Automatic Processing and Solar Cell Detection in Photovoltaic Electroluminescence Images

TL;DR

This paper introduces automated methods for processing and detecting solar cells in photovoltaic electroluminescence images, addressing challenges like distortions and background noise to enable large-scale, reliable analysis of PV modules.

Contribution

The paper presents novel automated techniques for correcting distortions and extracting solar cells from EL images, suitable for large-scale real-world PV module analysis.

Findings

Methods achieve high accuracy and robustness in cell detection.

Techniques work reliably on large, real-world datasets.

Effective even on low-contrast images.

Abstract

Electroluminescence (EL) imaging is a powerful and established technique for assessing the quality of photovoltaic (PV) modules, which consist of many electrically connected solar cells arranged in a grid. The analysis of imperfect real-world images requires reliable methods for preprocessing, detection and extraction of the cells. We propose several methods for those tasks, which, however, can be modified to related imaging problems where similar geometric objects need to be detected accurately. Allowing for images taken under difficult outdoor conditions, we present methods to correct for rotation and perspective distortions. The next important step is the extraction of the solar cells of a PV module, for instance to pass them to a procedure to detect and analyze defects on their surface. We propose a method based on specialized Hough transforms, which allows to extract the cells even when the module is surrounded by disturbing background and a fast method based on cumulated sums (CUSUM) change detection to extract the cell area of single-cell mini-module, where the correction of perspective distortion is implicitly done. The methods are highly automatized to allow for big data analyses. Their application to a large database of EL images substantiates that the methods work reliably on a large scale for real-world images. Simulations show that the approach achieves high accuracy, reliability and robustness. This even holds for low contrast images as evaluated by comparing the simulated accuracy for a low and a high contrast image.