Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module

Background/Objective: The primary objective of the present study is to distinguish several visual faults which hinder the performance, reliability and lifetime of photovoltaic (PV) modules. Research question: Conventional fault detection techniques require specific operating conditions which also consumed a lot of time, manpower and expenditure. Innovative techniques and technological advancements in the highly paced world expect instant results. Advanced and automatic fault diagnosis is such a process that delivers instant results and guarantees an extended lifetime for numerous critical photovoltaic module (PVM) components. Hypothesis: This study performs an automatic detection of faults in PVM with convolutional neural networks (CNN) that accurately classifies various faults based on the images captured from unmanned aerial vehicles (UAVs). Methodology: Dataset creation is one of the primary constraints when it comes to working with CNN. To overcome this drawback, a data augmentation method is adopted to enlarge the dataset from the limited number of available aerial images of PVM. These augmented images are fed into an automatic fault detection CNN model for deep feature extraction and classification. Results and Conclusion: The presented method exhibits an increase in the accuracy and performance of PVM health monitoring when compared with other conventional solutions. The performances of uniform and non-uniform datasets are also presented. Various pre-trained models like VGG16 and ResNet50 are compared with the proposed solution for performance evaluation. The results demonstrate that the overall classification accuracy of the proposed model for uniform and non-uniform datasets was found to be 95.07% and 94.14% respectively with lesser training time and number of epochs.