NDLI: A Framework for Evaluating Automatic Classification of Underlying Causes of Disturbances and Its Application to Short-Circuit Faults

Content Provider	IEEE Xplore Digital Library
Author	Morais, J. Pires, Y. Cardoso, C. Klautau, A.
Copyright Year	1986
Abstract	Most works in power systems event classification concern classifying an event according to the morphology of the corresponding waveform. An important and even more difficult problem is the classification of the event underlying cause. However, the lack of labeled data is more problematic in this second scenario. This paper proposes a framework based on frame-based sequence classification (FBSC), the Alternative Transient Program (ATP), and a public dataset to advance research in this area. As a proof of concept, a thorough evaluation of automatic classification of short circuits in transmission lines is discussed. Simulations with different preprocessing (e.g., wavelets) and learning algorithms (e.g., support vector machines) are presented. The results can be reproduced at other sites and elucidate several tradeoffs when designing the front end and pattern recognition stages of a sequence classifier. For example, when considering the whole event in an offline scenario, the combination of the raw front end and a decision tree is competitive with wavelets and a neural network.
Sponsorship	IEEE Power Engineering Society
Starting Page	2083
Ending Page	2094
Page Count	12
File Size	1150111
File Format	PDF
ISSN	08858977
Volume Number	25
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Power system simulation Classification tree analysis Support vector machines underlying cause classification Fault classification machine learning sequence classification simulation of power system events
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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