  Today's evolving power system contains an increasing amount of power
electronic interfaced energy sources and loads that require a paradigm shift in
utility operations. Sub-synchronous oscillations at frequencies around 13-15
Hz, for instance, have been reported by utilities due to wind farm controller
interactions with the grid. Dynamics at such frequencies are unobservable by
most SCADA tools due to low sampling frequencies and lack of synchronization.
Real-time or off-line frequency domain analysis of phasor measurement unit
(PMU) data has become a valuable method to identify such phenomena, at the
expense of costly power system data and communication infrastructure. This
article proposes an alternative machine learning (ML) based application for
sub-synchronous oscillation detection in wind farm applications. The
application is targeted for real-time implementation at the edge, resulting in
significant savings in terms of data and communication requirements. Validation
is performed using data from a North American wind farm operator.
