Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements

Automated analysis of internal quantum efficiency measurements of GaAs solar cells using machine learning

Investigating the internal quantum efficiency (IQE) of solar cells is essential for identifying performance limitations and improving their efficiency. However, fitting IQE measurements of gallium arsenide solar cells using numerical simulation programs can be a laborious and tedious process, often limiting the depth of the analysis to only qualitative levels. In this study, we propose the use of machine learning to automate the fitting process and enable the extraction of key electrical quantities that represent the performance-limiting mechanisms of the cells. This novel method can help unlock the full potential of IQE measurements as a powerful characterization tool for further research and development of gallium arsenide solar cells

On the Effect of Misalignment Distributions on the I-V Curve of Micro-CPV Modules

Hybrid micro concentrator photovoltaic (CPV) modules that combine conventional and concentrator PV technologies have demonstrated higher efficiency and less installation complexity compared to PV and CPV modules, respectively [2]. Higher efficiency is achieved as the diffuse light is harvested by the conventional PV cells while the direct light is concentrated into highly efficient multijunction solar cells. Higher installation simplicity is reached through module elements downsizing and integrated tracking [4]. The integrated tracking is an embedded system that translates the receivers backplane a few millimeters with respect to the lenses (thanks to the elements’ reduced sizes) for obtaining an on-axis condition as the light angle of incidence varies, allowing installing these fixed modules on rooftops instead of mounting them onto a tracker. However, the high efficiency and excellent performance of CPV modules depend on precise alignment between the lenses and receivers. Misalignments between these elements can significantly reduce the cells current generation [5]. The mounting process has a strong impact on the alignment: the high number of involved lensreceiver units, small mounting tolerances, and difficulties in the integrated tracking positioning can lead to misplacements between lenses and receivers. The distribution of misaligned lens-receiver units impacts the current-voltage (IV) module performance. The severeness of this effect depends on the cells’ interconnection (series/parallel) and bypass diodes location; although in conventional modules each cell has a bypass diode, micro-CPV modules use a single bypass diode for a cells’ string. There are methods to characterize misalignments between the lens-receiver units in a micro-CPV module that can be implementable into production lines, but they are time and resource consuming. Since the misalignments affect the IV curves, a characterization method that is based on IV data and that does not require additional measurements can be highly valued if information about the misalignments can be extracted from its evaluation. In this study, we investigate the relationship between misalignments and the module electrical performance using simulations that reproduce IV curves resulting from given misalignment distributions. We expect that in the future this method, together with machine learning, will serve as a powerful quality control too