Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/38975

Title: Artificial Intelligence for Fault Detection in Photovoltaic Panels
Authors: José, D.F.
Janeiro, Fernando M.
Pires, V.F.
Pires, A.J.
Martins, J.F.
Keywords: Fault detection
Photovoltaic systems
Artificial intelligence
Deep learning
Renewable energy
Issue Date: May-2025
Publisher: IEEE
Abstract: This paper presents an Artificial Intelligence solution for fault detection and classification in photovoltaic systems. The proposed tool integrates electrical and visual analysis methods, including I-V curve analysis, direct difference measurement, infrared thermography, electroluminescence imaging, and visual inspection. These methods are enhanced by deep learning models, which achieve high accuracy in identifying and diagnosing faults. A Python-based web application provides users with an intuitive interface for real-time data processing and fault classification. Experimental results demonstrate the tool’s effectiveness, with neural network models achieving accuracy levels exceeding 98% in electrical methods and over 90% in visual methods. By optimizing fault detection processes, the tool reduces maintenance costs, minimizes downtime, and enhances the operational reliability of photovoltaic systems. This research represents a significant step toward scalable, automated maintenance solutions, ensuring photovoltaic systems’ sustainability and efficiency in the transition to renewable energy.
URI: http://hdl.handle.net/10174/38975
Type: lecture
Appears in Collections:DEM - Comunicações - Em Congressos Científicos Internacionais

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