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www.steveransome.com25-Oct-16 1© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Determining the causes and rates of PV degra...
www.steveransome.com25-Oct-16 2© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Introduction to degradation analysis
• Most ...
www.steveransome.com25-Oct-16 3© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Smooth IV curves are needed for good RSC and...
www.steveransome.com25-Oct-16 4© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Checking IV data quality with Log Resistance...
www.steveransome.com25-Oct-16 5© SRCL / Gantner Instruments
PVPMC #6 Freiburg
SRCL/Gantner “Loss Factors Model” [LFM]
GI d...
www.steveransome.com25-Oct-16 6© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Comparing “Loss Factors Model” with standard...
www.steveransome.com25-Oct-16 7© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Yearly
IV traces
by
irradiance
Sept.
2010-16...
www.steveransome.com25-Oct-16 8© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Yearly
IV traces
by
irradiance
Sept.
2010-16...
www.steveransome.com25-Oct-16 9© SRCL / Gantner Instruments
PVPMC #6 Freiburg
GI Tempe OTF from North to South East
Low ho...
www.steveransome.com25-Oct-16 10© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Shading from powerlines affect the sensors ...
www.steveransome.com25-Oct-16 11© SRCL / Gantner Instruments
PVPMC #6 Freiburg
SRCL/Gantner “Loss Factors Model” vs. Irrad...
www.steveransome.com25-Oct-16 12© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Analysis method for frequent IV curves outd...
www.steveransome.com25-Oct-16 13© SRCL / Gantner Instruments
PVPMC #6 Freiburg
LFM 
vs.
irradiance
GI data
It’s hard to ...
www.steveransome.com25-Oct-16 14© SRCL / Gantner Instruments
PVPMC #6 Freiburg
LFM 
vs.
irradiance
GI data
Irradiance
de...
www.steveransome.com25-Oct-16 15© SRCL / Gantner Instruments
PVPMC #6 Freiburg
nRsc vs. DateTime and Log(Irradiance)
Low l...
www.steveransome.com25-Oct-16 16© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Measurement Conclusions
NOTES:
• Atypical d...
www.steveransome.com25-Oct-16 17© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Predictions : Site Dependent Energy Yield D...
www.steveransome.com25-Oct-16 18© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Predictions : Energy yield degradation rate...
www.steveransome.com25-Oct-16 19© SRCL / Gantner Instruments
PVPMC #6 Freiburg
Predictions : Conclusions
LFM gives
• Degra...
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Determining the causes and rates of PV degradation using the Loss Factors Model (LFM) with high quality IV measurements

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

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

  1. 1. www.steveransome.com25-Oct-16 1© SRCL / Gantner Instruments PVPMC #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 UK 2Gantner Instruments, Germany PVPMC #6 – Freiburg Germany 25th Oct 2016
  2. 2. www.steveransome.com25-Oct-16 2© SRCL / Gantner Instruments PVPMC #6 Freiburg 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.)
  3. 3. www.steveransome.com25-Oct-16 3© SRCL / Gantner Instruments PVPMC #6 Freiburg 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 𝑹 𝒏 = − ∆𝑽 ∆𝑰 = − 𝑽 𝒏 − 𝑽 𝒏−𝟏 𝑰 𝒏 − 𝑰 𝒏−𝟏 Worse RSC accuracy from synthesised data (e.g. truncated or noisy) ISC, RSC VOC, ROC
  4. 4. www.steveransome.com25-Oct-16 4© SRCL / Gantner Instruments PVPMC #6 Freiburg 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 NREL CdTe
  5. 5. www.steveransome.com25-Oct-16 5© SRCL / Gantner Instruments PVPMC #6 Freiburg 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
  6. 6. www.steveransome.com25-Oct-16 6© SRCL / Gantner Instruments PVPMC #6 Freiburg Comparing “Loss Factors Model” with standard models L F M 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”. Independent Parameters ? Almost independent (nVOC depends a little on nRSC and nISC) No. Parameters are often interdependent e.g. nF and Io Dependence on Irradiance and temperature Simple optimum fits give exact 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.
  7. 7. www.steveransome.com25-Oct-16 7© SRCL / Gantner Instruments PVPMC #6 Freiburg Yearly IV traces by irradiance Sept. 2010-16 GI data Discrepancies seen at very low light levels ? Changes in LFM parameters ΔnISC ΔnRSC ΔPRDC ΔnROC ΔnVOC For each module and Irradiance (e.g. ~0.8kW/m²) ISC variability e.g. soiling, sensor calibration etc.
  8. 8. www.steveransome.com25-Oct-16 8© SRCL / Gantner Instruments PVPMC #6 Freiburg Yearly IV traces by irradiance Sept. 2010-16 GI data Discrepancies in ISC seen at very low light levels 0.04kW/m² Why ? Year Deg %/y If module is degrading it’s worse at low light 1.0  0.3 kW/m2
  9. 9. www.steveransome.com25-Oct-16 9© SRCL / Gantner Instruments PVPMC #6 Freiburg GI Tempe OTF from North to South East Low horizon shading for morning sun GI hut position red Power lines green Sensors Cyan Modules Magenta Google Street view from south east
  10. 10. www.steveransome.com25-Oct-16 10© SRCL / Gantner Instruments PVPMC #6 Freiburg 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
  11. 11. www.steveransome.com25-Oct-16 11© SRCL / Gantner Instruments PVPMC #6 Freiburg SRCL/Gantner “Loss Factors Model” vs. Irradiance detailed information at www.steveransome.com, GI data •A drop in any LFM parameter limits overall PRDC •Any LFM parameter changing over time affects PRDC PRDC = nISC*nRSC*nIMP * nVMP*nROC*nVOC Low light limiting High light limiting
  12. 12. www.steveransome.com25-Oct-16 12© SRCL / Gantner Instruments PVPMC #6 Freiburg Analysis method for frequent IV curves outdoors GI Data Sudden change – damaged or failed module Steady decline module 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
  13. 13. www.steveransome.com25-Oct-16 13© SRCL / Gantner Instruments PVPMC #6 Freiburg LFM  vs. irradiance GI data It’s hard to see any changes in nISC unless corrected for shading, soiling, aoi, sr and direct:diffuse
  14. 14. www.steveransome.com25-Oct-16 14© SRCL / Gantner Instruments PVPMC #6 Freiburg LFM  vs. irradiance GI data Irradiance dependent degradation dnRSC Irradiance independent degradation dnROC
  15. 15. www.steveransome.com25-Oct-16 15© SRCL / Gantner Instruments PVPMC #6 Freiburg 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
  16. 16. www.steveransome.com25-Oct-16 16© SRCL / Gantner Instruments PVPMC #6 Freiburg 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
  17. 17. www.steveransome.com25-Oct-16 17© SRCL / Gantner Instruments PVPMC #6 Freiburg Predictions : Site Dependent Energy Yield Degradation Energy 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%/year low light -0.5%/year high light *
  18. 18. www.steveransome.com25-Oct-16 18© SRCL / Gantner Instruments PVPMC #6 Freiburg 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
  19. 19. www.steveransome.com25-Oct-16 19© SRCL / Gantner Instruments PVPMC #6 Freiburg 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!

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