Online Low Frequency Oscillation Detection and Analysis System with an Ensemble Filter

TL;DR

This paper introduces an online system for detecting low-frequency oscillations in power grids, utilizing an ensemble filter and DBSCAN clustering to improve accuracy and reduce false alarms in real-time monitoring.

Contribution

The paper proposes a novel LFODA system with a voting schema and time-serial filter, enhancing real-time detection accuracy of oscillation modes and locations.

Findings

Reduces false alarms in oscillation detection

Effective classification of oscillation modes and sites

Validated with simulated and real PMU data

Abstract

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.