Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50218

Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements

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Title:Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements
Authors:Ren, Huiying
Hou, Zhangshuan
Wang, Heng
Zarzhitsky, Dimitri
Etingov, Pavel
Keywords:Monitoring, Control, and Protection
PMU, anomaly detection, situational awareness, wavelet
Date Issued:03 Jan 2018
Abstract:Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability, and helps prevent electric grid blackouts due to early anomaly detection. The study presented in this paper is based on real PMU measurements of the U.S. Western Interconnection system. Given the nonlinear and non-stationary PMU data, we developed a robust anomaly detection framework that uses wavelet-based multi-resolution analysis with moving-window-based outlier detection and anomaly scoring to identify potential PMU events. Candidate events were evaluated via spatiotemporal correlation analysis and classified for a better understanding of event types, resulting in successful anomaly detection and classification of the recorded events.
Pages/Duration:7 pages
URI:http://hdl.handle.net/10125/50218
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.330
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Monitoring, Control, and Protection


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