Performance Comparison of Quantitative Methods for PMU Data Event Detection with Noisy Data
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This article compares distinct signal-based and
knowledge-based approaches often applied to process and detect
events in vast amounts of data collected by phasor measurement
units (PMU). The computation times and the accuracy of correct
event detections are tested and evaluated in a 1-hour data file
from the UT-Austin Independent Texas Synchrophasor Network
with phasor quantities plus an additive noise gathered at different
PMU substations. A sliding time window is considered to build
a representative model of the system operating conditions on the
fly and search for power system phenomena as soon as new data
are available
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