NDLI: Smart grid line event classification using supervised learning over PMU data streams

Content Provider	IEEE Xplore Digital Library
Author	Nguyen, Duc Barella, Richard Wallace, Scott A. Zhao, Xinghui Liang, Xiaodong
Copyright Year	2015
Description	Author affiliation: Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John's, A1B 3X5, USA (Liang, Xiaodong) \|\| School of Engineering and Computer Science, Washington State University Vancouver, 98686, USA (Nguyen, Duc; Barella, Richard; Wallace, Scott A.; Zhao, Xinghui)
Abstract	Emerging smart grid technology offers the possibility of a more reliable, efficient, and flexible energy infrastructure. A core component of the smart grid are phasor measurement units (PMUs) which offer the ability to capture time-coherent measurements across a geographically distributed area. However, due to the fast sampling rate of these devices, a significant volume of data is generated on a daily basis and this presents challenges for how to leverage the information most effectively. In this paper, we address this challenge by applying machine learning techniques to PMU data for the purpose of detecting line events in a wide-area power grid. Specifically, we use archived synchrophasor data from PMUs located across the Pacific Northwest to train and test a decision tree built using the J48 algorithm. In contrast to other studies exploring machine learning in the context of the smart grid, our work uses PMU data from a large, active, power grid as opposed to data obtained from a simulation. We show that our classifier performs as well as hand-coded rules developed by a domain expert when applied at locations near to a line fault and that it significantly outperforms hand-coded rules when identifying line faults from a distance.
Starting Page	1
Ending Page	8
File Size	1264104
Page Count	8
File Format	PDF
e-ISBN	9781509001729
DOI	10.1109/IGCC.2015.7393695
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Training Event detection Volume measurement Machine learning Phasor measurement units Smart grids Decision trees
Content Type	Text
Resource Type	Article

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