Voltage constrained optimal power flow based using genetic algorithm
Voltage constrained of Optimal Power Flow (OPF) is one of several methods to minimize fuel costs while maintaining system reliability constraints. In this study, Genetic Algorithm (GA) was applied to solve the problem of OPF. Active power generator, generator-buses voltages, transformer tap and in...
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Fakulti Kejuruteraan ,UKM,Bangi.
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ukm-95022016-12-14T06:50:07Z http://journalarticle.ukm.my/9502/ Voltage constrained optimal power flow based using genetic algorithm Yassir Asnawi, Teuku Hasannuddin, Voltage constrained of Optimal Power Flow (OPF) is one of several methods to minimize fuel costs while maintaining system reliability constraints. In this study, Genetic Algorithm (GA) was applied to solve the problem of OPF. Active power generator, generator-buses voltages, transformer tap and injection capacitor are used as control parameters. Effectiveness of the proposed method was tested on IEEE 30 bus system and it has been compared to other optimization of power fl ow using other methods, for example the Evolutionary Programming (EP), Differential Evolution (DE) and Particle Swarm Optimization (PSO) methods. Simulation results of the three-phase voltage profi le along the tested feeder using the proposed method provide better results than other mentioned methods. Before applying the proposed method, voltage profi le on bus 12 (load-buses) is equal to 1.058 p.u, which is beyond the acceptable limits. However, after applying the proposed method, it improved the voltage profi le at bus 12 to be within the acceptable limit, which is equal to 1.045 p.u. Fakulti Kejuruteraan ,UKM,Bangi. 2015 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/9502/1/2.pdf Yassir Asnawi, and Teuku Hasannuddin, (2015) Voltage constrained optimal power flow based using genetic algorithm. Jurnal Kejuruteraan, 27 . pp. 9-14. ISSN 0128-0198 http://www.ukm.my/jkukm/?page_id=557 |
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Digital Repository |
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institution |
Universiti Kebangasaan Malaysia |
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Online Access |
language |
English |
description |
Voltage constrained of Optimal Power Flow (OPF) is one of several methods to minimize fuel costs while maintaining
system reliability constraints. In this study, Genetic Algorithm (GA) was applied to solve the problem of OPF. Active power
generator, generator-buses voltages, transformer tap and injection capacitor are used as control parameters. Effectiveness
of the proposed method was tested on IEEE 30 bus system and it has been compared to other optimization of power fl ow
using other methods, for example the Evolutionary Programming (EP), Differential Evolution (DE) and Particle Swarm
Optimization (PSO) methods. Simulation results of the three-phase voltage profi le along the tested feeder using the proposed
method provide better results than other mentioned methods. Before applying the proposed method, voltage profi le on bus
12 (load-buses) is equal to 1.058 p.u, which is beyond the acceptable limits. However, after applying the proposed method,
it improved the voltage profi le at bus 12 to be within the acceptable limit, which is equal to 1.045 p.u. |
format |
Article |
author |
Yassir Asnawi, Teuku Hasannuddin, |
spellingShingle |
Yassir Asnawi, Teuku Hasannuddin, Voltage constrained optimal power flow based using genetic algorithm |
author_facet |
Yassir Asnawi, Teuku Hasannuddin, |
author_sort |
Yassir Asnawi, |
title |
Voltage constrained optimal power flow based using genetic algorithm |
title_short |
Voltage constrained optimal power flow based using genetic algorithm |
title_full |
Voltage constrained optimal power flow based using genetic algorithm |
title_fullStr |
Voltage constrained optimal power flow based using genetic algorithm |
title_full_unstemmed |
Voltage constrained optimal power flow based using genetic algorithm |
title_sort |
voltage constrained optimal power flow based using genetic algorithm |
publisher |
Fakulti Kejuruteraan ,UKM,Bangi. |
publishDate |
2015 |
url |
http://journalarticle.ukm.my/9502/ http://journalarticle.ukm.my/9502/ http://journalarticle.ukm.my/9502/1/2.pdf |
first_indexed |
2023-09-18T19:55:04Z |
last_indexed |
2023-09-18T19:55:04Z |
_version_ |
1777406474040901632 |