  The widespread deployment of phasor measurement unit (PMU) overpower systems
makes it possible to monitor and analyze grid dynamics in real-time.
Low-frequency oscillation is harmful to power system equipment and operation,
and in the worst-case scenario may lead to cascading failures. Therefore, it is
critical to detect and identify them as soon as they appear. This paper
presents an online low-frequency oscillation detection and analysis (LFODA)
system, which has the merit of significantly reducing the chance of false alarm
via a voting schema and a time-serial filter. A novel algorithm based on
density-based spatial clustering of applications with noise (DBSCAN) is
proposed to classify oscillation modes as well as to group their corresponding
buses/monitoring sites. Performance of the LFODA system is evaluated through
experiments using both simulated and real-world PMU data.
