# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1812.11266