Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'

The increasing penetration of intermittent, nonsynchronous
generation has led to a reduction in total power
system inertia. Low inertia systems are more sensitive to sudden
changes, and more susceptible to secondary issues that can result
in large scale events. Due to the short time frames involved,
automatic methods for power system event detection and diagnosis
are required. Wide-area monitoring systems can provide
the data required to detect and diagnose events; however due to
the increasing quantity of data it is next to impossible for power
system operators to manually process raw data. The important
information is required to be extracted and presented to system
operators for real/near-time decision making and control. This
paper demonstrates an approach for the wide-area classification
of a number of power system events. A mixture of sequential
feature selection and linear discriminant analysis is adopted
to reduce the dimensionality of PMU data. Successful event
classification is obtained by employing quadratic discriminant
analysis on wide-area synchronized frequency, phase angle and
voltage measurements. The reliability of the proposed method is
evaluated using simulated case studies and benchmarked against
other classification methods.