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Minimizing Variation in Outdoor CPV 

Power Ratings

2011 Reliability 

Workshop, Golden CO

Matthew Muller

2/16/2010

NREL/PR-5200-51122
NATIONAL RENEWABLE ENERGY LABORATORY

Outline

•NREL CPV testbed and its use in examining outdoor CPV power ratings

•IEC 62670 ,outdoor CPV power ratings, motivations and challenges

• ASTM E2527, a method for rating CPV module power outdoors

•Month to month variation in power ratings using ASTM method

•Attempts to improve the ASTM regression or develop an alternative

•Results to modifications to regression filtering and additional approaches

•Conclusions/Recommendations

2
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NREL CPV Testbed & Outdoor Power Ratings

3

• 2-axis tracker (+/-0.15 degree sun 

pointing error)

• Data acquisition provides module 

peak power tracking

• IV sweeps, 5 minute intervals

• DNI, GNI, wind speed, Tambient, 

Tmodule, and tracking error are 

measured

• Spectral data (SRRL)

• A unique data set of various CPV lens and module architectures

• Understanding CPV performance variation and supporting standards development
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IEC 62670 and Outdoor CPV Power Ratings

4

Concentrator Standard Operating Condition (CSOC)

Tambient (20C), DNI (900W/m2), Wind speed (2m/s) G173/AM 1.5 Spectrum
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Outdoor Power Ratings, WHY? HOW? 

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• A need to characterize module thermal performance

• Flat plate, ~25C above ambient at 1sun

• CPV , ?? Degrees C above ambient

• 300-1300X concentration varies the heat dissipation needed

• Not all heat sinks are created equal

• Outdoor temperatures  module/lens expansion/contraction 

• Does this impact performance?

• A need to characterize module spectral performance 

• It is well know that performance of triple junction cells is dependent on 

spectrum but how does this play out in a module and can it be quantified 

without specialized data

• If the above is the WHY? What about HOW?

• IEC62670 describes CSOC but has not established a clear method

• Method must provide consistent results from lab to lab (<5% variation)

• Method must work for various CPV architectures without specialized 

information from the manufacturer
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ASTM E2527, An Existing Method

CPV Module Power Rating Outdoors 

• Measure module maximum power and meteorological parameters

•Apply the following regression to the data set, reject if standard error too high 

Pmax=DNI(a1+a2DNI+a3Tambient+a4Wind)

Report Pmax @(Tamb=20C,W=4m/s,DNI=850W/m2)

•In this presentation CSOC or (Tamb=20C,W=2m/s,DNI=900W/m2) is used

•ASTM calls for the following restrictions on data for the regression

• 10C <= Tamb <= 30C

• DNI >= 750W/m2

• The average wind speed for the 5 minutes prior to a measurement is <= 5m/s

• Reject if visible clouds are within 10 degrees of sun

•Cloud restriction is approximated by rejecting a 10 minute DNI deviation >2%

•Other restrictions applied above and beyond ASTM criteria

• Reject if Diffuse radiation is > 140W/m2

• Reject if Tracking error is > 0.15 degrees

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60

65

70

75

80

85

90

750 850 950 1050

Power W

DNI W/m2

ASTM regression Measured

ASTM 2527 Applied to Sept 2010 data

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Rated Power = 77.5 W 

@ Tamb=20C,W=2m/s, DNI=900W/m2

 How much will the Rated Power for a CPV module if this procedure is 

applied over multiple months??

 The answer not only depends on outdoor conditions but also on the 

module technology! 
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• The graph shows the % 

variation in a modules 

monthly power rating as 

compared to lowest monthly 

rating over the 2 years

•For example, CPV1 had its 

lowest rating in month 12, 

while the rating in month 11 

was almost 6% greater

•Note that CPV2 appears to 

be degrading in the 2nd year.

8

0

2

4

6

8

10

12

14

0 5 10 15 20

% change in power rating

Months 0=Jan2009

%Variation in Module Rated Power 

ASTM2527 

CPV1 CPV2 CPV3 FlatPlate

•WHY so much variation with the regression results?

•AM neglected? 

•Lens performance neglected?

•Moisture in modules, soiling, alignment varies?

•Regression gives too much weight to outliers?
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Can other variables improve the regression?

9

Efficiency correlates with the 

following

•Tambient

•Tmodule

•DNI and GNI

•AM

Efficiency (after temp correction) 

correlates with 

•DNI and GNI

•AM

•Precipitable water vapor (moderate 

to weak)

•Turbidity (weak)

•In some cases there is positive 

correlation with Tambient, consistent 

with lenses that go out of alignment 

at low temperatures
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•New regressions were tested on yearly data sets using linear and nonlinear 

combinations of the variables on the previous slide

•The “best fit” for yearly data, judged by lowest standard error and the Tstat on the 

included variables, was Powertemp corrected =a1DNI+ a2DNI2+a3AM+a4WaterVapor

• The existing ASTM regression with AM included also performed well.

•When the “best fit” and the modified ASTM regression were tested on monthly data 

the monthly variation in power became worse. (Graphs show one reason why)

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14

15

16

17

18

19

20

21

22

1 2 3 4 5 6 7 8 9 10

Efficiency (%)

AirMass

9 months CPV data

Regressions examined
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• The graphs on the previous slide show how monthly 

regressions can fail to accurately model spectral performance 

variations

• As an alternative, can spectral filtering (specifically limitting

airmass from 1.3 to 1.7) reduce variation in the ASTM power 

rating?

•As another alternative, would variation be reduced if monthly 

data is corrected for temperature, airmass, and precipitable

water vapor before running a simple regression using DNI and 

DNI2?

• Only accept airmass values less than 2

• Assume efficiency increases 1% absolute per unit of AM

• Assume efficiency increases 0.6% per cm of PWV

• Exclude any data points that have a PWV below 0.4cm

• Exclude ambient temperatures below 15C

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Regression modification results CPV1

12

0

1

2

3

4

5

6

7

8

9

10

0 5 10 15 20

% change in power rating

Months 0=Jan2009

CPV1 (filtering and regression changes) 

ASTM ASTM (1.3-1.7AM) TEMP & SPECTRAL correction

• The tight filter on airmass eliminates Dec ,Jan but there is still ~9% variation

• The simple regression with corrected data reduces variation to ~4%
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Regression modification results CPV2

13

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20

% change in power rating

Months 0=Jan2009

CPV2 (filtering and regression changes) 

ASTM ASTM (1.3-1.7AM) TEMP & SPECTRAL correction

• Airmass filtering again removes Dec,Jan but variation is still high

• Ignoring 2010 degradation, corrected data has a variation of ~6%
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Regression modification results CPV3

14

0

2

4

6

8

10

0 5 10 15 20

% change in power rating

Months 0=Jan2009

CPV3 (filtering and regression changes) 

ASTM ASTM (1.3-1.7AM) TEMP & SPECTRAL correction

• Again airmass filtering removes Dec, Jan with only a slight reduction in variation

• Corrected data still has a variation of 6.5%, June 2009 looks questionable
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Conclusions and Recommendations

15

•Data sets for outdoor CPV power ratings should not 

include data points with a geometric airmass greater than 2

• Performance begins to decline at airmasses greater than 2 or 3

• This does little to help predict performance at AM1.5

• Ambient temperatures below 15 C have the potential to 

skew power ratings due to lens misalignment at low 

temperatures (true for some modules)

• Monthly regressions as a method for calculating CPV 

outdoor power ratings show too much variation (At least for 

data collected in Golden Colorado)

•Correcting outdoor data for temperature and spectral 

variation shows some potential to reduce month-to-month 

variation in outdoor CPV power ratings.