  Power quality (PQ) events are recorded by PQ meters whenever anomalous events
are detected on the power grid. Using neural networks with machine learning can
aid in accurately classifying the recorded waveforms and help power system
engineers diagnose and rectify the root causes of problems. However, many of
the waveforms captured during a disturbance in the power system need to be
labeled for supervised learning, leaving a large number of data recordings for
engineers to process manually or go unseen. This paper presents an autoencoder
and K-means clustering-based unsupervised technique that can be used to cluster
PQ events into categories like sag, interruption, transients, normal, and
harmonic distortion to enable filtering of anomalous waveforms from recurring
or normal waveforms. The method is demonstrated using three-phase,
field-obtained voltage waveforms recorded in a distribution grid. First, a
convolutional autoencoder compresses the input signals into a set of lower
feature dimensions which, after further processing, is passed to the K-means
algorithm to identify data clusters. Using a small, labeled dataset, numerical
labels are then assigned to events based on a cosine similarity analysis.
Finally, the study analyzes the clusters using the t-distributed stochastic
neighbor embedding (t-SNE) visualization tool, demonstrating that the technique
can help investigate a large number of captured events in a quick manner.
