摘要
针对电力系统中基于相量测量技术状态估计的虚假数据注入攻击难以被成功检测的问题,本文提出一种面向电力系统线性状态估计的攻击智能检测方法.采用自编码器对电网测量数据进行多次特征提取,逐渐降低特征维度;提取信息通过softmax层进行有监督学习,从而得到基于堆叠自编码器的攻击检测算法.针对自编码器的过度拟合问题,进一步提出基于降噪自编码的攻击检测方法.采用IEEE-118节点测试系统对所提出的方法进行仿真验证,结果表明所提出的攻击检测方法计算精度和效率高于其他方法.
It is difficult to successfully detect the false data injection attacks against the linear state estimation based on phasor measurement techniques in power systems.Here,we propose an intelligent method to detect false data injection attacks.First,the auto-encoder is used to extract the features of the power grid measurement data,which is done repeatedly to gradually reduce the feature dimension.Then the finally extracted feature is subjected to supervised learning through the Softmax layer,so as to obtain an attack detection algorithm based on stacked auto-encoders.Second,the attack detection approach is improved through noise reduction to solve the over fitting of auto-encoders.Finally,the proposed method is simulated and verified by IEEE-118 node test system,and the results show that the proposed attack detection method has high computational accuracy and efficiency.
作者
戚梦逸
刘涅煊
陶晓峰
吕朋朋
QI Mengyi;LIU Niexuan;TAO Xiaofeng;L Pengpeng(NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211106)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2023年第4期460-467,共8页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(62105160)。
关键词
自编码器
相量测量
状态估计
攻击检测
auto-encoder
phasor measurement
state estimation
attack detection
