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Title: Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements

Abstract

This paper presents an online early anomaly detection framework for phasor measurement units (PMUs) to monitor the power system dynamics and help prevent blackouts using machine learning approaches. Dynamical machine learning solutions including state space model and Kalman filter are conducted in this study to learn the nonlinear and nonstationary PMU measurements and accurately predict system behaviors in real-time. The anomalies can be detected within seconds by comparing the predicted system behaviors with the real system observations. The method proposed in this framework trains PMU data with a given time window (e.g., 5 seconds) using a dynamic nonlinear model, and then predicts system behaviors during the following time window. High prediction accuracy is achieved by applying the dynamic nonlinear model to the real-world PMU measurements – the anomalies detected are successfully validated given the recorded real-world events. High-performance-computing (HPC) techniques are utilized to further reduce computational time to provide real time power system situational awareness.

Authors:
 [1]; ORCiD logo [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:
1492435
Report Number(s):
PNNL-SA-131920
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Country of Publication:
United States
Language:
English

Citation Formats

Ren, Huiying, Hou, Zhangshuan, and Etingov, Pavel V. Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements. United States: N. p., 2018. Web. doi:10.1109/PMAPS.2018.8440495.
Ren, Huiying, Hou, Zhangshuan, & Etingov, Pavel V. Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements. United States. doi:10.1109/PMAPS.2018.8440495.
Ren, Huiying, Hou, Zhangshuan, and Etingov, Pavel V. Wed . "Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements". United States. doi:10.1109/PMAPS.2018.8440495.
@article{osti_1492435,
title = {Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements},
author = {Ren, Huiying and Hou, Zhangshuan and Etingov, Pavel V.},
abstractNote = {This paper presents an online early anomaly detection framework for phasor measurement units (PMUs) to monitor the power system dynamics and help prevent blackouts using machine learning approaches. Dynamical machine learning solutions including state space model and Kalman filter are conducted in this study to learn the nonlinear and nonstationary PMU measurements and accurately predict system behaviors in real-time. The anomalies can be detected within seconds by comparing the predicted system behaviors with the real system observations. The method proposed in this framework trains PMU data with a given time window (e.g., 5 seconds) using a dynamic nonlinear model, and then predicts system behaviors during the following time window. High prediction accuracy is achieved by applying the dynamic nonlinear model to the real-world PMU measurements – the anomalies detected are successfully validated given the recorded real-world events. High-performance-computing (HPC) techniques are utilized to further reduce computational time to provide real time power system situational awareness.},
doi = {10.1109/PMAPS.2018.8440495},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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