Application of the Singular Spectrum Analysis on electroluminescence images of thin-film photovoltaic modules
This work addresses image processing challenges for photovoltaic module inspection, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the problem of analyzing electroluminescence images of thin-film photovoltaic modules by applying singular spectrum analysis to decompose images into components, enabling tasks like identifying interconnection lines at sub-pixel accuracy and estimating physical parameters such as the inverse characteristic length.
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.