Paper Info
Title
Deep learning-based data forgery detection in automatic generation control.
Abstract
Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.
Year
Venue
Keywords
2017
IEEE Conference on Communications and Network Security
Power grid,AGC,data forgery attack,deep learning,attack detection
Field
DocType
ISSN
Data modeling,Computer science,Electric power system,Computer network,Fourier transform,Real-time computing,Artificial intelligence,Control system,Deep learning,Artificial neural network,Electricity generation,Automatic Generation Control
Conference
2474-025X
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Fengli Zhang115426.40
Qing-Hua Li2156388.15