Abstract
The rapid growth of the solar photovoltaic industry underlines the importance of effective operation and maintenance strategies, particularly for large-scale systems. Aerial infrared thermography has become an essential tool for detecting anomalies in photovoltaic modules due to its cost-effectiveness and scalability. Continuous monitoring through advanced fault detection and classification methods can maintain optimal system performance and extend the life of PV modules. This study investigates the application of advanced artificial intelligence methods for fault detection and classification comparing the performance of GPT-4o, a multimodal large language model, and ResNet, a convolutional neural network renowned for image classification tasks. Our research evaluates the effectiveness of both models using infrared images, focusing on binary defect detection and multiclass classification. ResNet demonstrated advantages in terms of computational efficiency and ease of implementation. Conversely, GPT-4o offered superior adaptability and interpretability, effectively analysing multimodal data to identify and explain subtle anomalies in thermal imagery. However, its higher computational requirements limit its feasibility in resource-limited settings. The results highlight the complementary strengths of these models and provide valuable insights into their role in advancing automated fault diagnosis in photovoltaic systems.