  High-impedance faults (HIF) are difficult to detect because of their low
current amplitude and highly diverse characteristics. In recent years, machine
learning (ML) has been gaining popularity in HIF detection because ML
techniques learn patterns from data and successfully detect HIFs. However, as
these methods are based on supervised learning, they fail to reliably detect
any scenario, fault or non-fault, not present in the training data.
Consequently, this paper takes advantage of unsupervised learning and proposes
a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to
the conventional autoencoders that learn from normal behavior, the
convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals
eliminating the need for presence of diverse non-HIF scenarios in the CAE
training. CAE distinguishes HIFs from non-HIF operating conditions by employing
cross-correlation. To discriminate HIFs from transient disturbances such as
capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of
the probability distribution shape. The performance evaluation studies
conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD
reliably detects HIFs, outperforms the state-of-the-art HIF detection
techniques, and is robust against noise.
