  This paper introduces a potential learning scheme that can dynamically
predict the stability of the reconnection of sub-networks to a main grid. As
the future electrical power systems tend towards smarter and greener
technology, the deployment of self sufficient networks, or microgrids, becomes
more likely. Microgrids may operate on their own or synchronized with the main
grid, thus control methods need to take into account islanding and reconnecting
of said networks. The ability to optimally and safely reconnect a portion of
the grid is not well understood and, as of now, limited to raw synchronization
between interconnection points. A support vector machine (SVM) leveraging
real-time data from phasor measurement units (PMUs) is proposed to predict in
real time whether the reconnection of a sub-network to the main grid would lead
to stability or instability. A dynamics simulator fed with pre-acquired system
parameters is used to create training data for the SVM in various operating
states. The classifier was tested on a variety of cases and operating points to
ensure diversity. Accuracies of approximately 85% were observed throughout most
conditions when making dynamic predictions of a given network.
