Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime

TL;DR

This paper introduces a neuromorphic, spike-based approach for real-time remote measurement inference in AC power grids, enabling local adaptation and enhanced security without relying on traditional cyber infrastructure.

Contribution

It presents a novel neuromorphic SNN framework that translates power measurements into spike events and uses unsupervised Hebbian learning for local, energy-efficient grid monitoring and control.

Findings

Effective online inference of remote voltages and currents.

Enhanced security by eliminating reliance on cyber communication.

Validated performance on IEEE 14-bus system and experimental setups.

Abstract

In this paper, we infer remote measurements such as remote voltages and currents online with change in AC power flows using spiking neural network (SNN) as grid-edge technology for efficient coordination of power electronic converters. This work unifies power and information as a means of data normalization using a multi-modal regime in the form of spikes using energy-efficient neuromorphic learning and event-driven asynchronous data collection. Firstly, we organize the synchronous real-valued measurements at each edge and translate them into asynchronous spike-based events to collect sparse data for training of SNN at each edge. Instead of relying on error-dependent supervised data-driven learning theory, we exploit the latency-driven unsupervised Hebbian learning rule to obtain modulation pulses for switching of power electronic converters that can now comprehend grid disturbances locally and adapt their operation without requiring explicit infrastructure for global coordination. Not only does this philosophy block exogenous path arrival for cyber attackers by dismissing the cyber layer, it also entails converter adaptation to system reconfiguration and parameter mismatch issues. We conclude this work by validating its energy-efficient and effective online learning performance under various scenarios in different system sizes, including modified IEEE 14-bus system and under experimental conditions.