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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
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
 
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Download PDF pdficon (1,274 KB) | Citation | Downloads: 274 | Views: 645

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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[CrossRef] [Full Text] [SCOPUS Times Cited 1]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef] [SCOPUS Record]


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[CrossRef] [Web of Science Times Cited 436]


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[CrossRef] [SCOPUS Record]


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[CrossRef]


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[CrossRef]


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[15] D. M. Popovici, C. M. Bogdan, M. Polceanu, R. Querrec, "Applying of an ontology based modeling approach to cultural heritage systems", Advances in Electrical and Computer Engineering, vol. 11, no. 3, pp. 105-110, 2011.
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[16] O. Matei, "Ontology-based knowledge organization for the radiograph images segmentation", Advances in Electrical and Computer Engineering, vol. 8, no. 1, pp. 56-61, 2008.
[CrossRef] [Full Text] [SCOPUS Times Cited 9]


[17] K. Petridis, S. Bloehdorn, C. Saathoff, N. Simou, S. Dasiopoulou, V. Tzouvaras, S. Handschuh, Y. Avrithis, Y. Kompatsiaris, S. Staab, "Knowledge representation and semantic annotation of multimedia content", IEE Proceedings - Vision, Image and Signal Processing, vol. 153, no.3, pp. 255-262, 2006.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 35]


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[19] F. D. Samirmi, W. H. Tang, Q. H. Wu, "Power transformer condition monitoring and fault diagnosis with multi-agent system based on ontology reasoning", 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference, pp. 1-6, 2013.
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[CrossRef]


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[CrossRef]


[29] N. Gao, G. J. Zhang, Z. Qian, Z. Yan, D. H. Zhu, "Diagnosis of DGA based on fuzzy and ANN methods", Proc. of 1998 International Symposium on Electrical Insulating Materials, pp. 767-770, 1998.
[CrossRef] [Web of Science Times Cited 5]




References Weight

Web of Science® Citations for all references: 1,357 TCR
SCOPUS® Citations for all references: 1,822 TCR

Web of Science® Average Citations per reference: 45 ACR
SCOPUS® Average Citations per reference: 61 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 2016-12-08 05:06 in 169 seconds.




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Faculty of Electrical Engineering and Computer Science
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