Field data requirements for the validation
of PV module performance models
4th PV Performance Modelling and Monitoring Workshop
Dipl phys. Gabi Friesen
Introduction
Test issues and requirements
Typical measurement uncertainties
Conclusions
Outlook
OUTLINE
Who is interested in module field testing?
• Planners kWh/Wp, kWh/m²
• PV module manufacturers ΔkWh/Wp (benchmarking)
• R&D (product development) kWh/year, kWhloss/year
• Software developers kWh±x%
• Standardisation groups kWhrating
• Financial kWhlifetime/€
• Equipment manufacturers kWh test requirements
• …
PV MODEL • Yield
• Lifetime
• Failure
• Degradation
Why do we need accurate field data? Laboratory testing – short term harmonised standard test conditions
Field testing – long term real operating conditions - site dependent
prediction
validation
IEC60904 1-10, IEC60891, IEC61853 (power
rating), IEC61215/IEC61646 (design approval),
IEC61730 (safety approval)
• Clear test procedures with
uncertainty declarations
• Measurable repeatability
(reproducible test conditions)
• Easy comparability (round robin’s)
IEC61724 (PV system performance monitoring)
not fully applicable to module testing!
ISSUES
• Not harmonized test practices
• Missing uncertainty declarations
• Time/weather specific results and
uncertainties
• Degradation issues
Why test in different climates?
Different local conditions irradiance
temperature
humidity
UV exposure
spectrum
soiling
temperature variations
…
different module performance
and degradation rates
To better understand the
differences reliable, accurate
and comparable
measurements are needed!
• Literature reports typical combined measurement uncertainties for kWp/Wp
of around±5%. The measurement uncertainties exceeds the technological
differences measured with high level equipment, but on the other hand there
are no detailed studies on the uncertainties!
• High discrepancies in measurement practice, understanding, uncertainty
determination and reporting can easily lead to contradictory results. Modules
ranked as the ‘best’ in one study comes out as ‘average’ in another study!
• Blind modeling round robins demonstrated large spread in predictions, which
requires further validation. Unfortunately most of the available field data are
reported without any measurement uncertainty making validation of models
very difficult.
• Field data are affected by reversible and not reversible degradation effects
which are not always taken into account when comparing different
technologies. Most studies are limited at first-year energy yields, but what is
more relevant is the life-time energy yield.
Harmonization of testing practices and reporting is the key!
How reliable are today module field data?
Field testing requirements
test device
test equipment
test site
test/data processing
Generally the focus is
on the measurement
equipment used for
testing!
Field testing requirements
test device • reference power (kWh/Wp)
• selection of modules (tolerance)
test equipment • hardware definition
• hardware accuracy
test site • non- homogeneities
• hardware exposure
test/data processing • data quality
• data processing & reporting
test device
• selection procedure which considers
statistical distribution
• full electrical characterisation accord.
IEC61853
• sorting of defect or damaged modules
• min. sample number (cross correlation)
• stabilisation of all modules
• stability check over time (annual
degradation rate)
• reference module (stored in the dark)
Wp±t1 → kWh/Wp±t2
Module sampling!
test device • selection of modules
• reference power (kWh/Wp)
test equipment • hardware definition
• hardware accuracy
test site • non- homogeneities
• hardware exposure
test/data processing • data quality
• data processing & reporting
test equipment
• regularly calibrated high precision
instruments and sensors
• class A solar simulator for STC meas.
• same solar simulator for all STC meas.
(repeatability dominates the uncertainty)
• max. measurement interval (1-5 min)
• MPP loading
• multiple sensors for cross verification
• check for transient effects (IV & MPPT)
• Synchronisation of measurements
(meteo/ module parameters)
• 4 wire measurements
• proper grounding
High standard equipment!
Field testing requirements
test device • selection of modules
• reference power (kWh/Wp)
test equipment • hardware definition
• hardware accuracy
test site • non- homogeneities
• hardware exposure
test/data processing • data quality
• data processing & reporting
test site
• stand configuration (modules mounted
top or next to each other, min distance
from ground)
• module mounting
• distribution and number of sensors
• alignment control of modules and
sensors
• irradiance homogeneity (reflections)
• temperature/ventilation homogeneity
• albedo
• shadows/horizont
Site characterisation!
Field testing requirements
test device • selection of modules
• reference power (kWh/Wp)
test equipment • hardware definition
• hardware accuracy
test site • non- homogeneities
• hardware exposure
test/data processing • data quality
• data processing & reporting
test/data processing
• maintenance
• cleaning procedure
• error/quality markers
• alert system
• cross verification (stability check)
• uncertainty calculation
• harmonised reporting
Harmonisation,
uncertainty calculation
& quality control!
Field testing requirements
test device • selection of modules
• reference power (kWh/Wp)
test equipment • hardware definition
• hardware accuracy
test site • non- homogeneities
• hardware exposure
test/data processing • data quality
• data processing & reporting
Field testing requirements
The consideration of all
these aspects allows:
• better comparability
of data with clear
measurement
uncertainties.
• better validation
studies and models.
• higher confidence in
technology
benchmarking.
Uncertainty contributions in field data
I,V meas.; 0.25%
MPP extraction; 1.0%
test site non-uniformity ;
1.0%
other; 0.5%
STC rating; 2.0%
product variability (TC,
LLE, SR, …); 2.0%
irradiance meas.; 3.0%
PR
combined measurement
uncertainty (k=2) 4.5% combined measurement
uncertainty (k=2) 1.5%
Typical uncertainties in Performance Ratio (PR) measurements.
I,V meas.; 0.25%
MPP extraction; 0.5%
test site non-uniformity ;
1.0%
other; 0.50%
STC rating; 1.0%
product variability (TC,
LLE, SR, …); 0.5%
irradiance meas.; 3.0%
ΔPR (ranking)
Conclusions
• Module field testing data are affected by many measurement issues
which has to be quantified and reduced to:
− increase the confidence into new PV module models (validation
studies).
− increase the confidence in a future energy rating standard
(demonstration of the validity of calculated rankings).
• The main uncertainties are coming from the experimental design, the
power rating, the irradiance measurement and the product variability.
• A standardized method for the assessment of uncertainties and the
reporting of results is needed.
• A standardized method for the measurement of degradation
parameters is needed to support the development of new degradation
models.
• Survey on outdoor measurements and its uncertainties → best practice guideline
• Creation of an open-source reference data base
• Presentation of climate and technology specific
performance figures
Source: Report IEA-PVPS T13-02:2014, May 2014
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IEA TASK 13 initiative
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