NDLI: Rough set methods in power system fault classification

Content Provider	IEEE Xplore Digital Library
Author	Xiuping Xu Peters, J.F.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada (Xiuping Xu; Peters, J.F.)
Abstract	This paper presents an approach to classifying power system faults using rough set methods. A knowledge-based fault detection and identification (FDI) system for power system faults has been introduced. The FDI system has the ability to detect and classify power system faults by combining conventional signal analysis methods (e.g., FFT, IFFT and wavelets) with granular computing and rough set methods. In granular computing, experimental data is partitioned into collections of data (called information granules) that are in some way similar. Rough set methods are based on set approximation, partition of each finite universe using an indiscernibility relation, attribute reduction, decision-rule derivation, and many useful measures such as approximation accuracy and rough inclusion. Traditional fuzzy set theory is also as part of fault signal feature extraction. The FDI system derives an indication of the type of faults that have occurred and also generates classification rules for the fault classification. This system has resulted from a study of fault files recorded by the Transcan Recording System (TRS) at the Manitoba Hydro Dorsey Station over several years. The contribution of this paper is the introduction of an approach to classifying power system faults using a combination of traditional signal analysis methods and a number of computational intelligence methods (granular computing, and rough set theory).
Sponsorship	Dept. Electr. & Comput. Eng. Faculty of Graduate Studies Faculty of Eng. TRLabs Internet Innovation Centre NRC Inst. Biodiagnostics APEGM IEEE Winnipeg Sec. IEEE Canada & IEEE Manitoba Hydro Vansco Electron. Bristol Aerosp. Nygard Int. Norsat
Starting Page	100
Ending Page	105
File Size	740739
Page Count	6
File Format	PDF
ISBN	0780375149
ISSN	08407789
DOI	10.1109/CCECE.2002.1015182
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system faults Fault detection Power system analysis computing Electrical fault detection Signal analysis Fault diagnosis Wavelet analysis Fuzzy set theory Feature extraction Computational intelligence
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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