Pinning Decision in Interconnected Systems with Communication Disruptions under Multi-Agent Distributed Control Topology

In this study, we propose a decision-making strategy for pinning-based distributed multi-agent (PDMA) automatic generation control (AGC) in islanded microgrids against stochastic communication disruptions. The target microgrid is construed as a cyber-physical system, wherein the physical microgrid is modeled as an inverter-interfaced autonomous grid with detailed system dynamic formulation, and the communication network topology is regarded as a cyber-system independent of its physical connection. The primal goal of the proposed method is to decide the minimum number of generators to be pinned and their identities amongst all distributed generators (DGs). The pinning-decisions are made based on complex network theories using the genetic algorithm (GA), for the purpose of synchronizing and regulating the frequencies and voltages of all generator bus-bars in a PDMA control structure, i.e., without resorting to a central AGC agent. Thereafter, the mapping of cyber-system topology and the pinning decision is constructed using deep-learning (DL) technique, so that the pinning-decision can be made nearly instantly upon detecting a new cyber-system topology after stochastic communication disruptions. The proposed decision-making approach is verified using a 10-generator, 38-bus microgrid through time-domain simulation for transient stability analysis. Simulations show that the proposed pinning decision making method can achieve robust frequency control with minimum number of active communication channels.

A method for assessing resilience of high-speed EMUs considering a network-based system topology and performance data

The high complexity of the system topology and the uncertainty of threats necessitate the consideration of the resilience of high-speed electric multiple units (EMUs). This paper first gives a definition of high-speed EMU resilience. Then, the structure of a high-speed EMU is described in the form of a network to enable the application of network science for resilience evaluation based on corresponding mathematical expressions. Afterward, a measure of system performance (MSP) is constructed that considers the influence of the high-speed EMU topology and performance data. According to the definition of resilience of a high-speed EMU, a system resilience measurement (SRM) is proposed that comprehensively considers the degree and time of the change in system performance. The validity of our method is then illustrated by applying it to the system topology and performance data associated with the Chinese standard electric multiple units (CEMUs) that serve on high-speed railways in China. Experiments are reported to illustrate the concept of resilience and the procedure for its measurement and to present comparisons with an existing resilience index. Our results indicate that the SRM proposed here can capture the sensitivity of the response of high-speed EMUs to the disturbances, thereby supporting the optimal design and risk management.