Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws

TL;DR

This paper introduces Spike Talk, a novel neural network-based infrastructure for microgrid coordination that leverages power flow data for adaptive, communication-free control, addressing the Von Neumann Bottleneck.

Contribution

It proposes a new Spike Talk system using spiking neural networks for decentralized microgrid control, emphasizing physics-based information inference and online learning.

Findings

Preliminary case studies demonstrate feasibility.

Spike Talk enables adaptive, communication-free coordination.

Features synaptic plasticity for online training.

Abstract

Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without the need of communication at all. This paper unravels the physics behind Spike Talk from the perspective of its distributed infrastructure, which aims to address the Von Neumann Bottleneck. Relying on inferring information via power flows in tie lines, Spike Talk allows adaptive and flexible control and coordination itself, and features in synaptic plasticity facilitating online and local training functionality. Preliminary case studies are demonstrated with results, while more extensive validations are to be included as future scopes of work.