摘要
为降低电气设备内部潜伏性局部放电缺陷导致运行设备发生突发性绝缘击穿故障次数,文中以某在运126 kV GIS设备绝缘放电为对象,进行电气设备局部放电融合诊断与智能预警系统研究。基于局部放电过程中表征出的声电信号特征与在线监测方法,采用动态频率采集法与云平台的形式设计了一套局部放电融合诊断系统,完成设备内部局部放电信号的采集与缺陷的初步定位。通过局部放电时间间隔内信号均值、标准差、最大值等状态图变量的分析,将缺陷状态分为平稳发展阶段与高击穿风险阶段,并通过BP神经网络实现缺陷发展状态的严重性自动评估及智能预警。试验结果证明所设计融合诊断与智能预警系统可以完成缺陷低风险阶段与高风险阶段的动态跟踪与智能预警。
For reducing the number of sudden dielectric breakdown of the operating equipment due to the latent partial discharge defects inside electrical equipment,the dielectric discharge of one operating 126 kV GIS equipment is taken as the object in this paper and the research on fusion diagnosis and intelligent early warning system of partial discharge is performed.Based on the characteristics of acoustic signals and online monitoring methods characterized in the process of partial discharge,a set of partial discharge fusion diagnosis system using dynamic frequency acquisition method and cloud platform is adopted to complete the partial discharge signal acquisition and preliminary positioning of defects inside the equipment.The defect state is divided into the stable development stage and the high breakdown risk stage through the analysis of the state diagram variables such as average signal,standard deviation and maximum value inside the interval of partial discharge time,and the automatic assessment of severity and the intelligent early warning of the defect development state is achieved by the BP neutral network.The test results prove that the designed fusion diagnosis and intelligent early-warning system can complete the dynamic tracking and early warning of the defect at low-risk and high-risk stage.
作者
王晓康
牛勃
马飞越
刘江明
谭东现
马建文
伍弘
WANG Xiaokang;NIU Bo;MA Feiyue;LIU Jiangming;TAN Dongxian;MA Jianwen;WU Hong(Wuzhong Company of State Grid Ningxia Power Co.,Ningxia Wuzhong 751100,China;Power Research Institute of State Grid Ningxia Power Co.,Yinchuan 750011,China;Maintenance Branch of State Grid Zhejiang Electric Power Co.,Hangzhou 311232,China;Shanghai Wenyou Industrial Co.,Ltd.,Shanghai 201419,China;Rongyan(Shanghai)Electric Technology Co.,Ltd.,Shanghai 201807,China)
出处
《高压电器》
CAS
CSCD
北大核心
2021年第11期93-100,107,共9页
High Voltage Apparatus
关键词
电气设备
局部放电
融合诊断
动态跟踪
智能预警
electrical equipment
partial discharge
fusion diagnosis
dynamic tracking
intelligent early warning
作者简介
王晓康(1986-),男,硕士,高级工程师,从事变电设备状态检测、故障诊断方面的研究;牛勃(1989-),男,硕士,高级工程师,从事开关类设备状态检测、评估以及变电设备故障诊断方面的研究(E-mail:15695013715@163.com)。
