Outdoor testing, analysis and performance predictions of PV

technologies

Steve Ransome (Owner SRCL)

and associate consultant (Intertechpira UK)www.steveransome.com

How kWh/kWp values are usedby industry sector

Section of PV industry kWh/kWp relevance Manufacturers Claim high performanceIndoor testers Measure relevant parametersSizing programs (simulation models)

Claim accurate predictions from complex models

Customers Expect high valuesFinancial backers Demand guaranteed values

over lifetimeIndependent outdoor comparisons

Different rankings for each technology (often within experimental error for correctly rated and measured modules)

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Typical daily AC performance of a large array, USA

1. Energy Yield YF (kWh/kWp/d) should be approximately proportionalto the daily insolation YR (kWh/m²/d)

2. Points below the line indicate underperformingperiods

3. The total uncorrected energy yield will include these bad points and worsen apparent yield

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Energy yield losses from AC arrays

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How 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 quantify the losses

View of typical DC setupISET, Kassel Germany

PV modules

“Spectrally sensitive”Irradiance sensors

Pyranometer

South

30°Tilt

Not shown : temperature sensorsDirect:Diffuse, precipitation , wind speed etc.

Typical DC outdoor measuring setup(single devices are better for characterisation)

Ambient Temperature (C)

Windspeed (ms-1)Plane of array Irradiance (kW/m²)

Device Temperature (C)

Data LoggerMeasure

every 1-10 minutes

Device under test

Impp, Vmppor IV scan

Other sensors ? e.g. horizontal irradiance, precipitation, air pressure, spectrum …

Data Analysis

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)

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How do we find the reason for 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 www.steveransome.com Page 8

Data validation for outdoor measurements

Normalise measurements to “measured/expected values”• Vdm = Vdc / Vmax.stc• Idn = Idc / Imax.stc / Irradiance

Define 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.

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Normalised electrical parameters showing 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 ~47C

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Diffuse sky (left) vs Clear sky (right)affects PV performance

Large attenuation of Beam

High reflection off clouds

Variable spectrum

Little attenuation of Beam

Little reflection off clouds

Spectrum ~ Air Mass

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Understanding Efficiency vs IrradianceImax and Vmax vs. Diffuse:Beam - cSi

Vmax vs Irradiance Imax vs Irradiance

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1. 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’t

Diffuse Clear

(1) Error

(2) Diffuse

(2) AOIEfficiency = Vmax * Imax

Comparing different module technologieshow important are any differences ?

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Crystalline Silicon #1 and #3 Thin Film #4 and #6

(2) Vmax very similar, TF slightly better voltage thermal coefficients

(1) Imax quite similar, TF slightly worse current variability – spectral mismatch/annealing

Diffuse Clear

Vmax

Imax

(2)

(1)

Comparing module technologiesEfficiency vs Clearness (top) & Beam Fraction (bottom)

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Thin Film #4

Diffuse Clear

Effi

cie

ncy

Clearness

Beam Fraction

Effi

cie

ncyCrystalline Silicon #3

(1) (2)

(3) (4)

Efficiency = Vmax * Imax

Insolation (kWh/m²/y) vs Irradiance, Clearness index and Beam fraction

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More Insolation at :

1. Higher irradiance than lower –most sites

2. Higher clearness index (clear skies)

3. Higher beam fraction (low diffuse)

than at lower valuesIrradiance (kW/m²)

Insolation (kWh/m²/y) vs. Tmodule and Irradiance

Tmodule (C)

Irra

dia

nce

(kW

/m²)

More Irradiance at high light levels than low light even in Germany

More frequent measurements show even more high light level

How all weather parameters are correlatedmaking understanding data more complicated

Indoor (STC) Outdoor

<Worse weather Better weather>

Irradiance 1 kW/m² Lower Higher

Module temperature 25 C Colder Warmer

Spectrum AM 1.5 G Redder Bluer

Angle of incidence 0° normal Away from normal Nearer normal

Direct : Diffuse All Direct Mostly diffuse Mostly direct

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Extracting temperature coefficients from 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 variable

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Empirical modelling predicting Tmodule, Vmax and dc Power

• Simple empirical models can predict Tmodule, Vmax and Pmax

Tmod = 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)

Empirical modellingFlow chart

Empirical formulae and coefficientsInputs

Irradiance (kW/m²)Ambient Temperature (C)Windspeed (ms-1)

Cell Temperature C

MPP Voltage V

MPP Current A

MPP Power W

Validate measurements

learning mode to derive coefficients

Sum (Power) = Energy Yield

Report discrepancies

Empirical modelling –validating Tmodule, Vmax and dc Power

(1) Tmod

(2) Vmax

(3) Pmax

Simulating outdoor performance, extracting indoor parameters

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DC module(IV scan)

DC module(IV scan)

String(Vmp track)

AC array(Inverter)

Efficiency vs

IrradianceTemperature

SpectrumAOI etc.

Pactual/nameplate,

Dirt,Thermal

Annealing, Degradation

Weather Correlation

Module Mismatch

Inverter efficiency,

Partial shading,Wiring loss,

String mismatch

Meas. Stage

indoors outdoors

Parameter extraction Performance modelling

Finding shading - Max. irradiance per 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)

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• 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 morning

AC Performance : Maximum ac yield per hour of the day and month of the year

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Finding shading : Solar Irradiance and array 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 areas

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1. Shading would show as poor performance in horizontal shapes

2. “Turn on” problems appear as missing data at beginning of day

3. Missing data all day

Finding “Turn on” problems and Shading Performance/predicted Vs. Date and Time

(2)(3)

(1)

Conclusions

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• 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.

Thank you for your attention

Thanks to ISET for the DC data

This paper and previous ones are available at

steve@steveransome.com

www.steveransome.com