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In this study, we propose a decision-making strategy for pinning-baseddistributed multi-agent (PDMA) automatic generation control (AGC) in islandedmicrogrids against stochastic communication disruptions. The target microgridis construed as a cyber-physical system, wherein the physical microgrid ismodeled as an inverter-interfaced autonomous grid with detailed system dynamicformulation, and the communication network topology is regarded as acyber-system independent of its physical connection. The primal goal of theproposed method is to decide the minimum number of generators to be pinned andtheir identities amongst all distributed generators (DGs). The pinningdecisionsare made based on complex network theories using the genetic algorithm (GA),for the purpose of synchronizing and regulating the frequencies and voltages ofall generator busbars in a PDMA control structure, i.e., without resorting to acentral AGC agent. Thereafter, the mapping of cyber-system topology and thepinning decision is constructed using deeplearning (DL) technique, so that thepinning-decision can be made nearly instantly upon detecting a new cyber-systemtopology after stochastic communication disruptions. The proposeddecision-making approach is verified using a 10-generator, 38-bus microgridthrough time-domain simulation for transient stability analysis.

A Cyber-Physical Perspective to Pinning-Decision for Distributed Multi-Agent Control in Microgrid against Stochastic Communication Disruptions