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.

Minimizing Variation in Outdoor CPV 

Power Ratings

CPV-7, Las Vegas, 

Nevada

Matthew Muller

4/4/2011

NREL/PR-5200-51370
NATIONAL RENEWABLE ENERGY LABORATORY

Outline

• An overview of 6 months of CPV and tracked PV power data

• Analyze NREL’s data set in an attempt to answer 3 questions:

• 1) How much variation is actually seen in outdoor power measurements

if tight filtering is placed on meteorological and spectral conditions?

• 2) How much variation is seen in monthly power ratings using a 

regression based approach?

• 3) Are there alternative methods to regression that result in less 

variation?

• 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 available

• 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, Outdoor or Indoor CPV Power Ratings

4

Concentrator Standard Operating 

Conditions 

(CSOC)

Tambient = 20C

DNI = 900 W/m2

Wind Speed 2m/s

G173/AM 1.5

Calculate Cell Temperature

Concentrator Standard Test 

Conditions 

(CSTC)

Tcell = 25C

DNI = 900 W/m2

G173/AM 1.5

Cell Temperature Fixed

Temperature

Coefficients
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Power Ratings at CSOC but How? 

5

• IEC62670 describes CSOC but has not established a clear method

• ASTM-2527 regression 

• ISFOC translation

• Consider other approaches

• Goal of work is to find the method that minimizes variation
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Power Ratings, Autonomously Collected MONTHLY Data 

6

• Filtering is applied to all data (based on ASTM-2527 data restrictions)

• ASTM-2527 calls for the following:

• 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 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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6 Months of Data with Basic Filtering in Place 

7

0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050

Power/RatedPower

DNI (W/m2)

CPV

0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050 1150

Power/RatedPower

GNI (W/m2)

Flat-Plate

• The plots below show about a 10% variation in the power produced at a 

given DNI for both flat-plate and CPV for a July – December 
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Same 6 Months of Data but Apply Temperature Correction 

8

0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050

Power/RatedPower

DNI (W/m2)

Temperature Corrected CPV

0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050 1150

Power/RatedPower

GNI (W/m2)

Temperature Corrected Flat-Plate

• The flat-plate variation is now < 5% while CPV variation decreased slightly 

at high DNI, increased for the lowest DNI and a bend is now more 

prominent at 850W/m2
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Apply Spectral Filtering in Combination with Temp Correction 

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0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050

Power/RatedPower

DNI (W/m2)

Temperature Corrected CPV

0.7

0.8

0.9

1

1.1

1.2

1.3

750 850 950 1050

Power/RatedPower

DNI (W/m2)

Temp Correct and AOD, PWV filter

• Filter the CPV data to only accept Precipitable Water Vapor (PWV) between 

1-3 cm and Aerosol Optical Depth (AOD) between 0.06-0.135

• Now the variation for CPV is about 5%. Additional filtering was considered 

based on geometric airmass (AM). Excluding AM>2 removes almost all 

data below the bend or below 850 W/m2
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60

65

70

75

80

85

90

750 850 950 1050

Power W

DNI W/m2

ASTM regression Measured

ASTM E2527 as an Example

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Rated Power = 77.5 W 

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

 How much will these results vary if applied over multiple months??

• Apply the ASTM regression to the data set, reject if standard error > 3% 

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

Report at CSOC (Tamb=20C,W=2m/s,DNI=900W/m2)

Example using September data
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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

•The ASTM power rating varies

about 10% for CPV and 5% for

flat-plate

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• CPV modules have their lowest ratings in the winter months

•(At NREL, December, January minimum AM is > 2, November, February 

minimum AM is > 1.7)

•Some modules show lens performance issues below 15C

• Even for flat-plate the regression coefficients don’t adequately model known

performance variation

0

1

2

3

4

5

6

7

8

9

10

0 5 10 15 20

% change in power rating

Month 0=Jan2009

CPV1 CPV2 FlatPlate
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• Spectral filtering considered hereafter inadvertently removes some months leading to

an unfair comparison between methods

• Excluding Nov-Feb, the ASTM variation reduces to 7.6% for CPV1 and 6.8% for CPV2

• All comparison hereafter exclude AirMass > 2 (Graphs below show why) 

12

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

Exclude highly variable winter months in comparing Power 

Rating Methods
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Approaches to Reducing Outdoor Power Rating Variation

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• 1) Modify ASTM

•ASTM1: added filtering 1.3≤AM≤1.7

•ASTM2: add AM term, Pmax=DNI(a1+a2DNI+a3Tambient+a4Wind +a5AM)

• 2) Build new regression from scratch considering all relevant parameters

•The “best fit” on a year of data using statistical indicators

PT =a1DNI+ a2DNI2+a3AM+a4PWV

• The PWV term was not valid on a monthly basis

• REGRESSION1: PT=a1DNI+a2DNI2+a3AM

• 3) Apply more translation/correction to the data and less regression

• REGRESSION2: PT,AM,PWV= a1DNI+a2DNI2

• PT,AM,PWV is a translation to CSOC cell temp, airmass of 1.5, PWV of 1.4cm

• assumes module efficiency increases 1% per AM unit and 0.6% per PWV unit

• ISFOC: Power translation used by ISFOC

• ISFOC1: translation with added filtering 1.3≤AM≤1.7

• ISFOC2: translation with above correction for AM and PWV
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Comparison of Power Rating Methods

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• Attempts to improve on ASTM-2527 failed as variation increased

• “Regression1” showed minor improvement, AM coefficient was positive in some months,

negative in others

•“Regression2”, translation for all but DNI shows even more improvement

• Both “ISFOC” and “ISFOC1” show some improvement of ASTM-2527 but are not as good as

“Regression2 which include correction for AM and PWV

• The variation is the lowest for both modules when “ISFOC2” is used, ISFOC with AM, PWV

corrections

Maximum Monthly Variation in Power Rating

Method CPV1 CPV2

ASTM 2527 7.6% 6.8%

“ASTM1” 8.7% 8.1%

“ASTM2” 8.0% 7.3%

“Regression1” 5.4% 6.7%

“Regression2” 4.2% 5.7%

“ISFOC” 6.9% 6.2%

“ISFOC1” 6.2% 6.1%

“ISFOC2” 2.9% 5.5%
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Conclusions and Recommendations

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• Applying filtering to a year of CPV data showed that power variation could be

reduced to ~5% for a given DNI

• Regression based approaches faired poorly in attempts to minimize the 

variation in a monthly power rating

•Translation/Correction based approaches worked best with maximum monthly

variation ranging from 2.9-5.7%

General suggestions/recommendations: 

1) Exclude AM >2, PWV <0.5 from power rating data. 

2) Exclude months that AM 1.5 does not occur.

3) PWV and AM should not be ignored.

4) Default to translation over regression approach.

5) Do not ignore temperature effects on lenses. 
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Future Work

16

• Apply translation based approaches to more modules and 

over longer time periods in order to confirm the results 

presented here. 

•Efforts should be made to refine corrections for AM and 

PWV or to apply alternate corrections as data becomes 

available that improves ability to characterize the spectral 

conditions. 

•Finally, corrections for variations in lens performance 

should be considered. 
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Questions?

17

Thanks!