Enhancing Solar Panel Maintenance Through Deep Learning for Damage Detection

Due to increasing emphasis on a cleaner society, there is a rapid growth in the usage of solar energy along with other renewable resources. With this great increase in the deployment of solar panels across the globe, the need for maintenance of the panels also increases. However, traditional inspection and maintenance processes are often labor intensive, costly, and time-consuming especially in solar farms. This work introduces an automated solution that uses the VGG19 model, a convolutional neural network architecture known for its effectiveness in large-scale image recognition and feature extraction. The model was trained and fine-tuned on a dataset consisting of images of solar panels, including those with visible damage. During training many optimizations were implemented, including early stopping mechanisms, to prevent overfitting and ensure the robustness of the model. The hardware implementation consists of a webcam to capture real-time images of solar panel surfaces. These images are then processed directly on a Raspberry Pi, which runs the trained VGG19 deep learning model. This setup allows for real-time inspections without requiring manual checks. The objective of this work is to enhance the efficiency and accuracy of solar panel damage detection using deep learning, providing an automated solution for the inspection process. Experimental results and analysis revealed a high accuracy in distinguishing damaged solar panels from those in optimal condition. The model achieved a validation score of about 94% , demonstrating its effectiveness in detecting various forms of damage across different settings.