  A deep learning approach is proposed to detect data and system anomalies
using high-resolution continuous point-on-wave (CPOW) or phasor measurements.
Both the anomaly and anomaly-free measurement models are assumed to have
unknown temporal dependencies and probability distributions. Historical
training samples are assumed for the anomaly-free model, while no training
samples are available for the anomaly measurements. By transforming the
anomaly-free observations into uniform independent and identically distributed
sequences via a generative adversarial network, the proposed approach deploys a
uniformity test for anomaly detection at the sensor level. A distributed
detection scheme that combines sensor level detections at the control center is
also proposed that combines local detections to form more reliable detections.
Numerical results demonstrate significant improvement over the state-of-the-art
solutions for various bad-data cases using real and synthetic CPOW and PMU data
sets.
