  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.
