  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 pinningdecisions
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 busbars 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 deeplearning (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.
