Identifying Degradation Modes of Photovoltaic Modules Using Unsupervised Machine Learning on Electroluminescense Images

In order to characterize the degradation of solar modules, it is necessary to systematically recognize common signatures, such as cracks and corrosion. This information can be useful in a multitude of ways, such as comparing failure models of different brands or types of cells. The process of electroluminescence imaging can illuminate these signatures such that automated image processing techniques can identify them. Both supervised (via C.N.N.) and unsupervised models can be applied; the focus of this work is on improvement of the unsupervised approach. Feature extraction, a pivotal step to computer vision, is used to produce localized descriptions of relevant regions in an image. In order to identify features, numerous feature extraction algorithms, such as ORB [12] and FAST [11] have been applied, which yield feature vectors that are invariant to location, scale, and orientation, as opposed to Haralick features [4]. The resulting feature vectors can be clustered via hierarchical clustering and a bag of visual words can be constructed that can identify similar features across a sample set. This method allows clustering around a large number of unidentified features both apparent and non-obvious to human inspection, including cracking, busbar corrosion, and other common forms of degradation. The resultant model and code implementation can be used as a form of exploratory data analysis before labeling in preparation for supervised machine learning, or as a classifier on its own.