A Novel Approach for PV Cell Fault Detection Using YOLOv8 and Particle Swarm Optimization

This paper introduces a novel approach for detecting faults in photovoltaic (PV) cells. The proposed method combines You Only Look Once version 8 (YOLOv8) and the Particle Swarm Optimization (PSO) architecture to enhance detection accuracy. Unlike existing methods, the proposed model leverages PSO to optimize the parameters of YOLOv8. To evaluate the effectiveness of the approach, two study cases are conducted using training sets of 70% and 80%, respectively. The PV system data is utilized as input, with YOLOv8 extracting features to detect faulty cells. The PSO algorithm optimizes the model's parameters to achieve the highest detection accuracy. Experimental results demonstrate that the proposed approach outperforms existing fault detection methods in terms of accuracy and robustness, achieving a mean Average Precision at 50 (mAP@50) of 94%. By harnessing the power of YOLOv8 and PSO, the approach offers a promising solution for reliable and efficient fault detection in PV systems, making it a viable option for enhancing system performance and reducing maintenance costs.