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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  2/2019 - 2

 HIGHLY CITED PAPER 

Differential Evolution Implementation for Power Quality Disturbances Monitoring using OpenCL

SOLIS-MUNOZ, F. J. See more information about SOLIS-MUNOZ, F. J. on SCOPUS See more information about SOLIS-MUNOZ, F. J. on IEEExplore See more information about SOLIS-MUNOZ, F. J. on Web of Science, OSORNIO-RIOS, R. A. See more information about  OSORNIO-RIOS, R. A. on SCOPUS See more information about  OSORNIO-RIOS, R. A. on SCOPUS See more information about OSORNIO-RIOS, R. A. on Web of Science, ROMERO-TRONCOSO, R. J. See more information about  ROMERO-TRONCOSO, R. J. on SCOPUS See more information about  ROMERO-TRONCOSO, R. J. on SCOPUS See more information about ROMERO-TRONCOSO, R. J. on Web of Science, JAEN-CUELLAR, A. Y. See more information about JAEN-CUELLAR, A. Y. on SCOPUS See more information about JAEN-CUELLAR, A. Y. on SCOPUS See more information about JAEN-CUELLAR, A. Y. on Web of Science
 
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Author keywords
evolutionary computation, parallel programming, parallel processing, power quality, power system faults

References keywords
power(29), comput(14), optimization(13), quality(10), evolution(10), algorithm(10), systems(9), parallel(9), energy(9), soft(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 13 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02002
Web of Science Accession Number: 000475806300002
SCOPUS ID: 85066330466

Abstract
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This article presents a new methodology to implement a computational parallel scheme based on Differential Evolution (DE) algorithm through the use of Graphical Processing Units (GPU). A system application in which it is possible to perform an online monitoring of Power Quality Disturbances (PQD) in electric grids is presented as a case study, where a fitting of the parameters of a mathematical model is performed through this technique. Hyper-parameter optimization of the parallel Differential Evolution algorithm is performed for the assigned fitting function. As a result of this parallel implementation, a speed-up of 37 times compared with the serial implementation is achieved by using a single low budget GPU. The work presented shows a significant speed and accuracy improvement compared with Micro-Genetic Algorithm for Power Quality Analysis (MGA-PQA) technique.


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References Weight

Web of Science® Citations for all references: 19,021 TCR
SCOPUS® Citations for all references: 24,028 TCR

Web of Science® Average Citations per reference: 366 ACR
SCOPUS® Average Citations per reference: 462 ACR

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

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