摘要
智能电网的安全性受到电力大数据质量影响,为提升电网安全,通过电力大数据定位智能电网漏洞位置。采用二维小波阈值去噪方法滤除智能电网电力大数据中的噪声数据,去除电网电力数据中的冗余数据,完成电力大数据质量优化;应用数据挖掘技术构建智能电网安全漏洞挖掘模型,利用处理后电力大数据与漏洞挖掘模型实现智能电网安全漏洞定位。实验结果显示,所提方法可实现智能电网电力大数据漏洞高精度定位,对智能电网电力大数据安全保护存在积极意义。
The security of smart grid is affected by the quality of power big data.In order to improve the security of power grid,the vulnerability location of smart grid is located through power big data.The two-dimensional wavelet threshold denoising method is used to filter the noise data in smart grid power big data,remove the redundant data in power grid power data,and complete the quality optimization of power big data.Data mining technology is applied to build the model of smart grid security vulnerability mining,and the model of big data and vulnerability mining is used to locate the security vulnerability of smart grid.The experimental results show that the proposed method can achieve high-precision location of big data vulnerability in smart grid,and has positive significance for the security protection of big data in smart grid.
作者
王琨
杜亮
马来·对山拜
徐静
WANG Kun;DU Liang;MA LAI·Duishanbai;XU Jing(Xinjiang Information Industry Co.Ltd.,Urumqi 830013,China)
出处
《微型电脑应用》
2021年第8期123-126,共4页
Microcomputer Applications
关键词
智能电网
电力
大数据
漏洞挖掘模型
去噪
数据挖掘技术
冗余数据
smart grid
power
big data
vulnerability mining model
denoising
data mining technology
data redundant
