Photovoltaic cell sorting workshop

Is cell sorting a reliable method for photovoltaic module manufacturing?

In photovoltaic module manufacturing processes, it is essential to achieve high production reliability of modules based on the given cells with scattered characteristics. This study aims to investigate the optimal cell sorting method to minimize the deviation of module power via simulation analysis.

How do we sort solar cells?

Sorting of solar cells is a vital step to achieve the predetermined power out of the photovoltaic module, nevertheless there is a lack of detailed investigations of all relevant parameters defining the global module efficiency. Sorting methods tend to rely on simple electrical parameters such as P-MAX, I-MPP, and I-SC.

Does a particular sorting method affect the performance variance of PV cells?

Meanwhile, a particular sorting method of PV cells will impact the performance variance of the modules considering limitedly produced cells with different characteristics in production lines.

Does optimal cell sorting minimize the deviation of module power?

This study aims to investigate the optimal cell sorting method to minimize the deviation of module power via simulation analysis. We consider the given solar cells to have different electrical characteristics with Gaussian distributions and ideal interconnections.

What are the criteria for cell sorting in module formation?

Representative criteria for cell sorting in module formation. Since we consider a module made of 72 cells with a series connection, the applied voltages on individual cells are superposed at a common current level to construct I – V curves.

How does the number of a sorting method affect module power?

The number of the sorting method corresponds to the number described in Table I. The sorting methods we employed affect only a marginal difference of ∼10 −4 % in the average values of the module power [Fig. 5 (a)]. The slight change is also reflected in the total mismatch loss for 100 modules with a difference of ∼1%.