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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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2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
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2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

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  4/2018 - 9

 HIGHLY CITED PAPER 

Simplified Model and Genetic Algorithm Based Simulated Annealing Approach for Excitation Current Estimation of Synchronous Motor

KAPLAN, O. See more information about KAPLAN, O. on SCOPUS See more information about KAPLAN, O. on IEEExplore See more information about KAPLAN, O. on Web of Science, CELIK, E. See more information about CELIK, E. on SCOPUS See more information about CELIK, E. on SCOPUS See more information about CELIK, E. on Web of Science
 
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Download PDF pdficon (1,456 KB) | Citation | Downloads: 773 | Views: 1,629

Author keywords
reactive power compensation, power factor, artificial intelligence, genetic algorithms, simulated annealing

References keywords
power(29), reactive(13), compensation(11), synchronous(9), search(9), energy(9), algorithm(9), control(8), automatic(8), motor(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 75 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04009
Web of Science Accession Number: 000451843400009
SCOPUS ID: 85058805696

Abstract
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Reactive power demanded by many loads besides active power is one of the important issue in terms of the efficient use of energy. The optimal solution of reactive power demand can be performed by tuning the excitation current of synchronous motor available in power system. This paper presents an effective application of genetic algorithm-based simulated annealing (GASA) algorithm to solve the problem of excitation current estimation of synchronous motors. Firstly, the multiple linear regression model used in a few studies for estimation of excitation current of synchronous motor, is considered and regression coefficients of this model are optimized by GASA algorithm using training data collected from experimental setup performed. The supremacy of GASA over some recently reported algorithms such as gravitational search algorithm, artificial bee colony and genetic algorithm is widely illustrated by comparing the estimation results. Owing to the observation of weak regression coefficient of load current indicating that it is not much beneficial to excitation current, load current is removed from the regression model. Then, the remaining regression coefficients are tuned to accommodate new modification. It is seen from the findings that both training and testing performance of the simplified model are improved further. The major conclusions drawn from this study are that it introduces a new efficient algorithm for the concerned problem as well as the multiple linear regression model, which has the advantages of simplicity and cost-friendliness.


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Cited-By Clarivate Web of Science

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Cited-By CrossRef

[1] A powerful variant of symbiotic organisms search algorithm for global optimization, Çelik, Emre, Engineering Applications of Artificial Intelligence, ISSN 0952-1976, Issue , 2020.
Digital Object Identifier: 10.1016/j.engappai.2019.103294
[CrossRef]

[2] Rescheduling‐based congestion management by metaheuristic algorithm: Hybridizing lion and moth search models, Srivastava, Jyoti, Yadav, Naresh Kumar, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, ISSN 0894-3370, Issue 2, Volume 35, 2022.
Digital Object Identifier: 10.1002/jnm.2952
[CrossRef]

[3] Commutation current ripple minimization of brushless DC motor drive based on programmed phase current references, Çelik, Emre, Öztürk, Nihat, Electrical Engineering, ISSN 0948-7921, Issue 6, Volume 103, 2021.
Digital Object Identifier: 10.1007/s00202-021-01264-y
[CrossRef]

[4] A multi-sample particle swarm optimization algorithm based on electric field force, Zhou, Shangbo, Han, Yuxiao, Sha, Long, Zhu, Shufang, Mathematical Biosciences and Engineering, ISSN 1551-0018, Issue 6, Volume 18, 2021.
Digital Object Identifier: 10.3934/mbe.2021369
[CrossRef]

[5] ANALYSIS OF THE ELECTRICITY METERING SYSTEM FOR OWN ELECTRIC SUBSTATION NEEDS, Stets, Sergiy, Stets, Andriy, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, ISSN 2391-6761, Issue 2, Volume 10, 2020.
Digital Object Identifier: 10.35784/iapgos.932
[CrossRef]

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


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