Practical implementation based on histogram of oriented gradient descriptor combined with deep learning: Towards intelligent monitoring of a photovoltaic power plant with robust faults predictions

Abstract

The increasing complexity of photovoltaic (PV) system monitoring underscores the importance of precise fault detection and energy loss prediction. This paper proposes a deep learning-based framework that integrates multiple advanced techniques to accurately detect, localize, and predict faults in PV panels. A pre-trained Convolutional Neural Network (CNN), based on the AlexNet architecture, processes thermal imaging data for precise fault extraction. This facilitates the classification of faults, contributing to improved decision-making in PV system management.