水力发电中的设备状态智能诊断技术分析  

Analysis of Intelligent Diagnosis Technology for Equipment Status in Hydroelectric Power Generation

作　　者：肖飞 薛林锋 叶志华 XIAO Fei;XUE Linfeng;YE Zhihua(Tianshengqiao First level Hydropower Development Co.,Ltd.Hydroelectric Power Plant,Guizhou 551700,China;Guangzhou Qingtian Industrial Co.,Ltd.,Guangdong 510860,China)

机构地区：[1]天生桥一级水电开发有限责任公司水力发电厂,贵州551700 [2]广州擎天实业有限公司,广东510860

出　　处：《电子技术(上海)》2024年第11期168-169,共2页Electronic Technology

摘　　要：阐述数据采集与预处理、特征提取与选择、智能诊断模型构建等关键技术,提出基于卷积神经网络和长短期记忆网络相结合的深度学习诊断模型,以实现对水电站电气设备状态的精准评估和故障预警。This paper describes key technologies such as data collection and preprocessing,feature extraction and selection,and intelligent diagnostic model construction.It proposes a deep learning diagnostic model based on a combination of convolutional neural networks and long short-term memory networks to achieve accurate evaluation and fault warning of electrical equipment status in hydropower stations.

关 键 词：智能诊断 深度学习 设备状态评估 水力发电站 

分 类 号：TV734[水利工程—水利水电工程] TP18[自动化与计算机技术—控制理论与控制工程]