AUTHOR=Rafferty Mark , Liu Xueqin , Rafferty John , Xie Lei , Laverty David , McLoone Seán 

TITLE=Sequential feature selection for power system event classification utilizing wide-area PMU data

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.957955

DOI=10.3389/fenrg.2022.957955

ISSN=2296-598X

ABSTRACT=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 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.