@inproceedings{ebc3b46d007f4f86b8a41438c7c40ef1,
title = "Detection of False Data Injection Attacks in Automatic Generation Control Systems Considering System Nonlinearities",
abstract = "Maintaining the power system frequency around its nominal value is a very critical issue for the system stability. This operation is performed by the Automatic Generation Control (AGC) system. A cyber attack on the AGC system may affect the whole stability and economic operation of the power system. This paper proposes a method using Recurrent Neural Networks to detect False Data Injection (FDI) attacks in AGC systems. The novelty of this work over other approaches is that the nonlinearities of the AGC system are considered, which make it difficult to use the conventional approaches to detect FDI in case of considering the nonlinearities. The AGC of a two-area power system is used and the results show that the proposed approach succeeded to detect FDI in AGC system with an accuracy of 94\%.",
author = "Abdelrahman Ayad and Mohsen Khalaf and Ehab El-Saadany",
note = "Funding Information: This publication was made possible by NPRP grant no. NPRP 9-055-2-022 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Electrical Power and Energy Conference, EPEC 2018 ; Conference date: 10-10-2018 Through 11-10-2018",
year = "2018",
month = dec,
day = "31",
doi = "10.1109/EPEC.2018.8598328",
language = "British English",
series = "2018 IEEE Electrical Power and Energy Conference, EPEC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE Electrical Power and Energy Conference, EPEC 2018",
address = "United States",
}