Deep Recurrent Convolutional Neural Network for Fault Identification in Photovoltaic Panels using Thermal Imaging
DOI:
https://doi.org/10.25729/esr.2025.03.0008Keywords:
Deep Recurrent Convolutional Neural Network, Fault Identification, Hot Spot Detection, End-of-Life Prediction, Proactive Maintenance, Predictive MaintenanceAbstract
Faults that are often invisible to existing inspection methods can significantly impact the reliability and efficiency of Photovoltaic (PV) systems. This study introduces an innovative framework for identifying PV panel faults using advanced machine learning algorithms and thermal images captured by unmanned aerial vehicles. The proposed Deep Recurrent Convolutional Neural Network (DRCNN) combines the strengths of Recurrent Neural Networks (RNNs) for learning temporal sequences and Convolutional Neural Networks (CNNs) for feature extraction. The DRCNN is designed to analyze thermal images of PV panels by capturing spatial and temporal patterns associated with fault-induced anomalies, such as hot spots, cracked cells or broken electrical connections. By leveraging CNN layers to extract critical features from thermal images and RNN layers to model the temporal dependencies and variations in thermal processes over time, the proposed approach integrates thermal image data collected under diverse environmental conditions.This dual strategy not only identifies faults based on spatial features but forecasts potential fault progression or the End-of-Life (EoL) for the panels by analyzing the evolution of thermal anomalies. The research aims to develop a highly accurate and efficient system for preventive maintenance and early problem detection, thereby improving the efficiency and durability of PV systems.The results demonstrate the DRCNN model's effectiveness in detecting various types of defects and delivering highly accurate real-time fault detection. By incorporating both spatial and temporal information, the proposed model significantly outperforms existing methods providing more robust and reliable defect identification.
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