基于虚拟现实技术和机器学习的电力故障应急演练系统设计
Design of Emergency Drill system for Electrical Power Based on Virtual Reality Technology and Machine Learning
吴宇红 1纪涛 1许泉强 2朱优优3
作者信息
- 1. 国网浙江省电力有限公司德清县供电公司,浙江 湖州 313000
- 2. 德清欣电电力建设有限公司,浙江 湖州 313000
- 3. 浙江大学台州研究院,浙江 台州 318000
- 折叠
摘要
为了提高应急处置人员面对电力故障的操作能力,文章介绍了一个基于虚拟现实技术和机器学习的电力故障应急演练系统.该系统通过多点位三维摄像头,搭载超广角镜头,实时采集培训人员的人体骨骼数据信息,通过主成分分析(PCA)算法以及k近邻(KNN)算法对骨骼信息进行特征提取和分类,可以真实模拟电力电子设备发生的常见故障,极大节省了演练费用,有效降低了人员伤亡概率.
Abstract
In order to improve the operation ability of emergency personnel facing electrical power,an electrical power emergency drill system based on virtual reality technology and machine learning has been developed.The system collects the human bone data information of training personnel in real time through a multi-point 3D camera equipped with ultra-wide-angle lens.Principal component analysis(PCA)algorithm and K-nearest neighbor(KNN)algorithm are used to extract and classify the bone information.The electrical power emergency drill system can simulate common faults that occur in power electronic equipment truly,saving drill costs greatly and reducing the probability of casualties effectively.
关键词
电力故障/虚拟现实技术/主成分分析/k近邻Key words
electrical power/virtual reality technology/principal component analysis/K-nearest neighbor引用本文复制引用
出版年
2024
NETL
NSTL
万方数据