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

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


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Fault Location on Radial Distribution Systems Using Wavelets and Artificial Neural Networks with a New Data Processing Feature

NERI Jr., A. L. See more information about NERI Jr., A. L. on SCOPUS See more information about NERI Jr., A. L. on IEEExplore See more information about NERI Jr., A. L. on Web of Science, MOREIRA, F. A. See more information about  MOREIRA, F. A. on SCOPUS See more information about  MOREIRA, F. A. on SCOPUS See more information about MOREIRA, F. A. on Web of Science, de SOUZA, B. A. See more information about de SOUZA, B. A. on SCOPUS See more information about de SOUZA, B. A. on SCOPUS See more information about de SOUZA, B. A. on Web of Science
 
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Download PDF pdficon (1,795 KB) | Citation | Downloads: 402 | Views: 312

Author keywords
distribution power systems, fault location, wavelet transform, data preprocessing, artificial neural networks

References keywords
power(25), fault(22), distribution(21), systems(18), location(18), wavelet(14), neural(10), networks(9), analysis(7), wavelets(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-05-31
Volume 24, Issue 2, Year 2024, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.02001
SCOPUS ID: 85195640726

Abstract
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Every power system is vulnerable to fault occurrences. Under permanent fault conditions, maintenance crew has the duty to detect the problem, repair the defect and recover the power supply system. If the fault is previously located, the repair can be performed faster. The common methods to locate the fault in electrical distribution systems use the final user information or some heuristics with fuse coordination and the loss of loads. In this paper, a new algorithm for fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet transform, a new data preprocessing feature, and artificial neural networks, this new algorithm tries to approximate the fault location using data provided by only one measurement point at the beginning of the feeder.


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

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References Weight

Web of Science® Citations for all references: 1,691 TCR
SCOPUS® Citations for all references: 2,399 TCR

Web of Science® Average Citations per reference: 47 ACR
SCOPUS® Average Citations per reference: 67 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 2024-07-15 05:04 in 195 seconds.




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