  Power systems are prone to a variety of events (e.g. line trips and
generation loss) and real-time identification of such events is crucial in
terms of situational awareness, reliability, and security. Using measurements
from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose
to identify events by extracting features based on modal dynamics. We combine
such traditional physics-based feature extraction methods with machine learning
to distinguish different event types. Including all measurement channels at
each PMU allows exploiting diverse features but also requires learning
classification models over a high-dimensional space. To address this issue,
various feature selection methods are implemented to choose the best subset of
features. Using the obtained subset of features, we investigate the performance
of two well-known classification models, namely, logistic regression (LR) and
support vector machines (SVM) to identify generation loss and line trip events
in two datasets. The first dataset is obtained from simulated generation loss
and line trip events in the Texas 2000-bus synthetic grid. The second is a
proprietary dataset with labeled events obtained from a large utility in the
USA involving measurements from nearly 500 PMUs. Our results indicate that the
proposed framework is promising for identifying the two types of events.
