Your SlideShare is downloading. ×
  • Like
Outdoor testing, analysis and performance predictions of PV technologies [PV 2009]
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Outdoor testing, analysis and performance predictions of PV technologies [PV 2009]


- Outdoor vs indoor measurements …

- Outdoor vs indoor measurements
- Analyzing and modeling outdoor data
- Validating or fault finding device performance
- Extracting coefficients
- Understanding the differences between modules
- Checking performance limitatons
Steve Ransome, Associate Consultant, IntertechPira

Published in Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Outdoor testing, analysis and performance predictions of PV technologies Steve Ransome (Owner SRCL)and associate consultant (Intertechpira UK)
  • 2. How kWh/kWp values are usedby industry sectorSection of PV industry kWh/kWp relevanceManufacturers Claim high performanceIndoor testers Measure relevant parametersSizing programs Claim accurate predictions from(simulation models) complex modelsCustomers Expect high valuesFinancial backers Demand guaranteed values over lifetimeIndependent outdoor Different rankings for eachcomparisons technology (often within experimental error for correctly rated and measured modules)14-May-09 Page 2
  • 3. Typical daily AC performance of alarge array, USA 1. Energy Yield YF (kWh/kWp/d) should be approximately proportional to the daily insolation YR (kWh/m²/d) 2. Points below the line indicate underperforming periods 3. The total uncorrected energy yield will include these bad points and worsen apparent yield14-May-09 Page 3
  • 4. Energy yield losses from AC arraysHow much of these energy losses are due to– component number and choice– down time– inverter loss (efficiency or low light turn on)– inherent differences between module technologies ?– other reasons ?DC module performance must be studied to quantifythe losses14-May-09 Page 4
  • 5. View of typical DC setup ISET, Kassel Germany “Spectrally30° sensitive”Tilt Irradiance sensorsSouth PV modules Not shown : temperature sensors Direct:Diffuse, precipitation , wind speed etc. Pyranometer
  • 6. Typical DC outdoor measuring setup(single devices are better for characterisation) Windspeed (ms-1) Plane of array Irradiance (kW/m²) Impp, Vmpp Device or IV scan under test Data Logger Device Temperature (C) Measure every 1-10 Ambient Temperature (C) minutes Other sensors ? e.g. horizontal irradiance, precipitation, air pressure, Data spectrum … Analysis
  • 7. Independent energy yield test :7 technologies Kassel, Germany 1. Most technologies give similar energy yields (<±4% kWh/kWp) 2. Two are much lower (are they faulty or have they degraded?) 3. Cannot identify reasons from kWh/kWp sums alone (see my paper PVSEC Valencia 2008 for details)14-May-09 Page 7
  • 8. How do we find the reasonfor differences in kWh/kWp ?Possible reasons• Monitoring errors e.g. Vmax mistracking• Pmax declaration (measured/nameplate)• Shading on some panels only• Degradation/annealing• Different technology performances at – low light – high temperature – diffuse light – different spectra …Detailed studies should reveal reasons for differences14-May-09 Page 8
  • 9. Data validation foroutdoor measurementsNormalise measurements to “measured/expected values”• Vdm = Vdc /• Idn = Idc / / IrradianceDefine simple limits to remove “bad” data points(e.g. 80-110% of expected value )Perform a sanity check on meteorological dataIrradiance (e.g. 0 to 1.4kW/m²),Clearness Index (e.g. 0.2 to 0.8)Diffuse Fraction (e.g. 0.1 to 0.9)Temperatures (e.g. -20 < Ambient < 40)etc.14-May-09 Page 9
  • 10. Normalised electrical parametersshowing limits used for Imax and Vmax Weather data (top) Electrical data (bottom) • Correct, interpolate or delete data outside sensible limits (shown in coloured bands) • “Redundant data” : calculate “NOCT” (Tmodule @800W/m², Tambient=20C, 1ms-1 wind) – should be ~47C14-May-09 Page 10
  • 11. Diffuse sky (left) vs Clear sky (right)affects PV performanceLarge attenuation of Beam Little attenuation of BeamHigh reflection off clouds Little reflection off cloudsVariable spectrum Spectrum ~ Air Mass14-May-09 Page 11
  • 12. Understanding Efficiency vs IrradianceImax and Vmax vs. Diffuse:Beam - cSi Diffuse ClearVmax vs Irradiance Imax vs Irradiance (2) Diffuse (1) Error (2) AOI Efficiency = Vmax * Imax1. Most points should be within narrow limits, outlier data due to poor tracking, shade or snow on module or sensor. Can temperature correct.2. Imax differs whether diffuse or clear sky, Vmax doesn’t14-May-09 Page 12
  • 13. Comparing different module technologies how important are any differences ? Diffuse Clear Crystalline Silicon #1 and #3 Thin Film #4 and #6Imax (1) (1) Imax quite similar, TF slightly worse current variability – spectral mismatch/annealingVmax (2) (2) Vmax very similar, TF slightly better voltage thermal coefficients 14-May-09 Page 13
  • 14. Comparing module technologies Efficiency vs Clearness (top) & Beam Fraction (bottom) Efficiency Crystalline Silicon #3 Thin Film #4 (1) (2)Efficiency =Vmax * Imax Clearness  Efficiency (3) (4) Beam Fraction  Diffuse Clear 14-May-09 Page 14
  • 15. Insolation (kWh/m²/y)vs Irradiance, Clearness index and Beam fraction More Insolation at : 1. Higher irradiance than lower – most sites 2. Higher clearness index (clear skies) 3. Higher beam fraction (low diffuse) Irradiance (kW/m²) than at lower values14-May-09 Page 15
  • 16. Insolation (kWh/m²/y)vs. Tmodule and Irradiance Irradiance (kW/m²)  More Irradiance at high light levels than low light even in Germany Tmodule (C)  More frequent measurements show even more high light level
  • 17. How all weather parameters are correlatedmaking understanding data more complicated Indoor (STC) Outdoor <Worse weather Better weather>Irradiance 1 kW/m² Lower HigherModule temperature 25 C Colder WarmerSpectrum AM 1.5 G Redder BluerAngle of incidence 0° normal Away from normal Nearer normalDirect : Diffuse All Direct Mostly diffuse Mostly direct14-May-09 Page 17
  • 18. Extracting temperature coefficientsfrom outdoor data Imax and Vmax Values may differ from internal measurements as weather parameters are correlated (e.g. spectrum and temperature) which will affect multi junction thin film more than c-Si. 1. Vmax more accurate than 2. Imax Need to filter out low irradiance/temperature data as too variable14-May-09 Page 18
  • 19. Empirical modellingpredicting Tmodule, Vmax and dc Power• Simple empirical models can predict Tmodule, Vmax and PmaxTmod = f(Irrad, Tamb, WS, …)Vmax = f(Irrad, Tamb, WS, …)Pmax = f(Irrad, Tamb, WS, …)• Can characterise measured and predict future PV performance fits (black dots) measured (coloured dots)
  • 20. Empirical modelling Flow chart learning mode to derive coefficients Empirical formulae andInputs coefficientsIrradiance (kW/m²)Ambient Temperature (C) Cell Temperature CWindspeed (ms-1) MPP Voltage V MPP Current A MPP Power WReport discrepancies Validate measurements Sum (Power) = Energy Yield
  • 21. Empirical modelling –validating Tmodule, Vmax and dc Power (1) Tmod (2) Vmax (3) Pmax
  • 22. Simulating outdoor performance, extracting indoor parameters indoors outdoorsMeas. DC module DC module String AC arrayStage (IV scan) (IV scan) (Vmp track) (Inverter) Efficiency Pactual/ Module Inverter vs nameplate, Mismatch efficiency, Irradiance Dirt, Partial shading, Temperature Thermal Wiring loss, Spectrum Annealing, String mismatch AOI etc. Degradation Weather Correlation  Parameter extraction Performance modelling  14-May-09 Page 22
  • 23. Finding shading - Max. irradianceper hour of the day and month of the year • Good unshaded sites will give smooth, symmetrical oval shapes as shown • Shading will show as lower maximum irradiance than expected for certain times and months (e.g. after 14:00 November to January for low horizon in the west)14-May-09 Page 23
  • 24. AC Performance : Maximum ac yieldper hour of the day and month of the year • Well performing arrays will give smooth, symmetrical oval shapes as shown • Thermal problems would be seen by summer afternoon dips (although this array seems good) • Turn on problems would be seen by low values in the morning14-May-09 Page 24
  • 25. Finding shading : Solar Irradiance andarray sum energy vs Solar position Total insolation vs Solar height and azimuth in 10° bins Good unshaded sites should have a symmetrical shape like this in Germany Horizon shading appears as wide low irradiance areas Tree or pole shading is seen in tall low irradiance areas14-May-09 Page 25
  • 26. Finding “Turn on” problems and ShadingPerformance/predicted Vs. Date and Time (1) 1. Shading would show as poor performance in horizontal shapes 2. “Turn on” problems appear as missing data at beginning of day (3) (2) 3. Missing data all day
  • 27. Conclusions• Sophisticated outdoor testing has been used to compare dc modules with ac arrays• Sum kWh/kWp figures alone are not enough to qualify measurements• A detailed knowledge of dc performance helps understand AC data• Normalise data for easier error checking V, I etc.• Max. Irradiance or Power vs. time of day and month can identify shading or thermal problems• Checking raw data enables faults, limits and weather effects to be analysed.14-May-09 Page 27
  • 28. Thank you for your attention Thanks to ISET for the DC data This paper and previous ones are available at