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PUBLISHER

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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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  2/2024 - 1
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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: 945 | Views: 827

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
Web of Science Accession Number: 001242091800001
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.


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


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