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

DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems