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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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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: 390 | Views: 1,173

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

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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 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

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

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

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

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