基于SCADA告警数据的电网故障类型判断方法  

Power Grid Fault Type Judgment Method Based on SCADA Alarm Data

作　　者：吴俊杰 李一荻 刘亮 罗宇 戴雯菊 WU Junjie;LI Yidi;LIU Liang;LUO Yu;DAI Wenju(Guiyang Power Supply Company of Guizhou Power Grid Co.,Ltd.,Guiyang 550000,China)

机构地区：[1]贵州电网有限责任公司贵阳供电局,贵州贵阳550000

出　　处：《微型电脑应用》2024年第1期77-79,共3页Microcomputer Applications

基　　金：贵州电网有限责任公司贵阳供电局科技项目(060100KK52200010,GZKJXM20200657)。

摘　　要：为了提高电网故障诊断效果,提出基于SCADA告警数据的电网故障类型判断方法。构建引入动量项、自适应学习率的改进小波神经网络故障识别模型,采用提升小波对元器件两端线路数正序信号进行分裂、预估、调整等过程分解,获取不同尺度正序信号输入神经网络,输出结果即为细化的电网故障类型。实验结果表明,分解尺度为3时,故障录波信号均方误差最小,小波神经网络性能更稳定。该方法根据相间两相电流突变量情况判断故障类型及故障相,可精准判断电网故障类型及故障原因。In order to improve the effect of power grid fault diagnosis,a power grid fault type judgment method based on SCADA alarm data is proposed.The improved wavelet neural network fault identification model with momentum term and adaptive learning rate is constructed.The lifting wavelet is used to decompose the positive sequence signals of lines at both ends of components through the processes of splitting,prediction and adjustment.It obtains the positive sequence signals of different scales,inputs them to the neural network,and the output result is the refined power grid fault type.The experimental results show that when the decomposition scale is 3,the mean square error of fault recording signal is the smallest,and the performance of wavelet neural network is more stable.This method judges the fault type and fault phase according to the sudden change of phase-to-phase two-phase current,and can accurately judge the fault type and fault cause of power grid.

关 键 词：数据采集与监视控制系统 电网故障 类型判断 故障录波数据 小波神经网络 故障识别模型 

分 类 号：TM77[电气工程—电力系统及自动化]