Anomaly detection using optimally placed μPMU sensors in distribution grids

Mahdi Jamei; Anna Scaglione; Ciaran Roberts; Emma Stewart; Sean Peisert; Chuck McParland; Alex McEachern

doi:10.1109/TPWRS.2017.2764882

Anomaly detection using optimally placed μPMU sensors in distribution grids

Mahdi Jamei, Anna Scaglione, Ciaran Roberts, Emma Stewart, Sean Peisert, Chuck McParland, Alex McEachern

Electrical, Computer, and Energy Engineering, School of (ECEE)

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

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. In this paper, focusing on microphasor 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. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors 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.

Original language	English (US)
Pages (from-to)	3611-3623
Number of pages	13
Journal	IEEE Transactions on Power Systems
Volume	33
Issue number	4
DOIs	https://doi.org/10.1109/TPWRS.2017.2764882
State	Published - Jul 2018

Keywords

Distribution grid
anomaly detection
micro-phasor measurement unit (μPMU)
optimal placement

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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title = "Anomaly detection using optimally placed μPMU sensors in distribution grids",

abstract = "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. In this paper, focusing on microphasor 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. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors 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.",

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note = "Funding Information: Manuscript received November 23, 2016; revised April 10, 2017 and August 25, 2017; accepted September 16, 2017. Date of publication October 25, 2017; date of current version June 18, 2018. This work was supported in part by the Director, Office of Electricity Delivery and Energy Reliability, Cyber-security for Energy Delivery Systems program, of the U.S. Department of Energy, under contracts DE-AC02-05CH11231 and DEOE0000780. This paper was presented in part at the 50th Hawaii International Conference on System Sciences, Waikoloa, HI, USA, January 2017. Paper no. TPWRS-01762-2016. (Corresponding author: Mahdi Jamei.) M. Jamei and A. Scaglione are with the Department of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287 USA (e-mail: mjamei@asu.edu; anna.scaglione@asu.edu).",

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AU - McParland, Chuck

AU - McEachern, Alex

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