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

FOCUS OF THE PRESENTATION

PV Performance Modeling Collaborative - https://pvpmc.sandia.gov/

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

Thank you for your attention!