NDLI: Exploratory analysis of massive data for distribution fault diagnosis in smart grids

Content Provider	IEEE Xplore Digital Library
Author	Yixin Cai Mo-Yuen Chow
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695 USA (Yixin Cai; Mo-Yuen Chow)
Abstract	Fault diagnosis in power distribution systems is critical to expedite the restoration of service and improve the reliability. With power grids becoming smarter, more and more data beyond utility outage database are available for fault cause identification. This paper introduces basic methodologies to integrate and analyze data from different sources. Geographic Information System (GIS) provides a framework to integrate these data through spatial and temporal relations. Features extracted from raw data provide different discriminant powers, which can be evaluated by the likelihood measure. A fault cause classifier is then trained to learn the relations between fault causes and the features. Two statistical methods, Linear Discriminant Analysis (LDA) and Logistic Regression (LR), are introduced. The assumptions, general approaches and performances of these two techniques are discussed and evaluated on a real-world outage dataset.
Starting Page	1
Ending Page	6
File Size	1715722
Page Count	6
File Format	PDF
ISBN	9781424442416
ISSN	19449925
DOI	10.1109/PES.2009.5275689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Fault diagnosis Smart grids Power system reliability Geographic Information Systems Linear discriminant analysis Power distribution Power system restoration Power grids Spatial databases Spatial-Temporal Relation Classification Fault Cause Identification Geographic Information System Power Distribution Systems Smart Grids
Content Type	Text
Resource Type	Article

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