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Title: Anomaly Detection Using Optimally-Placed μPMU Sensors in Distribution Grids

Abstract

IEEE As the distribution grid moves toward a tightly-monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. Here, focusing on Micro-Phasor Measurement Unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. And due to the key role of the μPMU devices in our architecture, a source-constrained optimal μPMU placement is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.

Authors:
 [1];  [2];  [3];  [3];  [3];  [3];  [4]
  1. Arizona State Univ., Tempe, AZ (United States). Electrical, Computer, and Energy Engineering
  2. Arizona State Univ., Tempe, AZ (United States). School of Electrical, Computer and Energy Engineering
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Power Standards Lab., Alameda, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE), Infrastructure Security and Energy Restoration (ISER) (OE-30)
OSTI Identifier:
1415973
Grant/Contract Number:  
AC02-05CH11231; OE0000780
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distribution grid; micro-phasor measurement unit (μPMU); anomaly detection; optimal placement

Citation Formats

Jamei, Mahdi, Scaglione, Anna, Roberts, Ciaran, Stewart, Emma, Peisert, Sean, McParland, Chuck, and McEachern, Alex. Anomaly Detection Using Optimally-Placed μPMU Sensors in Distribution Grids. United States: N. p., 2017. Web. doi:10.1109/TPWRS.2017.2764882.
Jamei, Mahdi, Scaglione, Anna, Roberts, Ciaran, Stewart, Emma, Peisert, Sean, McParland, Chuck, & McEachern, Alex. Anomaly Detection Using Optimally-Placed μPMU Sensors in Distribution Grids. United States. doi:10.1109/TPWRS.2017.2764882.
Jamei, Mahdi, Scaglione, Anna, Roberts, Ciaran, Stewart, Emma, Peisert, Sean, McParland, Chuck, and McEachern, Alex. Wed . "Anomaly Detection Using Optimally-Placed μPMU Sensors in Distribution Grids". United States. doi:10.1109/TPWRS.2017.2764882.
@article{osti_1415973,
title = {Anomaly Detection Using Optimally-Placed μPMU Sensors in Distribution Grids},
author = {Jamei, Mahdi and Scaglione, Anna and Roberts, Ciaran and Stewart, Emma and Peisert, Sean and McParland, Chuck and McEachern, Alex},
abstractNote = {IEEE As the distribution grid moves toward a tightly-monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. Here, focusing on Micro-Phasor Measurement Unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. And due to the key role of the μPMU devices in our architecture, a source-constrained optimal μPMU placement is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.},
doi = {10.1109/TPWRS.2017.2764882},
journal = {IEEE Transactions on Power Systems},
number = 4,
volume = 33,
place = {United States},
year = {Wed Oct 25 00:00:00 EDT 2017},
month = {Wed Oct 25 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 25, 2018
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