Multi-objective service restoration in distribution networks using genetic algorithm

Electricity is the backbone of each industrialised society and economy. Modern countries are not used to having even short power blackouts. As an effective postfault supply restoration strategy for distribution networks plays a key part in improving service reliability and enhancing customer satisfa...

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Main Author:	Moazami, Ehsan
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Electric power distribution Genetic algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/47590/1/fk%202013%2067R.pdf
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spelling	my-upm-ir.475902016-07-22T04:33:12Z Multi-objective service restoration in distribution networks using genetic algorithm 2013-04 Moazami, Ehsan Electricity is the backbone of each industrialised society and economy. Modern countries are not used to having even short power blackouts. As an effective postfault supply restoration strategy for distribution networks plays a key part in improving service reliability and enhancing customer satisfaction, where there has been considerable research effort focused on this problem. The main challenge has been in reducing the search space so as to achieve an optimal solution within an acceptable computing burden. Furthermore, restoration is a multi-objective problem that used for solving the minimization of out of service area, minimization of switching operation and minimization of power loss whilst considering the technical constraints. This thesis presents a new approach of supply restoration service using the Genetic Algorithm. The GA is robust in searching a global optimal solution for the large-scale combinatorial optimization problems. A new hybrid Genetic Algorithm is proposed for reducing the search space and execution burden in solving the supply restoration problems. A proposed algorithm is investigated for radiality checking that is found very efficient in distribution restoration problems. Another proposed technique is implemented to improve the penalty strategy to enhance the performance of algorithm and reduce the convergence iteration. The effectiveness of the proposed method is demonstrated by testing on two case studies, a 33-bus test system and a 16 bus test system. Then the results are compared with the previous works all using GA in restoration. Comparisons show the improvements in reducing of number of iteration and fulfilling the radiality of the system after restoration. Findings through comparisons are shown that the proposed method will be able to do full restoration and energize all loads. Also, full reenergizing of all loads as the most important objective function is satisfied with less number of switching and better voltage profile. According to the comparison of the result of thesis with other previous work,it can be observed that reducing the number of iteration is significantly reduced. Results shows very low iteration number and low computation burden compare to other previous works. Electric power distribution Genetic algorithms 2013-04 Thesis http://psasir.upm.edu.my/id/eprint/47590/ http://psasir.upm.edu.my/id/eprint/47590/1/fk%202013%2067R.pdf application/pdf en public masters Universiti Putra Malaysia Electric power distribution Genetic algorithms
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electric power distribution Genetic algorithms
spellingShingle	Electric power distribution Genetic algorithms Moazami, Ehsan Multi-objective service restoration in distribution networks using genetic algorithm
description	Electricity is the backbone of each industrialised society and economy. Modern countries are not used to having even short power blackouts. As an effective postfault supply restoration strategy for distribution networks plays a key part in improving service reliability and enhancing customer satisfaction, where there has been considerable research effort focused on this problem. The main challenge has been in reducing the search space so as to achieve an optimal solution within an acceptable computing burden. Furthermore, restoration is a multi-objective problem that used for solving the minimization of out of service area, minimization of switching operation and minimization of power loss whilst considering the technical constraints. This thesis presents a new approach of supply restoration service using the Genetic Algorithm. The GA is robust in searching a global optimal solution for the large-scale combinatorial optimization problems. A new hybrid Genetic Algorithm is proposed for reducing the search space and execution burden in solving the supply restoration problems. A proposed algorithm is investigated for radiality checking that is found very efficient in distribution restoration problems. Another proposed technique is implemented to improve the penalty strategy to enhance the performance of algorithm and reduce the convergence iteration. The effectiveness of the proposed method is demonstrated by testing on two case studies, a 33-bus test system and a 16 bus test system. Then the results are compared with the previous works all using GA in restoration. Comparisons show the improvements in reducing of number of iteration and fulfilling the radiality of the system after restoration. Findings through comparisons are shown that the proposed method will be able to do full restoration and energize all loads. Also, full reenergizing of all loads as the most important objective function is satisfied with less number of switching and better voltage profile. According to the comparison of the result of thesis with other previous work,it can be observed that reducing the number of iteration is significantly reduced. Results shows very low iteration number and low computation burden compare to other previous works.
format	Thesis
qualification_level	Master's degree
author	Moazami, Ehsan
author_facet	Moazami, Ehsan
author_sort	Moazami, Ehsan
title	Multi-objective service restoration in distribution networks using genetic algorithm
title_short	Multi-objective service restoration in distribution networks using genetic algorithm
title_full	Multi-objective service restoration in distribution networks using genetic algorithm
title_fullStr	Multi-objective service restoration in distribution networks using genetic algorithm
title_full_unstemmed	Multi-objective service restoration in distribution networks using genetic algorithm
title_sort	multi-objective service restoration in distribution networks using genetic algorithm
granting_institution	Universiti Putra Malaysia
publishDate	2013
url	http://psasir.upm.edu.my/id/eprint/47590/1/fk%202013%2067R.pdf
_version_	1747811946255613952

Multi-objective service restoration in distribution networks using genetic algorithm

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