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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2015 - 15
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Fuzzy Ontology Reasoning for Power Transformer Fault Diagnosis

SAMIRMI, F. D. See more information about SAMIRMI, F. D. on SCOPUS See more information about SAMIRMI, F. D. on IEEExplore See more information about SAMIRMI, F. D. on Web of Science, TANG, W. See more information about  TANG, W. on SCOPUS See more information about  TANG, W. on SCOPUS See more information about TANG, W. on Web of Science, WU, Q. See more information about WU, Q. on SCOPUS See more information about WU, Q. on SCOPUS See more information about WU, Q. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,274 KB) | Citation | Downloads: 387 | Views: 1,204

Author keywords
fault diagnosis, ontology, fuzzy sets, multi-agent systems, power transformer

References keywords
power(13), systems(12), fuzzy(8), agent(8), ontology(7), multi(6), diagnosis(6), transformers(5), system(5), fault(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04015
Web of Science Accession Number: 000368499800014
SCOPUS ID: 84949971675

Abstract
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This paper presents a novel fuzzy ontology reasoner for power transformer fault diagnosis under a multi-agent framework. The developed ontology provides a comprehensive knowledge base as part of a multi-agent system to enable imprecision reasoning. It is the first time that a fuzzy ontology model is developed for accurate power transformer fault diagnosis. It aims to develop an improved ontology model for transformer fault diagnosis by applying the fuzzy ontology. The proposed technique deals with the imprecision situation using the fuzzy ontology, in order to build an ontology-based knowledge representation for accurate power transformer fault diagnosis. The proposed system is tested with actual transformer online data to demonstrate the functionality of the developed fuzzy ontology, which can identify the faults that are unidentifiable using a basic ontology model, and this can significantly improve the overall accuracy for transformer fault diagnosis under a multi-agent framework.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 3,878 TCR
SCOPUS® Citations for all references: 43,758 TCR

Web of Science® Average Citations per reference: 129 ACR
SCOPUS® Average Citations per reference: 1,459 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-04-18 12:28 in 208 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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