Dr. Hafiz Imtiaz

Photovoltaic (PV) panels can develop many physical faults—like cracks, dirt, shading, or temperature damage—that reduce their power output. Detecting these faults early is important for keeping solar systems efficient. In this work, we present a machine learning method that detects PV faults using unlabeled electroluminescence (EL) images. We first label the images automatically using k‑means clustering, with features extracted from a pre‑trained VGG‑16 model. Principal component analysis (PCA) shows that 64 components capture most of the important information. This led to this Energy Advances publication.