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Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow ProblemABACI, K.   , YAMACLI, V. |
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Author keywords
heuristic algorithms, iterative methods, neural networks, optimization, power system analysis computing
References keywords
power(23), neural(13), algorithm(11), optimal(9), networks(8), flow(8), artificial(7), training(6), systems(6), search(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-11-30
Volume 19, Issue 4, Year 2019, On page(s): 57 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.04007
Web of Science Accession Number: 000500274700006
SCOPUS ID: 85077289267
Abstract
Power flow (PF) is in one of the most studied non-linear problems related to power systems which heavily affects security issues such as generation cost, voltage stability and active power loss. In this paper, a simple and new approach based on artificial neural network (ANN) and differential search (DSA) algorithm has been proposed and applied for one of the most complex problems in power systems, Power Flow (PF) problem. By using the proposed DSA implemented ANN method, IEEE 9-bus, IEEE 30-bus and IEEE 118-bus test system parameters are obtained without running iterative convergence methods such as Gauss-Siedel or Newton-Raphson. By comparing with several most used non-linear iterative methods, the results obtained using the classical training method and proposed DSA implemented hybrid training methods are presented and discussed. Obtained results in this work show that the ANN based power flow method can be implemented to solve non-linear static and dynamical problems concerning power systems successfully. |
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Digital Object Identifier: 10.1088/1757-899X/883/1/012185 [CrossRef]
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[4] Optimal converter control for PV-fed DC and AC interconnection by using hybrid artificial neural networks, Yamacli, Volkan, Abaci, Kadir, Journal of Computational Design and Engineering, ISSN 2288-5048, Issue 1, Volume 8, 2021.
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[5] Development of an algorithm to train artificial neural networks for intelligent decision support systems, Sova, Oleg, Turinskyi, Oleksandr, Shyshatskyi, Andrii, Dudnyk, Volodymyr, Zhyvotovskyi, Ruslan, Prokopenko, Yevgen, Hurskyi, Taras, Hordiichuk, Valerii, Nikitenko, Anton, Remez, Artem, Eastern-European Journal of Enterprise Technologies, ISSN 1729-4061, Issue 9 (103), Volume 1, 2020.
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[10] Taking into account functional restrictions in the form of inequalities when optimizing the modes of electric power systems under conditions of partial uncertainty of the initial information, Latipov, Sherkhon, Talipova, Surayyo, Karimov, Ilhom, Mamatkulov, Asom, III INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE “ACTUAL ISSUES OF POWER SUPPLY SYSTEMS” (ICAIPSS2023), ISBN , Issue , 2024.
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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.
