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

This paper presents a novel approach for detecting faults in photovoltaic (PV) cells. The proposed method combines the power of You Only Look Once version 8 (YOLOv8) and Particle Swarm Optimization (PSO) architecture. Unlike existing methods, the proposed model leverages PSO to optimize the parameters of YOLOv8, enhancing detection accuracy. To evaluate the efficacy of the proposed approach, two experimental cases are conducted, one with a 70% training set and the other with an 80% training set. The PV system data is used as input for the model, and YOLOv8 is utilized to extract necessary features before detecting fault cells from the data. We use PSO algorithm to optimize the model’s parameters to achieve the best detection accuracy. The experimental results demonstrate that the proposed approach achieves the highest mean Average Precision (mAP) of 94% at an intersection over union (IoU) threshold of 0.5, outperforming existing fault detection methods in terms of accuracy and robustness. Moreover, by leveraging the power of YOLOv8 and PSO, the approach offers a promising solution for reliable and efficient fault detection in PV systems, thus making it a practical solution to enhance the system’s performance and reduce maintenance expenses.