Paper Info
Title
Smart grid line event classification using supervised learning over PMU data streams.
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.
Year
DOI
Venue
2015
10.1109/IGCC.2015.7393695
IGSC
Keywords
Field
DocType
smart grids, phasor measurement units, machine learning, event detection, decision trees
Data mining,Decision tree,Units of measurement,Data stream mining,Smart grid,Subject-matter expert,Computer science,Phasor,Supervised learning,Real-time computing,C4.5 algorithm
Conference
Citations 
PageRank 
References 
1
0.36
4
Authors
5
Name
Order
Citations
PageRank
Duc Nguyen110.70
Richard Barella251.16
Scott A. Wallace3124.68
Xinghui Zhao47015.94
Xiaodong Liang53021.59