Paper Contents
Abstract
Solar panels offer a realistic substitute for the traditional energy source due to the increasing global necessity for sustainable energy solutions. Solar energy produces lesser contaminants, lesser operational cost, and a total independence from energy grid systems. Ordinarily, both transient and permanent failures act as inhibitors to performance and longevity of photovoltaic (PV) systems. Such failures include those of shading, dust deposits, bird droppings, and physical damage, having a huge potential for reducing power generation while increasing maintenance costs. Early fault identification and repairs are very important in optimal energy efficiency and reliability of solar power systems. This study deals with the automatic detection and identification of common faults in a solar panel so as to enhance maintenance planning. A dataset of images of affected panels has been compiled, focusing particularly on two types of faults: shadows and bird droppings. Image augmentations like image rotation, scaling, and shearing have enhanced the diversity and robustness of the dataset. The YOLO object detection model was then applied to the analyzed images: it will detect faults and impact areas with high precision. Preprocessing operations included image normalization and data balancing which improved model performance by inducing fewer classification errors. After the model identifies a fault, the system indicates the areas affected and automatically sends an email notifying users of the detected fault. The email notification carries fault details along with an image attachment, thus providing users with visual evidence for prompt action. The applicability of this alert system thus provides timely maintenance and reduces the risk of extended performance reduction. The automated detection of faults would, therefore, reduce the amount of required manual inspection, but at the same time improve fault diagnosis probability and enable predictive maintenance techniques. Real-time monitoring of solar PV will be improved using deep learning in the analysis of PV faults, thus increasing the output energy and reducing the time of operation. Future studies will be focused on the widening of detectable fault types, improved model accuracy, and developing real-time monitoring systems to configure the solar energy system for an even increased efficiency and sustainability level. The results are part of the continuing research geared towards solar panel performance improvement to make renewable energy systems efficient and cheap for mass applications.
Copyright
Copyright © 2025 Mahalakshmi. R. This is an open access article distributed under the Creative Commons Attribution License.
