Synergies between advanced communications, computing and artificial
intelligence are unraveling new directions of coordinated operation and
resiliency in microgrids. On one hand, coordination among sources is
facilitated by distributed, privacy-minded processing at multiple locations,
whereas on the other hand, it also creates exogenous data arrival paths for
adversaries that can lead to cyber-physical attacks amongst other reliability
issues in the communication layer. This long-standing problem necessitates new
intrinsic ways of exchanging information between converters through power lines
to optimize the system's control performance. Going beyond the existing power
and data co-transfer technologies that are limited by efficiency and
scalability concerns, this paper proposes neuromorphic learning to implant
communicative features using spiking neural networks (SNNs) at each node, which
is trained collaboratively in an online manner simply using the power exchanges
between the nodes. As opposed to the conventional neuromorphic sensors that
operate with spiking signals, we employ an event-driven selective process to
collect sparse data for training of SNNs. Finally, its multi-fold effectiveness
and reliable performance is validated under simulation conditions with
different microgrid topologies and components to establish a new direction in
the sense-actuate-compute cycle for power electronic dominated grids and
microgrids.

通过神经形态学学习，在微电网中使用脉冲神经网络（SNN）在每个节点上实现了沟通特征的植入，通过节点之间的功率交换在线协作地进行训练，从而验证了其多方面有效性和可靠性，为电子主导的电网和微电网中的感知 - 执行 - 计算循环建立了新的方向。