@inproceedings{a3370a43e37848ffa75c7ddccef262a7,
title = "Detection of false data injection in automatic generation control systems using Kalman filter",
abstract = "Automatic Generation Control (AGC) is a vital power system component responsible for the system frequency regulation. Also, it helps to minimize the tie-line power deviation in multi-area systems. The AGC uses communication to send/receive measurements/control actions about frequency and power deviation in power system. Small errors in AGC can drive the frequency out of the allowable range and blackouts may occur. Since communication links in recent smart grids are targets of cyber attackers, this renders AGC systems in modern smart grids susceptible to false data injection attacks. This paper investigates the impact of cyber attacks on the AGC and how the adversary can perform an attack against it. Also, it proposes a method to detect these attacks using a Kalman filter-based technique. To confirm the effectiveness of this approach, a 2-area power system is simulated using MATLAB/Simulink. The results show that the utilized technique is capable of detecting various types of false data injection attacks against AGC systems.",
author = "Mohsen Khalaf and Amr Youssef and Ehab El-Saadany",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Electrical Power and Energy Conference, EPEC 2017 ; Conference date: 22-10-2017 Through 25-10-2017",
year = "2018",
month = feb,
day = "7",
doi = "10.1109/EPEC.2017.8286194",
language = "British English",
series = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
address = "United States",
}