Deep Learning-Based Fault Diagnosis in Photovoltaic System

This study presents a methodology for diagnosing faults in photovoltaic (PV) systems using Convolutional Neural Networks (CNNs). By leveraging CNNs' ability to extract intricate features from PV data, including voltage, current, irradiation, and temperature, the proposed approach enables the accurate identification of various fault types such as short circuits, open circuits, wear, and shading. The contribution of this paper enhances PV system performance and reliability through early fault identification and a comprehensive framework for fault analysis. By effectively distinguishing between normal operating conditions and different fault occurrences, this methodology supports a more sustainable and efficient solar energy future. Through data preprocessing, optimization of the CNN architecture, and incorporation of a multi-class classification layer, the model achieved a high accuracy in fault detection, with an average detection and classification accuracy of 99.7%. The rigorous validation and testing employed in this study affirm the effectiveness and reliability of the CNN-based approach. Overall, this methodology offers a promising strategy for improving fault diagnosis in PV systems, advancing the reliability and efficiency of solar energy applications.