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Title: Pattern Mining and Anomaly Detection based on the 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 potentially can help prevent blackouts due to early anomaly detection. The study presented in the paper is based on the actual PMU measurements of the U.S. western interconnection system. Given the nonlinear and nonstationary PMUs data, we developed a robust anomaly detection framework. Wavelet-based multi-resolution analysis, combined with moving-window-based outlier detection and anomaly scoring, were deployed to identify potential PMU events. The candidate events were evaluated using spatiotemporal correlation analysis, and then classified for a better understanding of the event types. The results demonstrated successful anomaly detection and classification, compared with the recorded real-world events.

Authors:
 [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1492701
Report Number(s):
PNNL-SA-126905
Journal ID: ISSN 2572-6862
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS)
Country of Publication:
United States
Language:
English

Citation Formats

Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri V., and Etingov, Pavel V. Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements. United States: N. p., 2018. Web. doi:10.24251/HICSS.2018.330.
Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri V., & Etingov, Pavel V. Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements. United States. doi:10.24251/HICSS.2018.330.
Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Zarzhitsky, Dimitri V., and Etingov, Pavel V. Sat . "Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements". United States. doi:10.24251/HICSS.2018.330.
@article{osti_1492701,
title = {Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements},
author = {Ren, Huiying and Hou, Zhangshuan and Wang, Heng and Zarzhitsky, Dimitri V. and Etingov, Pavel V.},
abstractNote = {Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability and potentially can help prevent blackouts due to early anomaly detection. The study presented in the paper is based on the actual PMU measurements of the U.S. western interconnection system. Given the nonlinear and nonstationary PMUs data, we developed a robust anomaly detection framework. Wavelet-based multi-resolution analysis, combined with moving-window-based outlier detection and anomaly scoring, were deployed to identify potential PMU events. The candidate events were evaluated using spatiotemporal correlation analysis, and then classified for a better understanding of the event types. The results demonstrated successful anomaly detection and classification, compared with the recorded real-world events.},
doi = {10.24251/HICSS.2018.330},
journal = {},
issn = {2572-6862},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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