NDLI: Power systems reliability calculation based on fuzzy data mining

Content Provider	IEEE Xplore Digital Library
Author	Ramos, S. Khodr, H.M. Azevedo, F. Vale, Z.
Copyright Year	2009
Abstract	This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments', which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.
Starting Page	1
Ending Page	7
File Size	1503497
Page Count	7
File Format	PDF
ISBN	9781424442416
ISSN	19449925
DOI	10.1109/PES.2009.5275783
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	Power system reliability Fuzzy systems Data mining Preventive maintenance Delta modulation Fuzzy logic Equipment failure Companies Knowledge engineering Power transformers power system maintenance knowledge discovery fuzzy logic Monte Carlo
Content Type	Text
Resource Type	Article

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