摘要
为了提高风电机组运行状态监测与故障预警的实时性、精准化与智能化,提出一种数字孪生驱动的风电机组三维可视化实时监控与故障预警方法。构建了基于数字孪生的风电机组三维可视化实时监控与故障预警系统框架;设计了基于云边协同的风电机组数据采集与治理方法;利用WebGL与三维模型轻量化技术,实现了基于Web的风电机组三维可视化监控;建立了基于CNN-LSTM混合神经网络的故障预警模型,实现了风电机组关键零部件故障预警。设计、开发原型系统验证了所提方法的有效性与可行性。
To improve the real-time,precision and intelligence of operation state monitoring and fault warning of wind turbine,digital twin-driven 3D visualization monitoring and fault warning for wind turbine is proposed.Systematic framework of 3D visualization real-time monitoring and fault warning for wind turbine based on digital twins is constructed.A method for data collection and data governance of wind turbine is designed based on cloud-edge collaboration.Web-based 3D visualization monitoring of wind turbine is realized by using WebGL and 3D model lightweight technology.Fault warning model is built based on CNN(convolutional neural networks)and LSTM(long and short term memory)for fault warning for key components in wind turbine.A prototype system is designed and developed for verifying the effectiveness and feasibility of the proposed approach.
作者
杨伟新
樊小伟
孙荣富
孙雅旻
丁然
YANG Weixin;FAN Xiaowei;SUN Rongfu;SUN Yamin;DING Ran(Electric Power Research Institute,State Grid Jibei Electric Power Co.,Ltd.,Beijng 100045,China;State Grid Jibei Electric Power Co.,Ltd.,Beijng 100052,China)
出处
《弹箭与制导学报》
北大核心
2023年第2期94-102,共9页
Journal of Projectiles,Rockets,Missiles and Guidance
关键词
数字孪生
云边协同
风电机组
三维可视化监控
故障预警
digital twin
cloud-edge collaboration
wind turbine
3D visualization monitoring
fault warning
