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The electrical grid is one of the critical infrastructures of any country whose importance makes them an attractive target for malicious cyber attacks. This paper considers the particular case of data modification attacks in smart grids, where the data generated by Phasor Measurement Units (PMUs) is modified by the adversary in order to introduce errors in the monitoring and control applications that rely on PMU data. The proposed methodology is based on evaluating the equivalent impedance of a transmission line from buses at its either end. The deviations in the magnitude and angle of the equivalent impedances in the presence of a data modification attack are used to detect the attack. Extensive simulations using real PMU data are used to verify the accuracy of the proposed detection mechanism.

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models -along with their algorithmic implementation and performance evaluation -in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the Koopman operator theory which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the graph convolutional neural networks which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.INDEX TERMS Data-driven techniques, graph neural network, Koopman operator, transient dynamics.