# Application of the Singular Spectrum Analysis on electroluminescence images of thin-film photovoltaic modules

arXiv: https://arxiv.org/abs/2109.04048 · HTML: https://mlsanity.com/paper/2109.04048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules](https://mlsanity.com/paper/2010.07556/) (2020)
- [Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package](https://mlsanity.com/paper/1309.5050/) (2013)
- [Automatic Processing and Solar Cell Detection in Photovoltaic Electroluminescence Images](https://mlsanity.com/paper/1807.10820/) (2018)

## Abstract (arXiv)

This paper discusses an application of the singular spectrum analysis method (SSA) in the context of electroluminescence (EL) images of thin-film photovoltaic (PV) modules. We propose an EL image decomposition as a sum of three components: global intensity, cell, and aperiodic components. A parametric model of the extracted signal is used to perform several image processing tasks. The cell component is used to identify interconnection lines between PV cells at sub-pixel accuracy, as well as to correct incorrect stitching of EL images. Furthermore, an explicit expression of the cell component signal is used to estimate the inverse characteristic length, a physical parameter related to the resistances in a PV module.