  A new data-driven method is proposed to detect events in the data streams
from distribution-level phasor measurement units, a.k.a., micro-PMUs. The
proposed method is developed by constructing unsupervised deep learning anomaly
detection models; thus, providing event detection algorithms that require no or
minimal human knowledge. First, we develop the core components of our approach
based on a Generative Adversarial Network (GAN) model. We refer to this method
as the basic method. It uses the same features that are often used in the
literature to detect events in micro-PMU data. Next, we propose a second
method, which we refer to as the enhanced method, which is enforced with
additional feature analysis. Both methods can detect point signatures on single
features and also group signatures on multiple features. This capability can
address the unbalanced nature of power distribution circuits. The proposed
methods are evaluated using real-world micro-PMU data. We show that both
methods highly outperform a state-of-the-art statistical method in terms of the
event detection accuracy. The enhanced method also outperforms the basic
method.
