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

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...

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Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Bian, Desong, Yu, Zhe, Shi, Di, Diao, Ruisheng, Wang, Zhiwei
Format: Article
Language:English
Subjects:
Alarms
Channels
Clustering
Electric power grids
False alarms
Kalman filters
Oscillation modes
Prony's method
Real time
Shape recognition
Signal processing
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Summary: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.
ISSN:2331-8422