determining the causes and rates of pv degradation using the loss factors model (lfm) with high...

www.steveransome.com25-Oct-16 1© SRCL / Gantner InstrumentsPVPMC #6 Freiburg

Determining the causes and rates of PV degradation using the Loss Factors Model (LFM)with high quality IV measurements

Steve Ransome1 & Juergen Sutterlueti2

1Steve Ransome Consulting Limited, London UK2Gantner Instruments, Germany

PVPMC #6 – Freiburg Germany

25th Oct 2016

http://www.steveransome.com/


Introduction to degradation analysis

• Most reported PV degradation are STC corrected efficiency only (1kW/m2, 25C, AM1.5, AOI=0, direct only)

• ISC variability dominates performance uncertainty (due to soiling, spectral effects, irradiance sensor calibration …)

Can we analyse other parameters independently of ISC ?

• The cause of degradation (e.g. RSHUNT, RSERIES, VOC )gives site dependent energy yield degradation rates (due to differing proportions of insolation vs. irradiance, TMOD etc.)



Smooth IV curves are needed for good RSC and ROC calculations“Rn = Apparent resistance between adjacent data points”

Typical GI measured IV curve (CdTe) GI raw measured (smooth data) vs.

synthesised “poor” data

truncated accuracy and added noise

𝑹𝒏= −∆𝑽∆𝑰

= −𝑽𝒏− 𝑽𝒏−𝟏𝑰𝒏− 𝑰𝒏−𝟏

Worse RSC accuracy from synthesised data (e.g. truncated or noisy)

ISC, RSC

VOC, ROC



Checking IV data quality with Log Resistance-Voltage (RV) curves GI data much smoother than NREL’s Daystar and therefore easier to fit.

Can ignore a few “bad end points” with V~0 or V > VOC

GI CdTe

NRELCdTe



SRCL/Gantner “Loss Factors Model” [LFM]GI data

Measure raw IV curves = f(G,T)

Fit lines to RSC and ROC

Normalise data to datasheet

6 normalised losses LFM

PRDC = nISC*nRSC*nIMP * nVMP*nROC*nVOC

Cell mismatch, shading

Cell rollover

Curvature for better understanding



Comparing “Loss Factors Model” with standard models

LFM

1-diode (and similar models)

Fit to IV curves Exact values for 8 parameters around every IV curve

“Best fit to whole curve” depends on data point distribution/weighting “imperfect traces” e.g. cell mismatch, roll over

Normalised values for module variability

Yes. e.g. “nRsc = 98.0 ± 2.0%”

No. Specific module data only e.g. “RSHUNT = 1234Ohms”.

IndependentParameters ?

Almost independent(nVOC depends a little on nRSC and nISC)

No. Parameters are often interdependent e.g. nF and Io

Dependence onIrradiance and temperature

Simple optimum fits giveexact coefficient behaviour for low light,temp coeffs etc. each module

Try to fit pre-defined equations (even if they don’t fit data) e.g. RSHUNT (GI), I0(TCELL) etc. low light and temp coeffs. may be wrong.

Separation of all inputs e.g. ISC ~ AOI, SR

Not needed. Can just measure outdoor params(for ISC separate clear from cloudy skies)

Need to separate all parameters ISC = ISC0 * f(AOI) * f(SR) …

Fault finding and quantification of loss

Yes. Can easily identify quantify Cell mismatch, shading, R and VOC changes etc.

Some are possible (e.g. RSHUNT, RSERIES) but not mismatch, rollover etc.



Yearly IV traces by irradiance Sept. 2010-16 GI data

Discrepancies seen at very low light levels ?

Changes in LFM parametersΔnISC ΔnRSC

ΔPRDC

ΔnROC

ΔnVOC

For each module and Irradiance (e.g. ~0.8kW/m²)

ISC variability e.g. soiling, sensor calibration etc.



Yearly IV traces by irradiance Sept. 2010-16 GI data

Discrepancies in ISC seen at very low light levels0.04kW/m²Why ?

Year Deg

%/y

If module is degrading it’s worse at low light 1.0 0.3 kW/m2



GI Tempe OTF from North to South East Low horizon shading for morning sun

GI hut position redPower lines green

Sensors CyanModules Magenta

Google Street view from south east



Shading from powerlines affect the sensors and modules at different times of morning (5 distinct dips)

Efficiency Isc / Gi

Approx. shade times

modules (07:35-08:05) sensors (07:10-07:40)

Sensors higher than modules so are shaded earlier in morning

(Late afternoons are affected by 2D tracker)

Low light performance measurements vs. irradiance must be properly corrected for shading



SRCL/Gantner “Loss Factors Model” vs. Irradiance detailed information at www.steveransome.com, GI data

•A drop in any LFMparameter limitsoverall PRDC

•Any LFM parameterchanging over timeaffects PRDC

PRDC = nISC*nRSC*nIMP * nVMP*nROC*nVOC

Low light limiting

High light limiting




Analysis method for frequent IV curves outdoorsGI Data

Sudden change –damaged or failed module

Steady declinemodule

Stable performance module

PRDC from 6 years of hourly measurements 2010-2016

modules chosen to analyse differing behaviour

PRDC at Low light may be seasonally dependent (longer day length, sun behind module)

PRDC at High Irradiance tends not to be seasonally dependent



LFM vs. irradiance

GI data

It’s hard to see any changes in nISC

unless corrected for shading, soiling, aoi, sr and direct:diffuse



LFM vs. irradiance

GI data

Irradiance dependentdegradation

dnRSC

Irradiance independentdegradation

dnROC



nRsc vs. DateTime and Log(Irradiance)Low light levels performance degrades much faster than high light levels

High light levels(0.5–1.0kW/m²) dnRSC -0.5%/y

Low light levels(0.1–0.2kW/m²) dnRSC -2.0%/y

Very Low light levels(0.001–0.02kW/m²) dnRSC -5%/y



Measurement Conclusions

NOTES:

• Atypical devices analysed vs. a stable module

• Smooth IV curves needed for degradation analysis (check if “Rn = –V/I” is good on your measurement system)

GANTNER INSTRUMENTS dataset in AZ (6 years) - SRCL/GI Loss Factors Model

• LFM separates degradation components from nISC

• Good Gantner Instruments IV trace quality allows study of RSC and ROC

• Modules may degrade differently at high or low light levels

• LFM allows a fast independent check of degradation rates



Predictions : Site Dependent Energy Yield DegradationEnergy Yield Gi,Tmod [Insolation(Gi,Tmod) * Efficiency(Gi,Tmod)]

Irradiance distribution is

site dependent (cumulative Hi kWh/m² % > Gi kW/m²)

nRSC (related to RSHUNT) degradation/y vs. Irradiance

-2.0%/yearlow light

-0.5%/yearhigh light

*



Predictions : Energy yield degradation rate at sites (from measured dnRSC )

High Insolation site = Lower Energy Yield degradation-0.7%/y

Lower Insolation site =Higher Energy Yield degradation-1.3%/y



Predictions : Conclusions

LFM gives

• Degradation rates for various parameters vs. irradiance etc.

• Predicted Energy Yield (kWh/y) degradation vs. site

• Low light drops in nRsc (~ RSHUNT) cause worse falls at low than high insolation sites

• Analysis methodology is being integrated into

• www.gantner-webportal.com(see separate poster)

Thank you for your attention!


http://www.gantner-webportal.com/

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