  Proliferation of advanced metering devices with high sampling rates in
distribution grids, e.g., micro-phasor measurement units ({\mu}PMU), provides
unprecedented potentials for wide-area monitoring and diagnostic applications,
e.g., situational awareness, health monitoring of distribution assets.
Unexpected disruptive events interrupting the normal operation of assets in
distribution grids can eventually lead to permanent failure with expensive
replacement cost over time. Therefore, disruptive event classification provides
useful information for preventive maintenance of the assets in distribution
networks. Preventive maintenance provides wide range of benefits in terms of
time, avoiding unexpected outages, maintenance crew utilization, and equipment
replacement cost. In this paper, a PMU-data-driven framework is proposed for
classification of disruptive events in distribution networks. The two
disruptive events, i.e., malfunctioned capacitor bank switching and
malfunctioned regulator on-load tap changer (OLTC) switching are considered and
distinguished from the normal abrupt load change in distribution grids. The
performance of the proposed framework is verified using the simulation of the
events in the IEEE 13-bus distribution network. The event classification is
formulated using two different algorithms as; i) principle component analysis
(PCA) together with multi-class support vector machine (SVM), and ii)
autoencoder along with softmax classifier. The results demonstrate the
effectiveness of the proposed algorithms and satisfactory classification
accuracies.

Machine Learning;Systems and Control