgemuth

Nick Bosco

Peter Hacke

Bill Marion

Bill Sekulic

Kent Terwilliger

Rest of the NREL reliability team

Eric Maass 

Thank you for your attention!

dirk.jordan@nrel.gov

36
National Renewable Energy Laboratory 37 Innovation for Our Energy Future

g _ _ _ p_ pp

PVUSA progress report_Townsend_Endecon_1995.pdf

11 year field exposure_Reis_Humbold State CA_PVSC_2002.pdf

10 year PV review_Rosenthal_NM_PVSC_1993.pdf

Field test of 2400 PV modules_Hishikawa_Japan_PVSC_2002.pdf

Rd analysis c-Si_Osterwald_NREL_PVSC_2002.pdf

c-Si after 20 years_Quintana_Sandia_PVSC_2000.pdf

Rd c-Si_Machida_Japan_SEM&SC_1997.pdf

PV durability_King_Sandia_PiPV_2000.pdf

27+ years PV_Tang_ASU_2006.pdf

PV performance arctic college_Poissant_Canada_2006.pdf

20 year PV_Alawaji_Saudia Arabia_Renewable Energy Reviews_2001.pdf

Outdoor PV degradation_Vignola_UofOregon_ASES_2009.pdf

Perfromance analysis a-Si_Gregg_United Solar_PVSC_2005.pdf

Long-term performance of 8 PV arrays_Granata_Sandia_2008.pdf

Eff degradation c-Si_DeLia_Italy_2003.pdf

Long-term stability of the SERF_Marion_NREL_2003.pdf

double junction a-Si BIPV_Ruther_Brazil_2003.pdf

Performance losses_Mani_IPVR_2009.pdf

a-Si in different climates_Gottschalg_England_2001.pdf

a-Si in Sacramento_Osborn_Spectrum Energy_2003.pdf

Performance of a-Si in diff climates_Rüther_Brazil_2006.pdf

PV round robin_IEA Task2_Austria_2006.pdf

TISO-20 Jahre ARCO-PP.pdf

Anual report_LEEE-TISO_Switzerland.pdf

Field test in Mexico_Foster_New Mexico State_2005.pdf

c-Si of 22 years_Dunlop_EU_2006.pdf

Common degradation mechanism_Quintana_Sandia_IEEE_2003.pdf

Field test of c-Si in 1990_Sakamoto_Japan_PVenergyconv_2003.pdf

PV degradation_King_Sandia_2003.pdf

Outdoor PV in Sahara 3months_Saok_Algeria_2008.pdf

PV Greece_Kalykakis_Greece_2009.pdf

PV Korea_So_Korea_2006.pdf

Outdoor PV on Cyprus_Makrides_Cyprus_2009.pdf

DegRate for c-Si_Osterwald_NREL_2002.pdf

Outdoor testing at ASU_Mani_ASU_2006.pdf

PV Power production after 10 years_Cereghetti_Switzerland_2003.pdf

Degradation of a-Si_van Dyk_South Africa_SEM&SC_2010.pdf

20 year lifetime_Dunlop_EU_2005.pdf

Predicted long-term PV performance_Muirhead_Australia_PVScienceConf_1996.pdf

Long-term field age_Skoczek_Italy_2009.pdf

Degradationenergy payback_Davis_FSEC_2009.pdf

PV Performance analysis_Mau_Austria_2005.pdf

PV performance after field expsoure_King_Sandia_.pdf

Performance of 100 sites_Wolgemuth_BP_PVSC_2005.pdf

PV performance in different climates_Carr_Australia_SolarEnergy_2003.pdf

Outdoor monitoring_Dhere_FSEC_2005.doc

Energy rating for CPV_Verlinden_Australia_2009.ppt

Sytem performance_Wohlgemuth_IEEE_2002.pdf

performance of First Solar_Marion_NREL_2001.pdf

 

 

 

 

 

 

 

 

p _ __p

Degradation rates from PERT_Osterwald_NREL_2005.pdf

TEP study_Moore_Sandia_PIPV_2008.pdf

Improved Power ratingsd_Kimber_PVSC_2009.pdf

Degradation x-Si_Skoczek_Italy_PPV_2009.pdf

Field PV reliability_Vazquez_Spain_2008.pdf

PV degradation_Moore_Tucson Electric_2007.pdf

Relaibility 1kW a-Si_NREL_Adelstein_2005p.pdf

PV performance parameters_Marion_NREL_PVSC_2005.pdf

6 years 100 sites summary_Ransome_BP_PVSC_2005.pdf

Performance of a-Si in diff climates_Rüther_Brazil_2006.pdf

A-Si in Kenya_Jacobsen_Berkley_ASES_2000

Outdoor testing at ASU_Mani_ASU_2006.pdf

Field PV reliability_Vazquez_Spain_2008.pdf

PV durability_King_Sandia_PiPV_2000.pdf

PV in Saxony_Decker_Germany_1997.pdf

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 y_ _ y_ p

25 yearold PV modules_Hedstroem_Sweden_2006

Sunpower reliability_Bunea_Sunpower_PVSEC_2010.pdf

C-Si degradation_Morita_Japan_PVenergyconv_2003

Rd c-Si_Machida_Japan_SEM&SC_1997.pdf

c-Si after 20 years_Quintana_Sandia_PVSC_2000.pdf

Rd analysis c-Si_Osterwald_NREL_PVSC_2002.pdf

10 year PV review_Rosenthal_NM_PVSC_1993.pdf

11 year field exposure_Reis_Humbold State CA_PVSC_2002.pdf

PVUSA progress report_Townsend_Endecon_1995.pdf

Long-term PV FL_Hickman_FSEC_NRELworkshop_2010.ppt

Degradationenergy payback_Davis_FSEC_2009.pdf

PV degradation in moderate climate_Tetsuyuki_Japan_PiPV_2010

Outdoor PV Degradation comparison_Jordan_NREL_PVSC_2010.pdf

PV module characterization_Eikelboom_Netherlands_2000.pdf

Mean time before failure_Realini_ Switzerland_2003.pdf

PV EVA browing Negev Desert_Berman_Israel_SEM&SC_1995.pdf

PV modules Gobi desert_Adiyabat_Mongolia_PVSC_2010.pdf

Degradation rates from PERT_Osterwald_NREL_2005.pdf

PV performance parameters_Marion_NREL_PVSC_2005.pdf

Long-term reliability_Wohlgemuth_BP-2008.pdf

Outdoor Rd wo Irr_Pulver_UofA_PVSC_2010

20+ years PV_Bing_Massachusetts_PVMR2010_2010.pdf

Rd c-Si in Spain_Sanchez_Spain_PiPV_2011.pdf

Degradation Analysis PV plants_Kiefer_Fraunhofer_PVSEC_2010.pdf

Degradationsmessungen_Becker_Munich_2005.pdf

Long-term PV tsting_Hawkins_Australia_PVsec_1996.pdf

PV power at Telstra_Muirhead_Telstra_Au&NZSolar_95.pdf

Field performance a-Si_Osborn_CA_2008.pdf

Power drop rate_Kang_Korea_PVSEC_2010.pdf

PV reliability in 4 climates_Bogdanski_Germany_PVSEC_2010.pdf

Long-term CIS_Musikowski_Germany_PVSEC_2010.pdf

735 c-Si modules after 1 year_Coello_Spain_PVSEC_2010.pdf

PV performance arctic college_Poissant_Canada_2006.pdf

Notation: Title_Author_Institute/Country_Journal/Conference_Year