.png){kind=icon}
返回
Data-Driven Resilient Automatic Generation Control Against False Data Injection Attacks
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2021.3058413.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.
Keyword:
Automatic generation control
Control systems
Mathematical model
Power system dynamics
Load modeling
Frequency control
Cyberattack
Attack-tolerant control
automatic generation control (AGC)
cyber-physical system
data-driven regression
false data injection attack (FDIA)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.9
论文数:
8.8K
被引数:
7.4W
机构
引用论文
Load altering attack-tolerant defense strategy for load frequency control system.png)
APPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAI分享收藏Event-Triggered Robust Adaptive Sliding Mode Fault-Tolerant Control For Nonlinear Systems非线性系统的事件触发鲁棒自适应滑模容错控制
PREAI
分享收藏
查看更多内容