  The expansion in technology and attainability of a large number of sensors
has led to a huge amount of real-time streaming data. The real-time data in the
electrical distribution system is collected through distribution-level phasor
measurement units referred to as $\mu$PMU which report high-resolution phasor
measurements comprising various event signatures which provide situational
awareness and enable a level of visibility into the distribution system. These
events are infrequent, unschedule, and uncertain; it is a challenge to
scrutinize, detect and predict the occurrence of such events. For electrical
distribution systems, it is challenging to explicitly identify evolution
functions that describe the complex, non-linear, and non-stationary signature
patterns of events. In this paper, we seek to address this problem by
developing a physics dynamics-based approach to detect anomalies in the
$\mu$PMU streaming data and simultaneously predict the events using governing
equations. We propose a data-driven approach based on the Hankel alternative
view of the Koopman (HAVOK) operator, called DynamoPMU, to analyze the
underlying dynamics of the distribution system by representing them in a linear
intrinsic space. The key technical idea is that the proposed method separates
out the linear dynamical behaviour pattern and intermittent forcing (anomalous
events) in sequential data which turns out to be very useful for anomaly
detection and simultaneous data prediction. We demonstrate the efficacy of our
proposed framework through analysis of real $\mu$PMU data taken from the LBNL
distribution grid. DynamoPMU is suitable for real-time event detection as well
as prediction in an unsupervised way and adapts to varying statistics.
