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In modern highly interconnected power grids, automatic generation control(AGC) is crucial in maintaining the stability of the power grid. The dependenceof the AGC system on the information and communications technology (ICT) systemmakes it vulnerable to various types of cyber-attacks. Thus, information flow(IF) analysis and anomaly detection became paramount for preventing cyberattackers from driving the cyber-physical power system (CPPS) to instability.In this paper, the ICT network traffic rules in CPPSs are explored and thefrequency domain features of the ICT network traffic are extracted, basicallyfor developing a robust learning algorithm that can learn the normal trafficpattern 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 methodthe deep learning model is trained by traffic frequency features, which makesour model robust against AGC's parameters uncertainties and modelingnonlinearities.

Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning