This paper studies the load frequency control (LFC) performance of a multi-area power network targeted by false data injection attacks (FDIAs). To this end, a cyberattack-resilient defense strategy consisting of the model and learning-based methods is proposed to improve the network frequency response. A strategy based on comparing the state estimation performed by the model-based observer with a threshold value is used in the presented mechanism to detect attacks. After detecting an attack, an artificial intelligence (AI) observer predicts the control signals and compares them with the observed ones. When there is a significant deviation, an event-trigger strategy blocks the observed signal and sends the predicted signal to the physical network. In the presented strategy, a model-free nonsingular terminal sliding mode control (MFNTSMC) scheme based on the ultra-local model (ULM) principle is also developed as the secondary controller to regulate the network frequency response under FDIAs. Also, a sliding mode observer is designed to estimate the unknown terms related to the ULM. The presented controller improves the finite-time convergence of the system states to the origin and inherits the intrinsic robustness of sliding mode methods. Moreover, it provides high-precision tracking under disturbances and uncertainties. An auxiliary deep policy gradient method with actor and critic neural networks is designed to enhance the controller’s dynamic efficiency. The practical implementation of the suggested strategy is assessed utilizing the Speedgoat-based real-time platform and compared with the other methods under physical limitations and FDIAs.

Increased use of distributed renewable energy resources in low-voltage distribution grids open up for communication-based voltage control. However, non-ideal communication can affect the system and cause damage to the system. In particular, long update intervals, delays, and packet losses can be seen as imperfections of communication and should be compensated for. One solution is modeling these features and compensating for them in the control method. In this paper, a hybrid method to model the communication features and electrical grid together is presented. The model considers continuous dynamics of the electrical grid, discrete dynamics of the communication, as well as long update rates of communication, delays, and packet losses.

Computer architects rely heavily on software-based microarchitecture simulators, which typically take hours or days to produce results. We have developed LiveSim, a novel microarchitectural simulation methodology that provides simulation results within seconds, making it suitable for interactive use. LiveSim works by creating in-memory checkpoints of application state, and then executing randomly selected samples from these checkpoints in parallel to produce simulation results. The initial results, which we call LiveSample, are reported less than one second after starting the simulation. As more samples are simulated the results become more accurate and are updated in real-time. Once enough samples are gathered, LiveSim provides confidence intervals for the reported values and continues simulation until it reaches the target confidence level, which we call LiveCI. We evaluated LiveSim using SPEC CPU 2006 benchmarks and found that within 5 seconds after starting simulation, LiveSample results reached an average error of 3.51% compared to full simulation, and the LiveCI results were available within 41 seconds on average.