Abstract
This paper contributes to the growing body of work that aims to
characterize similarities and differences between synchrophasor data
from real-power systems and those from synthetic power systems with
emulated Phasor Measurement Units (PMUs). In particular, we survey
previous works that characterize PMU noise and analyze the impacts on
applications of these time-series data into machine learning algorithms
in the power systems domain. We benchmark these methodologies with three
datasets: data from an Oregon State University local PMU network, from
two PMUs using the same set of sensors, and from multiple-utility
interconnect-wide data. We found that it is important to consider each
signal individually when synthesizing PMU data with noise, and that the
noise needs to be adjusted by key statistical metrics.