22,343 research outputs found
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
In modern highly interconnected power grids, automatic generation control
(AGC) is crucial in maintaining the stability of the power grid. The dependence
of the AGC system on the information and communications technology (ICT) system
makes it vulnerable to various types of cyber-attacks. Thus, information flow
(IF) analysis and anomaly detection became paramount for preventing cyber
attackers from driving the cyber-physical power system (CPPS) to instability.
In this paper, the ICT network traffic rules in CPPSs are explored and the
frequency domain features of the ICT network traffic are extracted, basically
for developing a robust learning algorithm that can learn the normal traffic
pattern based on the ResNeSt convolutional neural network (CNN). Furthermore,
to overcome the problem of insufficient abnormal traffic labeled samples,
transfer learning approach is used. In the proposed data-driven-based method
the deep learning model is trained by traffic frequency features, which makes
our model robust against AGC's parameters uncertainties and modeling
nonlinearities.Comment: Editor: Geert Deconinck. 18th European Dependable Computing
Conference (EDCC 2022), September 12-15, 2022, Zaragoza, Spain. Fast Abstract
Proceedings - EDCC 202
Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids
Smart grid is a large complex network with a myriad of vulnerabilities,
usually operated in adversarial settings and regulated based on estimated
system states. In this study, we propose a novel highly secure distributed
dynamic state estimation mechanism for wide-area (multi-area) smart grids,
composed of geographically separated subregions, each supervised by a local
control center. We firstly propose a distributed state estimator assuming
regular system operation, that achieves near-optimal performance based on the
local Kalman filters and with the exchange of necessary information between
local centers. To enhance the security, we further propose to (i) protect the
network database and the network communication channels against attacks and
data manipulations via a blockchain (BC)-based system design, where the BC
operates on the peer-to-peer network of local centers, (ii) locally detect the
measurement anomalies in real-time to eliminate their effects on the state
estimation process, and (iii) detect misbehaving (hacked/faulty) local centers
in real-time via a distributed trust management scheme over the network. We
provide theoretical guarantees regarding the false alarm rates of the proposed
detection schemes, where the false alarms can be easily controlled. Numerical
studies illustrate that the proposed mechanism offers reliable state estimation
under regular system operation, timely and accurate detection of anomalies, and
good state recovery performance in case of anomalies
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