Photovoltaic Cell Defect Detection by Lock-In Thermography Using 2-D Gaussian Profile

Abstract

The electrical energy produced by photovoltaic systems can be critically affected by a variety of factors. In order to detect defective photovoltaic cells, several monitoring techniques, such as lock-in thermography, have been widely used alongside some analytical methods that avoid subjectivity. This article proposes a method with low computational cost that provides a simple and easily implementable way to quantifiably discern if a photovoltaic cell is defective or not. A two-dimensional Gaussian fit is applied to images generated by fast Fourier transform and principal component analysis algorithms on thermographic data from lock-in thermography tests. The considered coefficient of determination ${R}^2$ was found to be a good measure of fitting quality. Additionally, the method highlighted the potential of its application on first principal component, with ${R}^2$ between 0.944 and 0.986, and magnitude images, with ${R}^2$ between 0.965 and 0.985, in order to identify and distinguish nondefective cells from defective ones.