Classification and Early Detection of Solar Panel Faults with Deep Neural Network Using Aerial and Electroluminescence Images

This paper presents an innovative approach to detect solar panel defects early, leveraging distinct datasets comprising aerial and electroluminescence (EL) images. The decision to employ separate datasets with different models signifies a strategic choice to harness the unique strengths of each imaging modality. Aerial images provide comprehensive surface-level insights, while electroluminescence images offer valuable information on internal defects. By using these datasets with specialized models, the study aims to improve defect detection accuracy and reliability. The research explores the effectiveness of modified deep learning models, including DenseNet121 and MobileNetV3, for analyzing aerial images, and introduces a customized architecture and EfficientNetV2B2 models for electroluminescence image analysis. Results indicate promising accuracies for DenseNet121 (93.75%), MobileNetV3 (93.26%), ELFaultNet (customized architecture) (91.62%), and EfficientNetV2B2 (81.36%). This study's significance lies in its potential to transform solar panel maintenance practices, enabling early defect identification and subsequent optimization of energy production.