  Frequently recurring transient faults in a transmission network may be
indicative of impending permanent failures. Hence, determining their location
is a critical task. This paper proposes a novel image embedding aided deep
learning framework called DeVLearn for faulted line location using PMU
measurements at generator buses. Inspired by breakthroughs in computer vision,
DeVLearn represents measurements (one-dimensional time series data) as
two-dimensional unthresholded Recurrent Plot (RP) images. These RP images
preserve the temporal relationships present in the original time series and are
used to train a deep Variational Auto-Encoder (VAE). The VAE learns the
distribution of latent features in the images. Our results show that for faults
on two different lines in the IEEE 68-bus network, DeVLearn is able to project
PMU measurements into a two-dimensional space such that data for faults at
different locations separate into well-defined clusters. This compressed
representation may then be used with off-the-shelf classifiers for determining
fault location. The efficacy of the proposed framework is demonstrated using
local voltage magnitude measurements at two generator buses.
