The growth in adoption of communication technologies in the power system results in an increase in its vulnerability towards cyberattacks. This paper presents a novel state partition-particle filter (SP-PF) based detection algorithm that dynamically adapts to varying operating conditions. The algorithm partitions state variables into size-restricted blocks, effectively grouping highly correlated variables to enhance computational efficiency and detection accuracy. Our approach consists of two main steps: (i) state-partition estimation of variables and (ii) detection based on likelihood conditions. The proposed detection algorithm was tested in a real-time cyber-physical environment using a real-time digital simulator (RTDS) in hardware-in-loop configuration with PMUs and a synchronization clock, all connected via standard TCP/UDP protocols. Experimental results demonstrate successful detection of false data injection attacks, replay attacks, and hybrid attacks under various operating conditions. Comparative analysis with extended Kalman filter shows that our approach achieves significantly improved accuracy in state estimation with reduced mean square error, enhancing the overall robustness of the detection mechanism.

Cascading failures in power systems are extremely rare occurrences caused by a combination of multiple, low probability events. The looming threat of cyberattacks on power grids, however, may result in unprecedented large-scale cascading failures, leading to a blackout. Therefore, new analysis methods are needed to study such cyber induced phenomena. In this article, we propose a data-driven method for dynamical analysis of power system cascading failures caused by cyberattacks. We provide experimental proof on how attacks may accelerate the cascading failure mechanism, in comparison to historically observed blackouts. Using a dynamic power grid model, consisting of multiple, coordinated protection schemes, we define and analyze the point of no return in a cascading failure sequence by applying the Hilbert–Huang transform for time-frequency analysis. Numerical results indicate, cyberattacks may accelerate cascading failures at least by a factor of 3x. This is due to the excitation and non-damping of multiple frequency modes greater than 1 Hz in a short time span. The proposed method is tested using time domain simulations conducted through a modified IEEE 39-bus test system, which can simulate cascading outages using coordinated protection schemes.

As future power systems become increasingly complex and interconnected to other energy carriers, a single research infrastructure can rarely provide the required test-beds to study a complete energy system, especially if different types of real power hardware are expected to be in-the-loop. Therefore, virtual interconnection of laboratories for large-scale systems plays an important role for geographically distributed realtime simulation. This paper presents the improvements made in simulation fidelity as well as usability for establishing future simulator and laboratory connections. A general procedure is proposed and analyzed for geographically distributed real-time simulation, which allows users easily to adapt this procedure to specific test cases. A systematic and comprehensive analysis of a dynamic phasor based co-simulation interface algorithm and its improvements are provided to demonstrate the advantages as well as limitations of this approach.