.png){kind=icon}
返回
False data injection attack detection based on interval affine state estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2022.108100.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the development and application of information communication technology, it makes the potential threat of cyber-attacks prominent increasingly that the power information network and physical network are becoming deeply integrated. In the energy management system (EMS), the state estimation module is the main target of hackers' for false data injection attacks (FDIAs) on power systems deliberately. Therefore, from the perspective of redundancy of system measurement data, it proposes a pseudo-measurement model based on a new neural network, convolutional long short-term memory (CLSTM) neural network, which is a combination of different networks. And using interval number and affine number to analyze and quantitatively describe the measurement uncertainty, this paper proposes a FDIAs detection model based on interval affine state estimation. It provides technicians with the upper and lower limit information of the systems state, which makes it easier to judge whether the measured data is attacked and thus exceeds the normal fluctuation range. This paper has tested IEEE 33-bus system and the results show that this method has obvious advantages in term of calculation speed and accuracy.
Keyword:
Interval affine
Neural network
Pseudo measurement
FDIAs
State estimation
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
Coordinated cyber-physical attacks considering DoS attacks in power systems电力系统中考虑DoS攻击的网络物理协同攻击
PREAI
分享收藏
Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling基于人工神经网络方法的伪量测建模在配电网状态估计中的应用
PREAI
分享收藏
查看更多内容