Fault diagnosis in photovoltaic systems using InceptionV3 convolutional neural network for enhanced image-based classification

Photovoltaic (PV) systems are an invaluable green power solution to the energy requirements in the world yet as they are placed outside, they are subject to numerous issues and environmental challenges that may lead to loss of efficiency in the systems and even system failures. Thus, early and accurate fault diagnosis is necessary to minimize downtime and costs of maintenance. This paper proposes an automated PV-based fault diagnosis system with the aid of a pre-trained image-based InceptionV3 convolutional neural network, which can find faults in panels. The model was trained and tested on a diverse dataset of 885 images to classify six categories of conditions, including physical degradation, electrical degradation, bird droppings, dust, snow coverage, and fault-free panels. Using transfer learning and extensive data augmentation, the model achieved a good overall test-based accuracy of 85.71%. The comprehensive analysis of the performance showed that it was highly suitable for detecting Dusty, Clean, and Bird-drop classes with high F1-scores. However, the model struggled with the lower recall Physical-Damage class, indicating that it was not fully reliable in detecting subtle physical defects such as cracks. The results, complemented by ROC curves and confusion matrices, determine the extent to which the InceptionV3 architecture is appropriate for this task and highlight some diagnostic limitations. This article is important in providing more reliable and cost-effective solar energy production by creating a clear and methodologically rigorous standard for automated PV fault detection.