摘要
随着电力系统向能源互联新生态逐渐迈进及网络层和物理层的深度耦合,网络攻击对电力系统的威胁不断提升。源身份欺骗攻击作为一种新型且复杂、强隐秘性的虚假数据注入攻击,可导致电网控制系统判断错误,引发系统瘫痪。针对这一问题,提出一种基于空间特征的电网同步量测虚假数据注入攻击检测方法。该方法通过变分模态分解和改进离散正交S变换提取同步量测装置不同位置的空间特征,实现在不损失量测数据空间特征的基础上,提取量测数据的身份认证信息;结合轻量型卷积神经网络评估量测数据遭受源身份攻击的可能性,加速检测响应速度。通过实际多点同步量测数据的检测结果,验证了该方法的有效性。
As the power system gradually moves toward a new ecosystem of energy interconnection and the deep coupling of the network layer and physical layer,the threat of network attacks on the power system keeps rising.The source identity(ID)spoofing attack,as a new and complex,strong stealthy false data injection attack,can cause the grid control system to misjudge and cause system paralysis.To address this problem,a spatial feature based method is proposed for detecting false data injection attacks on synchronous measurements of power grids.It has extracted different spatial features of the synchronous measurement devices at different locations by variational modal decomposition(VMD)and improved discrete orthonormal Stockwell transform(IDOST),so as to extract the authentication information of the measurement data without losing the spatial features of the measurements.Combined with the light convolutional neural network(LCNN)to evaluate the likelihood of measurement data being attacked by source ID to enhance the speed of detection response.The effectiveness of the method is verified by the detection results of actual multi-point synchronous measurement data.
作者
郑瑶
张颉
姚文轩
邱伟
唐思豪
ZHENG Yao;ZHANG Jie;YAO Wenxuan;QIU Wei;TANG Sihao(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;Power Internet of Things Key Laboratory of Sichuan Province,Chengdu 610041,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2023年第10期128-134,共7页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(52177078)
湖南省自然科学基金资助项目(2022JJ30151)
电力物联网四川省重点实验室开放课题资助项目(PIT-F-202201)
中国博士后科学基金资助项目(BX20220102)。
关键词
广域测量系统
同步量测数据
虚假数据注入攻击
卷积神经网络
wide area measurement system
synchronous measurement data
false data injection attack
convolutional neural network
