Latest Research on AI in Fault Detection for Photovoltaic Systems Published

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 PV 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.

A latest research from the Institute for Renewable Energy, in collaboration with Università degli Studi di Modena, has just been published in EPJ Photovoltaics, examining the evolving role of AI in fault detection for PV systems.

Detecting failures in PV installations is essential for ensuring long-term performance and reliability. In this study, researchers compared two powerful AI models for analyzing aerial infrared (IR) imagery:

• GPT-4o – a multimodal large language model renowned for its adaptability and interpretability in analyzing multimodal data to identify and explain subtle anomalies in thermal imagery. However, its higher computational requirements limit its feasibility in resource-limited settings. • ResNet – a widely used convolutional neural network for image classification that demonstrates advantages in terms of computational efficiency and ease of implementation.

"This study investigates the application of advanced artificial intelligence methods for fault detection and classification, comparing the performance of GPT-4o and ResNet. "Our research evaluates the effectiveness of both models using infrared images, focusing on binary defect detection and multiclass classification. The results highlight the complementary strengths of these models and provide valuable insights into their role in advancing automated fault diagnosis in PV systems". explains Sandra Gallmetzer, first author of the study.

The work, carried out within the SUPERNOVA project, sheds light on the strengths of traditional and cutting-edge AI models and offers new perspectives on automated operation and maintenance strategies in PV systems.

For more details, read the full publication in EPJ Photovoltaics.

This work has received the financial support from the project PE00000021 “Network 4 Energy Sustainable Transition NEST PNRR MUR".