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.

Recent Photovoltaic 

Performance Data in the USA

Photovoltaic Module Reliability Workshop (PVMRW)

Golden, CO

Dirk Jordan, Sarah Kurtz

February 25, 2014

NREL/PR-5200-61446
2

Outline

 Introduction

 American Recovery & Reinvestment Act data

 Performance relative to predicted

 Reliability categories

 First geographic trends

 Impact of reliability categories

 Conclusion
3

Recent Negative Headlines about Solar Industry

Quality issues threaten to give solar a black eye, PVTECH , F.Carus, 15 January 2013

How Bad Is PV Panel Performance?, IEEE Spectrum, B. Sweet, 30 May 2013 

Solar Industry Anxious Over Defective Panels, New York Times, T. Woody, May 28, 2013

 Documenting field performance data, especially on a large scale

Has the recent PV price decline led to reliability issues?

D. Feldman et al., Q3 2013 /Q4 2013 Solar Industry Update, 2014 Edition
4

Geographic Distribution of Systems 

50,000 Systems

1.7 Gwatts capacity

Up to 4 years of data

Data filtering rules:

 Nameplate=production year1,2,3,4  reject production years

 Predicted capacity factor: Predicted AC energy/(24*365*DC rating)*100= 3-40%

 Year1 production = Year2 production  reject year2 entry

 Annual production = predicted production  reject

Data available:

AC production, 

Predicted AC production

Zip code location

Nameplate

Comments

Not available:

Insolation

Orientation

American Recovery & Reinvestment Act, Section 1603
5

No Evidence of Massive failure

Data Points:

Year 1 Year 2 Year 3 Year 4

18,632 5,825 479

Normal

Hardware

No information

91.5

0.7

0.8

5.8

0.9

0.2

95.3

0.6

1.0

1.3

0.9

0.9

97.6

0.5

0.1

0.7

0.6

0.7

95.8

0

0.2

2.3

0.8

0.8

Hardware

No information

Project

Weather

Data Collection

48,259

Year of Operation:

Project/Site: Delay, grid interconnection, construction etc.

Weather: Snow, soiling, lightning, hurricanes etc.

Data Collection: Data acquisition, less than a year of data, etc. (not performance related)

Hardware: Inverter, module, breaker, fuses etc.
6

Most systems produce as expected

Project issues data points tend to be farther away from unity line 

They don’t occur that often (previous slide) but if they do they tend to have larger impact

Measured vs. predicted production for all operation years
7

Median

/ Stdev Year1 Year2 Year3 Year4

Normal 1.05 ±

0.14

1.06 ±

0.14

1.04 ±

0.14

1.03 ±

0.13

Project 0.84 ±

0.24

0.61 ±

0.37

0.29 ±

0.36 -

Data 

Collection 

0.90 ±

0.41

0.76 ±

0.35

0.71 ±

0.21

0.65 ±

0.19*

Hardware 0.86 ±

0.23

0.77 ±

0.24

0.74 ±

0.18

0.74 ±

0.42*

Weather 0.87 ±

0.22

0.78 ±

0.20

0.45 ±

0.29* -

No 

information

0.25 ±

0.37

0.48 ±

0.47

0.50 ±

0.16

0.54 ±

0.12*

Count

Ratio: measured production/predicted production

Ca. 90% of normal data produce at least 90% of predicted

Normal data produce a few percent more than expected

 may have significant impact on performance based incentive programs

Normal data

* Indicates less than 10 data points
8

20

30

40

50

60

80

10

20

30

40

Year2 yield

1 2 3 5 10 30 100 1000 10000 5

NP with corrections

Year 2

20

30

40

50

60

80

10

20

30

40

Year1 Yield

1 2 3 5 10 30 100 1000 10000

NP with corrections

Year 1

0

0

0

0

0

0

0

0

0

0

1 2 3 5 10 30 100 1000 10000 5

NP with corrections

20

30

40

50

60

80

10

20

30

40

Year3 yield

1 2 3 5 10 30 100 1000 10000

NP with corrections

Year 3 Year 4

Yield (kWh/kWp)

Nameplate Rating (kW)

Mean yield: 1444± 282 kWh/kW Mean yield: 1454± 278 kWh/kW

Mean yield: 1450± 258 kWh/kW Mean yield: 1427± 208 kWh/kW

No trend with nameplate rating

Yield appears wider for smaller systems  caused by higher point density

Yield (AC energy/nameplate rating) vs. nameplate & year
9

20

30

40

50

60

80

10

20

30

40

Year2 yield

1 2 3 5 10 30 100 1000 10000 5

NP with corrections

Year 2

20

30

40

50

60

80

10

20

30

40

Year1 Yield

1 2 3 5 10 30 100 1000 10000

NP with corrections

Year 1

0

0

0

0

0

0

0

0

0

0

1 2 3 5 10 30 100 1000 10000 5

NP with corrections

20

30

40

50

60

80

10

20

30

40

Year3 yield

1 2 3 5 10 30 100 1000 10000

NP with corrections

Year 3 Year 4

Yield (kWh/kWp)

Nameplate Rating (kW)

Mean yield: 1444± 282 kWh/kW Mean yield: 1454± 278 kWh/kW

Mean yield: 1450± 258 kWh/kW Mean yield: 1427± 208 kWh/kW

No trend with nameplate rating

Yield appears wider for smaller systems  caused by higher point density

Yield (AC energy/nameplate rating) vs. nameplate & year
10

Yield distributions similar for different system size

Note: individual 

histograms have 

different count 

scales

Distribution shapes for the AC yield for different rating bins as a function of year

Nameplate rating

AC yield
11

Data collection issues fall by order magnitude

0

5

10

15

20

25

30

35

40

Count

DAQ Problem Missing Data Poor estimat

Y 3 D C ll 

0

20

40

60

80

100

Cum Percent

Year 1 Year 2 Year 3

0

20

40

60

80

10

12

14

16

18

20

22

24

26

Count

Missing Data DAQ Problem Poor estim

Y 1 D t ll t

Less than 1 year: 2.1

DAQ Problem: 3.5

Data collection-related issues 

Mean missed time (months)

0

50

10

15

20

Count

DAQ Problem Missing Data Poor estima

 

Cumulative percentageCumulative percentageCumulative percentage

Less than 1 year: 2.2

DAQ Problem: 4.5

Less than 1 year: 2.1

DAQ Problem: 5

DAQ: data acquisition problem such as data transmission problem, internet outage etc.

Missing data: Less than a year before data were entered 
12

Project-related issues are dominated by delays

 

 

Count Percentage

0

1

2

3

5

10

20

30

50

10

20

0.0

0.

0.2

0.3

0.4

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

Year

Percentage (%) Count 

Year 

Delay: Permitting & construction

Utility: Grid interconnection

Construct.: Roof repair, remodeling... 

Fire: fire at site not caused by PV 

PV was turned off as precaution

Design: addition of new part-array @ 

different orientation

Financials: shutdown due to 

foreclosure

Not used: PV system not used as 

often

 Delay & grid interconnection dominates 1st year, rapidly declines in subsequent years

 Construction shows small percentage increase over years

 Fire can have broader impact due to precautionary PV system shutdown

 Financial reasons correlated to states with large housing price cycles
13

Hardware-related issues are dominated by inverter

 

Count Percentage

1

2

3

5

10

20

30

50

10

20

0.0

0.

0.

0.

0.

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

Year

Percentage (%) Count 

Year 

Repair: Unspecified repair & outages

Unauthorized shutdown: May be 

mitigated by interconnect locks

Less than 0.1% are due to defective modules 
14

Weather-related issues dominated by snow & hurricanes

2010 2011 2012

Hurricane Sandy

Hurricane Isaac

Hurricane Irene

Hurricane Sandy

Hurricane: Typically, down time due to grid outage, not because of damage to system 

Lightning: Not direct hits to PV system but transformer
15

20

0

20

40

60

80

10

 Loss Percentage (%) Loss Percentage (%)

Year 1

Year 2 

Annual loss impact (%) of different categories

Module recalls do not occur often but if they do they can have large impact

Performance in anomalous 

year compared to normal 

year  loss estimation 

due to cause of anomaly

(assumes the same annual 

production)

Diamonds: 95% 

confidence interval

Crossbar: Mean

Project Hardware Weather
16

Conclusion

 Analyzed field performance of 50,000 PV systems

 More than 90% of normal systems produce at least 90% of expected

 Project problems may not occur as often but have large impact

 Project-related problems are dominated by delays in the first year of 

operation, then rapidly declines in subsequent years.

 Hardware issues dominated by inverter, particularly in the first year of 

operation

 Less than 0.1% are due to defective or underperforming modules

 Module recalls can lead to significant annual production losses 

 Weather-related issues are dominated by snow and hurricanes
17

Acknowledgments

US Department of the Treasury, Section 

1603 of the American Reinvestment and 

Recovery Act

Edward Settle

Jason Coughlin

Doug Gagne

Thank you for your attention

National Renewable Energy Laboratory

15013 Denver West Parkway, MS 3411

Golden, CO 80401, USA

dirk.jordan@nrel.gov

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08-GO28308 

with the National Renewable Energy Laboratory