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Simplified Model and Genetic Algorithm Based Simulated Annealing Approach for Excitation Current Estimation of Synchronous MotorKAPLAN, O.   , CELIK, E. |
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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
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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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
