A Monitoring Algorithm for Substation Equipment Based on OPGW Communications

As the electricity demand continues to grow in a digital society, substations, as a key part of the grid infrastructure, face many challenges to the reliability and stability of their equipment. Traditional substation equipment monitoring methods such as manual and helicopter inspection suffer from slow speed, high cost, and safety hazards, making it difficult to meet real-time monitoring and efficient detection demand. In recent years, machine learning-based target detection techniques have shown great potential for automatic classification and identification of substation equipment. However, the existing methods still have the problems of insufficient feature extraction and low positioning accuracy in small-scale target detection, especially under the influence of interfering factors in complex environments, such as occlusion, light change, and scale change, the detection effect is further degraded. Aiming at the above problems, this paper proposes an improved YOLOv8 target detection model, which is dedicated to the detection of substation equipment in complex transmission and distribution line scenarios. Specifically, a multi-head self-attention (MHSA) module is introduced in front of the backbone network of YOLOv8 to enhance the feature extraction capability of the network and improve the detection accuracy of small-scale targets. Secondly, the P2 detection layer is designed and integrated to specifically address the detection needs of small-scale targets, which significantly improves the detection rate and positioning accuracy of substation equipment. Through experimental validation in the actual complex transmission and distribution line environment, the proposed detection model shows superior detection performance and robustness, effectively improves the fault detection capability of substation equipment, and ensures the safe and stable operation of the power system. The results show that the proposed model has significant advantages in substation equipment monitoring and has a wide range of application prospects.