A comprehensive study for solar panel fault detection using VGG16 and VGG19 convolutional neural networks

Abstract

The utilization of solar energy has experienced remarkable growth as a sustainable and clean alternative to conventional power sources. Solar panels, as the fundamental components of photovoltaic systems, play a pivotal role in harnessing solar energy efficiently. However, solar panel faults can significantly degrade system performance, necessitating timely detection and maintenance. In this research paper, we present a comprehensive study on solar panel fault detection employing Convolutional Neural Networks (CNNs), specifically the VGG16 and VGG19 architectures. The proposed methodology integrates CNN models to automate the process of solar panel fault detection. A diverse dataset encompassing various common solar panel defects, such as cracks, dust, and bird spots, is collected and preprocessed to facilitate model training. The comparative analysis of the VGG16 and VGG19 models is conducted to assess their respective capabilities in identifying and classifying these faults. Our experiments demonstrate the effectiveness of deep learning-based fault detection, achieving high accuracy rates of unseen data. Moreover, achieving greater accuracy not only boosts the efficiency of spotting faulty solar panels but also makes it easier to switch out the affected ones. This turn simplifies the upkeep process, ensuring the solar system maintains top-notch performance without a hitch.