On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids

TL;DR

This paper investigates the robustness of neuromorphic inferential communication in microgrids against noise, highlighting its potential for secure, reliable, and energy-efficient power system coordination.

Contribution

It explores the noise resilience of neuromorphic communication in microgrids, addressing a gap in understanding its adaptability compared to biological neurons.

Findings

Neuromorphic communication shows promising noise tolerance in microgrid scenarios.

Case studies demonstrate potential challenges and solutions for real-world implementation.

Insights suggest enhanced reliability and security in microgrid control systems.

Abstract

Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids. Its event-driven paradigm facilitates promising prospect in resilient and energy-efficient coordination among power electronic converters. However, different from biological neurons that are focused in the literature, microgrids exhibit distinct architectures and features, implying potentially diverse adaptability in its capabilities to dismiss information transfer, which remains largely unrevealed. One of the biggest drawbacks in the information transfer theory is the impact of noise in the signaling accuracy. Hence, this article hereby explores the noise resiliency of neuromorphic inferential communication in microgrids through case studies and underlines potential challenges and solutions as extensions beyond the results, thus offering insights for its implementation in real-world scenarios.