Programm Photovoltaik Ausgabe 2008 ... - Bundesamt für Energie BFE

data sets. The indoor data sets consisted of IV-measurements of 8 different crystalline silicon modules,
run with a class A solar simulator at 5 irradiance levels (300,600,800,900 and 1000W/m²) and 8
temperatures, ranging from 25-60 °C. The extension of this study to a first set of outdoor data wanted
to show how close to real performance one can get with a short outdoor measurement campaign and
the application of the proposed I-V translation methods. In each set of data some base IV curves have
been defined, which are then extrapolated and/or interpolated to the remaining sets of operating conditions
and compared to the measured ones. The first two methods needs 7-8 I-V curves, whereas the
linear interpolation method only 3-4.
Table 4 summarises the results obtained with the indoor data. The average translation error of the
maximum power Pmax and its standard deviation is here shown for the different approaches. As the
original standard (ed. 1987) recommends that the target irradiance should be within ±30% of the base
irradiance, the table was split into two parts: 300W/m² (outside of 800W/m² ± 30% range) and the remaining
irradiances. At 300W/m² the error of the original IEC 60891 method reaches up to 4% with an
average over the whole temperature range of 2.8%. In the range of applicability the error remains instead
below 1%. The other two approaches, the modified Blaesser method and the linear interpolation
method, shows to have a higher accuracy over the whole range of temperature and
irradiance levels. The error in Pmax is generally below 0.5%.
G=300 W/m² G=600-1000 W/m²
T=25-65°C
T=25-65°C
avg. error St.Dev avg. error St.Dev
IEC60891 2.8% 1.5% 0.1% 0.5%
Modified Blaesser -0.4% 0.3% 0.0% 0.2%
Linear interpolation 0.4% 0.2% 0.0% 0.1%
Table 4: Average over a defined range of irradiances and temperatures of the avg. Pmax error of
8 c-Si modules obtained by 3 different IV-translation methods. Note: standard deviation in
italic.
The advantages and disadvantages of the single methods can be summarised as follow. The obvious
advantage for indoor applications of the new proposed methods are the high accuracy over the whole
range of interest and in the special case of the modified Blaesser method, the capability to translate
the whole I-V curve, especially the open-circuit voltage, not possible with the original IEC method
when translating to higher irradiances and more difficult with the linear interpolation method due to
some restrictions in the method. The linear interpolation method has the advantage to lead to the lowest
errors in the determination of the power with only 3 to 4 I-V measurements, but with the risk that if
one of these base curves is of bad quality the whole translation accuracy is consequently reduced.
The other known disadvantage of this method is the much higher uncertainty when extrapolating instead
of interpolating the data. It is so almost obligatory to measure the extreme values of the area of
interest, requirement which is difficult to fulfil with a short outdoor measurement campaign. The comparison
of translated values from outdoor data with the in average occurring maximum power during a
whole year of operation showed in fact a higher uncertainty with the linear interpolation method than
with the other methods, particularly pronounced at 300W/m 2 . The errors observed for the modified
Blaesser method and the original IEC method were all within ±2.5%. The generally higher uncertainty
in the translation of outdoor data compared to indoor data can be explained by the higher measurement
uncertainty and the lower repeatability of the measurements due to the many environmental
parameters influencing the final output.
3. Modelling and analysis (SP4)
3.2 ISAAC 2007 ACTIVITIES (SP4)
3.2.1 First Modelling Round Robin (WP4.4)
The ISAAC coordinated work-package 4 consists mainly of existing energy prediction methods in the
intercomparison and validation. Eight separate methods, developed independently across European
Universities and Research Centres, have been compared with respect to their estimated DC energy
generation for five different photovoltaic (PV) module technologies and 7 different sites distributed
over whole Europe. The analysis of this work is the basis for further improvements of each of the
modelling approaches and its main objective is to further reduce the prediction error in PV yield estimations.
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