Research on intelligent cross-boundary alarm technology of substation based on 3D real-time vision
Runbo Lu, Ying Zhang, Hao Chen, Yungen Liu, Yanmei Deng, Ke Zeng, Shanqiang Feng, Hongbiao Lun · IET conference proceedings. · 2025
With the rapid development of smart grids, the safety monitoring and operational management of substations have become critical components in ensuring the stability and reliability of power supply. Traditional monitoring systems rely heavily on manual surveillance, which is time-consuming, labor-intensive, and prone to errors or omissions. This study focuses on the development of an intelligent cross-boundary alarm system for substations based on real-time 3D vision technology, aiming to provide efficient and accurate safety monitoring of personnel in the work area. The system is designed to promptly detect and issue alerts when unauthorized boundary-crossing behaviors occur within the substation. By utilizing movable cameras equipped with real-time 3D vision technology, our system achieves high-precision acquisition of the workers' 3D geographical information. This data not only includes the spatial positioning of the personnel but also provides detailed information about their preci se posture relative to the surrounding environment. Furthermore, the system seamlessly maps the 3D geographical data of the workers onto a pre-constructed 3D model of the substation. Based on this, real-time and intuitive safety situation analysis and alerts are performed, significantly enhancing the safety management level of the substation work area.