NDLI: Evaluation of distribution fault diagnosis algorithms using ROC curves

Content Provider	IEEE Xplore Digital Library
Author	Yixin Cai Mo-Yuen Chow Wenbin Lu Lexin Li
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, 27695 USA (Yixin Cai; Mo-Yuen Chow) \|\| Department of Statistics, North Carolina State University, Raleigh, 27695 USA (Wenbin Lu; Lexin Li)
Abstract	In power distribution fault data, the percentage of faults with different causes could be very different and varies from region to region. This data imbalance issue seriously affects the performance evaluation of fault diagnosis algorithms. Due to the limitations of conventional accuracy (ACC) and geometric mean (G-mean) measures, this paper discusses the application of Receiver Operating Characteristic (ROC) curves in evaluating distribution fault diagnosis performance. After introducing how to obtain ROC curves, Artificial Neural Networks (ANN), Logistic Regression (LR), Support Vector Machines (SVM), Artificial Immune Recognition Systems (AIRS), and K-Nearest Neighbor (KNN) algorithm are compared using ROC curves and Area Under the Curve (AUC) on real-world fault datasets from Progress Energy Carolinas. Experimental results show that AIRS performs best most of the time and ANN is potentially a good algorithm with a proper decision threshold.
Starting Page	1
Ending Page	6
File Size	514682
Page Count	6
File Format	PDF
ISBN	9781424465491
ISSN	19449925
e-ISBN	9781424465514
e-ISBN	9781424483570
DOI	10.1109/PES.2010.5588154
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Support vector machines Classification algorithms Fault diagnosis Training Testing Performance evaluation ROC curves artificial neural networks artificial immune recognition systems classification fault cause identification k-nearest neighbor algorithm logistic regression power distribution systems support vector machine
Content Type	Text
Resource Type	Article

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