Extended Solar Cell Parameters - General Purpose Descriptive I/V
Parameters for Solar Cells
Abstract
For the automated analysis of I/V-characteristics of solar cells
and modules, descriptive parameters are essential. In particular with
the rise in machine-learning techniques and the related increase data
volumes, there is a need for good, general purpose, descriptive
parameters. The most commonly used descriptive parameters for I/V
are the standard solar cells parameters, consisting of V oc , I sc , V
mpp , and I mpp . Also other representations may be considered, such as
one diode model parameters corresponding to a particular I/V.
However, these representations are very coarse and cannot distinguish or
represent many common (non-ideal) features of an I/V (e.g. an
S-shape). In this work we propose an extended set of solar cell
parameters, which are well defined, and easy to determine. We evaluate
the effectiveness of the extended solar cell parameters by
reconstructing the I/V from the extracted parameters. This allows
one to “measure” information loss. We compare the accuracy of our
parameters with other commonly used curve models for I/V, namely
the one diode model, and the Karmalkar-Haneefa model. The models are
applied to a large set of I/V (about 2.2 million curves),
covering a wide range of technologies and conditions. We demonstrate our
extended solar cell parameters consistently provide an accurate
description of nearly all I/V in these datasets. Furthermore, we
present our I/V analysis tool which we use to process these
datasets. This tool is fast and capable of extracting the extended solar
cell parameters, as well as parameters for the one diode model and the
Karmalka-Haneefa model. Finally, we exemplary show how the extended
solar cell parameters may be used to detect partial shading in outdoor
data, by training a simple random-forest classifier based on extended
solar cell parameters.