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Article:Optimal Location and Control Parameter Settings of UPFC using Differential Evolution Algorithm

by R.Vanitha, M.Sudhakaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 4
Year of Publication: 2011
Authors: R.Vanitha, M.Sudhakaran
10.5120/3812-5264

R.Vanitha, M.Sudhakaran . Article:Optimal Location and Control Parameter Settings of UPFC using Differential Evolution Algorithm. International Journal of Computer Applications. 31, 4 ( October 2011), 20-26. DOI=10.5120/3812-5264

@article{ 10.5120/3812-5264,
author = { R.Vanitha, M.Sudhakaran },
title = { Article:Optimal Location and Control Parameter Settings of UPFC using Differential Evolution Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 4 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number4/3812-5264/ },
doi = { 10.5120/3812-5264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:15.864961+05:30
%A R.Vanitha
%A M.Sudhakaran
%T Article:Optimal Location and Control Parameter Settings of UPFC using Differential Evolution Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 4
%P 20-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main objective of this paper is to find the optimal location and control parameters settings of Unified Power Flow Controller (UPFC) with regard to power loss minimization. The proposed algorithm is based on steady state power injection model of UPFC. In this paper, two Evolutionary optimization techniques, namely Differential Evolution Algorithm (DE) and Genetic Algorithm (GA) are employed to solve optimal power flow problems. IEEE 14 bus & IEEE 30 bus test power systems are used for studies. The obtained results indicate that both techniques can successfully find the optimal location and control parameter settings of UPFC, but DE is faster than GA from the time perspective.

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Index Terms

Computer Science
Information Sciences

Keywords

Optimal power flow Power loss minimization Genetic Algorithm Differential Evolution Evolutionary Optimization technique

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