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