  In the monitoring of a complex electric grid, it is of paramount importance
to provide operators with early warnings of anomalies detected on the network,
along with a precise classification and diagnosis of the specific fault type.
In this paper, we propose a novel multi-stage early warning system prototype
for electric grid fault detection, classification, subgroup discovery, and
visualization. In the first stage, a computationally efficient anomaly
detection method based on quartiles detects the presence of a fault in real
time. In the second stage, the fault is classified into one of nine pre-defined
disaster scenarios. The time series data are first mapped to highly
discriminative features by applying dimensionality reduction based on temporal
autocorrelation. The features are then mapped through one of three
classification techniques: support vector machine, random forest, and
artificial neural network. Finally in the third stage, intra-class clustering
based on dynamic time warping is used to characterize the fault with further
granularity. Results on the Bonneville Power Administration electric grid data
show that i) the proposed anomaly detector is both fast and accurate; ii)
dimensionality reduction leads to dramatic improvement in classification
accuracy and speed; iii) the random forest method offers the most accurate,
consistent, and robust fault classification; and iv) time series within a given
class naturally separate into five distinct clusters which correspond closely
to the geographical distribution of electric grid buses.
