Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing

TL;DR

This paper introduces a neuromorphic computing approach using spiking neural networks for energy-efficient, real-time classification of power system disturbances, leveraging event-driven processing and unsupervised learning.

Contribution

It presents a novel SNN-based framework with a QR decomposition technique for disturbance classification, improving energy efficiency and real-time performance in power system monitoring.

Findings

Achieves accurate disturbance classification with low energy consumption.

Utilizes event-driven neuromorphic architecture for real-time processing.

Validated on real power system data from the New England-New York system.

Abstract

Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring. Although many conventional machine learning techniques are very successful in classifying events, they rely on extracting information from PMU data at control centers and processing them through CPU/GPUs, which are highly inefficient in terms of energy consumption. To solve this challenge without compromising accuracy, this paper presents a novel methodology based on event-driven neuromorphic computing architecture for classification of power system disturbances. A Spiking Neural Network (SNN)-based computing framework is proposed, which exploits sparsity in disturbances and promotes local event driven operation for unsupervised learning and inference from incoming data. Spatio-temporal information of PMU signals is first extracted and encoded into spike trains and classification is achieved with SNN-based supervised and unsupervised learning framework. Moreover, a QR decomposition-based selection technique is proposed to identify signals participating in the low rank subspace of multiple disturbance events. Performance of the proposed method is validated on data collected from a 16-machine, 5-area New England-New York system.