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Title: Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements

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. Furthermore, 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.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1493762
Report Number(s):
PNNL-SA-131425
Journal ID: ISSN 2572-6862; ISBN 978-0-9981331-1-9
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Journal Name:
Proceedings of the Annual Hawaii International Conference on System Sciences
Additional Journal Information:
Conference: 51st Hawaii International Conference on System Sciences, Waikoloa Village, HI, 3-6 Jan. 2018; Journal ID: ISSN 2572-6862
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri, and Etingov, Pavel. Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements. United States: N. p., 2018. Web. doi:10.24251/HICSS.2018.330.
Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri, & Etingov, Pavel. Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements. United States. doi:10.24251/HICSS.2018.330.
Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri, and Etingov, Pavel. Wed . "Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements". United States. doi:10.24251/HICSS.2018.330. https://www.osti.gov/servlets/purl/1493762.
@article{osti_1493762,
title = {Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements},
author = {Ren, Huiying and Hou, Zhangshuan and Wang, Heng and Zarzhitsky, Dimitri and Etingov, Pavel},
abstractNote = {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. Furthermore, 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.},
doi = {10.24251/HICSS.2018.330},
journal = {Proceedings of the Annual Hawaii International Conference on System Sciences},
issn = {2572-6862},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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Figures / Tables:

Figure 1 Figure 1: Wavelet-based PMU anomaly detection and classification framework.

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