摘要
随着现代电网建设的加速,针对电网监控视频分析中存在的时延高、准确率低等问题,文章提出一种基于边缘计算的电网监控视频分析系统。通过在边缘节点部署深度学习模型,实现了高效的异常检测与故障诊断。实验结果表明,该系统在降低延迟、减少带宽占用的同时,大幅提高了故障检测准确率。该研究为智能电网建设提供了新的解决方案,并展示了边缘计算在电网监控中的应用潜力。
With the acceleration of modern power grid construction,aiming at the problems of high time delay and low accuracy in power grid monitoring video analysis,this paper proposes a power grid monitoring video analysis system based on edge computing.By deploying deep learning model in edge nodes,efficient anomaly detection and fault diagnosis are realized.The experimental results show that the system can greatly improve the accuracy of fault detection while reducing the delay and bandwidth occupation.This research provides a new solution for smart grid construction,and shows the application potential of edge computing in power grid monitoring.
作者
黄佳
HUANG Jia(Nanjing Urban Digital Governance Center,Nanjing 210000,China)
出处
《通信电源技术》
2024年第21期207-209,共3页
Telecom Power Technology
关键词
电网监控
视频分析
边缘计算
power grid monitoring
video analysis
edge computing
