Low-cost AI-based solar panel detection drone design and implementation for solar power systems

• Published in: Assembly automation 2023-11, Vol.43 (6), p.605-624
• Main Authors: Özer, Tolga, Türkmen, Ömer
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alternative energy sources; Artificial neural networks; Cameras; Damage detection; Datasets; Deep learning; Design; Drones; Energy resources; Ground stations; Low cost; Noise; Object recognition; Performance measurement; Power plants; Real time; Renewable resources; Signal processing; Solar energy; Solar panels; Unmanned aerial vehicles; Wavelet transforms
• ISSN: 2754-6969; 2754-6977
• DOI: 10.1108/RIA-03-2023-0022

PurposeThis paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.Design/methodology/approachThis paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.FindingsThe training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.Originality/valueThe AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.