变电站电气设备检测与三维建模系统

目前变电站三维虚拟现实技术是现代电力工业重要的研究方向之一,其中三维建模技术是非常重要的一部分,尤其是在变电站这种有着特殊要求的建筑中,三维建模技术为变电站的建设和测绘提供了极大的便利。但三维建模技术存在无法根据设备二维图像直接建模的缺点,以及场景建模体积过大,难以快速建立单区域设备模型、现场测绘复杂度大等问题。基于此,探索构建了一种适用于变电站设备运维管理的深度学习三维建模系统,并改进Yolov5视觉检测算法,改进后的算法检测精度高达97.5%,检测速率为156 fps,实现三维建模系统中对设备类型的快速检测。基于智能检测搭建的三维建模系统,实现了自动识别类型与快速建模,并与辅助设备监控系统与激光雷达智能联动,为变电站运维人员提供了直观形象、交互友好的监控人机界面。

Substation electrical equipment detection and three-dimensional modeling system

At present,3D virtual reality technology for substations is one of the important research directions in the modern power industry.3D modeling technology is a very important part,especially in buildings with special requirements such as substations,which provides great convenience for substation surveying and mapping.However,three-dimensional modeling technology has the disadvantages that it cannot directly model the two-dimensional image of the equipment,and the scene modeling volume is too large,it is difficult to quickly establish a single-region equipment model,and the complexity of on-site surveying and mapping is large.On this basis,this paper explores and constructs a deep learning 3D modeling system suitable for substation equipment operation and maintenance management,and improves Yolov5 visual inspection algorithm.The improved algorithm has a detection accuracy of 97.5%and a detection rate of 156 fps,thus realizing a quick detection of device types in the three-dimensional modeling system.Based on intelligent detection,the 3D modeling system realizes automatic identification of types and rapid modeling,and intelligent linkage with auxiliary equipment monitoring system and lidar,which provides an intuitive and interactive monitoring human-machine interface for substation operation and maintenance personnel.