查看更多>>摘要：Despite great progress in modeling the resilience response of power systems under extreme events, it remains difficult to assess the evolutionary trend of system performance at a specific observation moment during such events. Conventional simulation-based assessment methods are typically time-consuming because a series of scenario-specific optimization problems must be solved as a prerequisite. Thus, a spatial-temporal graph-based approach is proposed for fast resilience assessment to provide timely warning information. The key factors, including observable meteorological information, component vulnerabilities, emergency dispatch, and repair strategies, are modeled in the form of matrices that depict the spatial and temporal relationships. Based on these matrices, a spatiotemporal graph neural network is developed to fit the mapping relationship between observable states and resilience indicators, which is trained offline and enables fast assessment via forward inference. Regarding the uncertainties of various extreme scenarios, the evaluation procedure combines the whole-process simulation and single-state replay technologies, which can respectively consider the uncertainties and provide deterministic data labeling for assessment. Finally, the effectiveness of the proposed method is verified on the benchmarks, including the IEEE 118-bus system and a realistic 2868-bus system.
查看更多>>摘要：Based on model predictive control and an attack detection mechanism, the stabilization problem of a class of nonlinear discrete-time cyber–physical systems with deception attacks at the sensor-to-controller and controller-to-actuator communication channels is studied. In this article, the deception attacks on both ends of the controller are considered using a more generic probability model. Attack detection models and mechanisms are established to detect the integrity of transmitted information in order to mitigate the impact of deception attacks. To ensure the input-to-state practical stability of the closed-loop system, a model predictive controller is designed based on the detected state. Finally, a simulation case is presented to demonstrate the stability and effectiveness of the proposed method.
Mahmoud A. AlbreemAlaa H. Al HabbashAmmar M. Abu-HudroussM.-T. EL Astal...
32-42页
查看更多>>摘要：The design of low-complexity data detection techniques for massive multiple-input multiple-output (mMIMO) systems continues to attract considerable industry and research attention due to the critical need to achieve the right tradeoff between complexity and performance, especially with the signed quadrature spatial modulation (SQSM) scheme. However, the SQSM scheme attains a high spectral efficiency and good performance but suffers from a high computational complexity with mMIMO systems. In this article, we propose an efficient low-complexity detection framework for the SQSM scheme. Sparsity detection is amalgamated in this article with minimum mean-square error (MMSE) detector by decoupling the detection of the real and imaginary vector streams. Unfortunately, the MMSE-based detector has a matrix inversion which incurs a high computational complexity. Therefore, we employed several iterative methods; i.e., conjugate gradient and Gauss–Seidel, to avoid the exact matrix inversion, and hence, the computational complexity is significantly reduced. Moreover, the proposed framework can host other iterative methods such as the JA, successive over relaxation, accelerated over relaxation, Neumann series, Newton iteration, two-parameter over relaxation, and Richardson methods. The proposed detection framework attains a significant complexity reduction with a small or insignificant deterioration in the performance.
NETL
NSTL
IEEE