  Instance selection is a vital technique for energy big data analytics. It is
challenging to process a massive amount of streaming data generated at high
speed rates by intelligent monitoring devices. Instance selection aims at
removing noisy and bad data that can compromise the performance of data-driven
learners. In this context, this paper proposes a novel similarity based
instance selection (SIS) method for real-time phasor measurement unit data. In
addition, we develop a variant of the Hoeffding-Tree learner enhanced with the
SIS for classifying disturbances and cyber-attacks. We validate the merits of
the proposed learner by exploring its performance under four scenarios that
affect either the system physics or the monitoring architecture. Our
experiments are simulated by using the datasets of industrial control system
cyber-attacks. Finally, we conduct an implementation analysis which shows the
deployment feasibility and high-performance potential of the proposed learner,
as a part of real-time monitoring applications.
