Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal

This thesis presents a newly developed technique for the improvement of the elitist binary genetic algorithms (EGA) in implementing the reactive power planning (RPP) in power system. The genetic algorithm (GA) is a search technique based on the behaviour of natural genetics. The study conducts compa...

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Main Author:	Mohd Kamal, Mohamad Fadhil
Format:	Thesis
Language:	English
Published:	2010
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/99258/1/99258.pdf
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spelling	my-uitm-ir.992582024-12-16T02:25:45Z Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal 2010 Mohd Kamal, Mohamad Fadhil Algorithms This thesis presents a newly developed technique for the improvement of the elitist binary genetic algorithms (EGA) in implementing the reactive power planning (RPP) in power system. The genetic algorithm (GA) is a search technique based on the behaviour of natural genetics. The study conducts comparative analyses on the performances of the elitist genetic algorithms (EGA). Modified steady state genetic algorithms (SSGA) and computationally enhanced steady state genetic algorithm (CSGA) in improving the voltage stability and minimizing loss via the optimization of the RPP in power system. Elitism is one of method implemented to improve the accuracy of the solution and computation time of the GA. The application of elitism in GA constitutes the deployment of the elitism mechanism in the selection scheme or genetic operator. The elitist mechanism guarantees that the best fitness of the population discovered in the earlier generation will never disappear unless a better solution is found. The EGA ensures the quality of the solution never deteriorates as the generations progress since the fittest solution of the current population is duplicated in the subsequent generations. It may strike a fair balance between exploitation and exploration in achieving an acceptable optimum solution with an appropriate population composition. Any result inferior to the reading produced by the EGA shall be considered as a premature convergence onto a local optimum. However, the EGA has the weakness of a moderate convergent rate despite of a good search performance. The study adopts the reading produced by the EGA as the benchmark for drawing any judgment towards developing the improved EGA. 2010 Thesis https://ir.uitm.edu.my/id/eprint/99258/ https://ir.uitm.edu.my/id/eprint/99258/1/99258.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Algorithms
spellingShingle	Algorithms Mohd Kamal, Mohamad Fadhil Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
description	This thesis presents a newly developed technique for the improvement of the elitist binary genetic algorithms (EGA) in implementing the reactive power planning (RPP) in power system. The genetic algorithm (GA) is a search technique based on the behaviour of natural genetics. The study conducts comparative analyses on the performances of the elitist genetic algorithms (EGA). Modified steady state genetic algorithms (SSGA) and computationally enhanced steady state genetic algorithm (CSGA) in improving the voltage stability and minimizing loss via the optimization of the RPP in power system. Elitism is one of method implemented to improve the accuracy of the solution and computation time of the GA. The application of elitism in GA constitutes the deployment of the elitism mechanism in the selection scheme or genetic operator. The elitist mechanism guarantees that the best fitness of the population discovered in the earlier generation will never disappear unless a better solution is found. The EGA ensures the quality of the solution never deteriorates as the generations progress since the fittest solution of the current population is duplicated in the subsequent generations. It may strike a fair balance between exploitation and exploration in achieving an acceptable optimum solution with an appropriate population composition. Any result inferior to the reading produced by the EGA shall be considered as a premature convergence onto a local optimum. However, the EGA has the weakness of a moderate convergent rate despite of a good search performance. The study adopts the reading produced by the EGA as the benchmark for drawing any judgment towards developing the improved EGA.
format	Thesis
qualification_level	Master's degree
author	Mohd Kamal, Mohamad Fadhil
author_facet	Mohd Kamal, Mohamad Fadhil
author_sort	Mohd Kamal, Mohamad Fadhil
title	Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
title_short	Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
title_full	Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
title_fullStr	Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
title_full_unstemmed	Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal
title_sort	improved elitist genetic algorithm for reactive power planning in power system / mohamad fadhil mohd kamal
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/99258/1/99258.pdf
_version_	1818588003902160896

Improved elitist genetic algorithm for reactive power planning in power system / Mohamad Fadhil Mohd Kamal

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