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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: 643243560
doi: 10.4316/AECE


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  3/2013 - 18

Estimating Probability of Gas Breakdown Using Grey-Fuzzy Logic

CHEN, M.-J. See more information about CHEN, M.-J. on SCOPUS See more information about CHEN, M.-J. on IEEExplore See more information about CHEN, M.-J. on Web of Science, WU, Y.-C. See more information about  WU, Y.-C. on SCOPUS See more information about  WU, Y.-C. on SCOPUS See more information about WU, Y.-C. on Web of Science, WEN, K.-L. See more information about  WEN, K.-L. on SCOPUS See more information about  WEN, K.-L. on SCOPUS See more information about WEN, K.-L. on Web of Science, TSAI, M.-T. See more information about TSAI, M.-T. on SCOPUS See more information about TSAI, M.-T. on SCOPUS See more information about TSAI, M.-T. on Web of Science
 
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Download PDF pdficon (736 KB) | Citation | Downloads: 286 | Views: 1,512

Author keywords
grey system theory, fuzzy theory, gas breakdown, probability estimation

References keywords
grey(25), system(12), fuzzy(12), chen(7), systems(6), deng(6), control(5), applications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 113 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03018
Web of Science Accession Number: 000326321600018
SCOPUS ID: 84884935729

Abstract
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Designing the protection system of high voltage power systems involves elucidating the properties of gas breakdown. This work presents a simplified mathematical approach based on grey and fuzzy methods to estimate the probability of gas breakdown, which differs from other grey-fuzzy methods. With the help of the data normalization process and a linear fuzzy membership function in determining the proper value of the distinguishing coefficient, the proposed approach outperforms other grey-fuzzy-based methods, the curve fitting method, and the neural network method in terms of estimation accuracy. The average relative error of test cases 1 and 2 with the normalization processing is 6.43%, which is 2.53 lower than that of the neural network method, 4.35% lower than that of the curve fitting method, 17.64 lower than that of the 5-interval max-max fuzzy-grey method, 1.51% lower than that of the 5-interval min-min fuzzy-grey method, 13.87% lower than that of the 3-interval max-max method, and 2.72% lower than that of the 3-interval min-min method.


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

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References Weight

Web of Science® Citations for all references: 368 TCR
SCOPUS® Citations for all references: 471 TCR

Web of Science® Average Citations per reference: 14 ACR
SCOPUS® Average Citations per reference: 18 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 2017-04-24 09:43 in 91 seconds.




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