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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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  4/2019 - 7

 HIGHLY CITED PAPER 

Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem

ABACI, K. See more information about ABACI, K. on SCOPUS See more information about ABACI, K. on IEEExplore See more information about ABACI, K. on Web of Science, YAMACLI, V. See more information about YAMACLI, V. on SCOPUS See more information about YAMACLI, V. on SCOPUS See more information about YAMACLI, V. on Web of Science
 
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Download PDF pdficon (500 KB) | Citation | Downloads: 441 | Views: 899

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
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Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

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[15] T. Olsson, K. Magnusson, "Training artificial neural networks with genetic algorithms for stock forecasting" KTH Royal Institute Of Technology School Of Computer Science And Communication, Stockholm, 2016, vol. 1.

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[17] F. Valdez, O. Castillo, P. Melin, "Ant Colony Optimization for the Design Of Modular Neural Networks in Pattern Recognition," in Proc. International Joint Conference on Neural Networks, Canada, 2016, pp. 24-29.
[CrossRef] [SCOPUS Times Cited 9]


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[CrossRef] [Web of Science Times Cited 253] [SCOPUS Times Cited 307]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 61]


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References Weight

Web of Science® Citations for all references: 5,495 TCR
SCOPUS® Citations for all references: 7,377 TCR

Web of Science® Average Citations per reference: 167 ACR
SCOPUS® Average Citations per reference: 224 ACR

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

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-09-15 14:33 in 164 seconds.




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