Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques

Detalhes bibliográficos
Autor(a) principal: Muhammad, Munir Azam
Data de Publicação: 2018
Outros Autores: Mokhlis, Hazlie, Naidu, Kanendra, Franco, John Fredy [UNESP], Illias, Hazlee Azil, Wang, Li
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1049/iet-gtd.2017.1134
http://hdl.handle.net/11449/163822
Resumo: Reconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non-radiality network solutions during the optimisation process. To address this issue, this work proposes to store pre-determined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi-objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33-bus and 118-bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions.
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spelling Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniquesevolutionary computationparticle swarm optimisationcombinatorial mathematicsdatabase management systemsmathematics computingdistribution networkspower engineering computingintegrated database approachmultiobjective network reconfigurationdistribution system performance enhancementcomplex combinatorial processglobal optimum solutionsoptimal network configurationsnonradiality network solution elimination33-bus distribution systems118-bus distribution systemsswitching actionsvoltage deviationpower loss minimizationdiscrete evolutionary particle swarm optimisation techniquesdiscrete evolutionary programmingnetwork reconfiguration optimisationpre-determined network radiality solutionsReconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non-radiality network solutions during the optimisation process. To address this issue, this work proposes to store pre-determined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi-objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33-bus and 118-bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions.SATU Joint Research SchemeUniversity of MalayaUniv Malaya, Dept Elect Engn, Fac Engn, Kuala Lumpur 50603, MalaysiaUniv Kuala Lumpur, British Malaysian Inst, Elect Technol Sect, Bt 8,Jalan Sungai Pusu, Gombak 53100, Selangor Darul, MalaysiaSao Paulo State Univ, Sch Energy Engn, BR-19274000 Rosana, SP, BrazilNatl Cheng Kung Univ, Dept Elect Engn, Coll Elect Engn & Comp Sci, Tainan 70101, TaiwanSao Paulo State Univ, Sch Energy Engn, BR-19274000 Rosana, SP, BrazilUniversity of Malaya: ST020-2017Inst Engineering Technology-ietUniv MalayaUniv Kuala LumpurUniversidade Estadual Paulista (Unesp)Natl Cheng Kung UnivMuhammad, Munir AzamMokhlis, HazlieNaidu, KanendraFranco, John Fredy [UNESP]Illias, Hazlee AzilWang, Li2018-11-26T17:45:06Z2018-11-26T17:45:06Z2018-02-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article976-986application/pdfhttp://dx.doi.org/10.1049/iet-gtd.2017.1134Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 12, n. 4, p. 976-986, 2018.1751-8687http://hdl.handle.net/11449/16382210.1049/iet-gtd.2017.1134WOS:000424423500021WOS000424423500021.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIet Generation Transmission & Distribution0,907info:eu-repo/semantics/openAccess2024-08-06T18:56:03Zoai:repositorio.unesp.br:11449/163822Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T18:56:03Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
title Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
spellingShingle Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
Muhammad, Munir Azam
evolutionary computation
particle swarm optimisation
combinatorial mathematics
database management systems
mathematics computing
distribution networks
power engineering computing
integrated database approach
multiobjective network reconfiguration
distribution system performance enhancement
complex combinatorial process
global optimum solutions
optimal network configurations
nonradiality network solution elimination
33-bus distribution systems
118-bus distribution systems
switching actions
voltage deviation
power loss minimization
discrete evolutionary particle swarm optimisation techniques
discrete evolutionary programming
network reconfiguration optimisation
pre-determined network radiality solutions
title_short Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
title_full Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
title_fullStr Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
title_full_unstemmed Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
title_sort Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques
author Muhammad, Munir Azam
author_facet Muhammad, Munir Azam
Mokhlis, Hazlie
Naidu, Kanendra
Franco, John Fredy [UNESP]
Illias, Hazlee Azil
Wang, Li
author_role author
author2 Mokhlis, Hazlie
Naidu, Kanendra
Franco, John Fredy [UNESP]
Illias, Hazlee Azil
Wang, Li
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Univ Malaya
Univ Kuala Lumpur
Universidade Estadual Paulista (Unesp)
Natl Cheng Kung Univ
dc.contributor.author.fl_str_mv Muhammad, Munir Azam
Mokhlis, Hazlie
Naidu, Kanendra
Franco, John Fredy [UNESP]
Illias, Hazlee Azil
Wang, Li
dc.subject.por.fl_str_mv evolutionary computation
particle swarm optimisation
combinatorial mathematics
database management systems
mathematics computing
distribution networks
power engineering computing
integrated database approach
multiobjective network reconfiguration
distribution system performance enhancement
complex combinatorial process
global optimum solutions
optimal network configurations
nonradiality network solution elimination
33-bus distribution systems
118-bus distribution systems
switching actions
voltage deviation
power loss minimization
discrete evolutionary particle swarm optimisation techniques
discrete evolutionary programming
network reconfiguration optimisation
pre-determined network radiality solutions
topic evolutionary computation
particle swarm optimisation
combinatorial mathematics
database management systems
mathematics computing
distribution networks
power engineering computing
integrated database approach
multiobjective network reconfiguration
distribution system performance enhancement
complex combinatorial process
global optimum solutions
optimal network configurations
nonradiality network solution elimination
33-bus distribution systems
118-bus distribution systems
switching actions
voltage deviation
power loss minimization
discrete evolutionary particle swarm optimisation techniques
discrete evolutionary programming
network reconfiguration optimisation
pre-determined network radiality solutions
description Reconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non-radiality network solutions during the optimisation process. To address this issue, this work proposes to store pre-determined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi-objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33-bus and 118-bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-26T17:45:06Z
2018-11-26T17:45:06Z
2018-02-27
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1049/iet-gtd.2017.1134
Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 12, n. 4, p. 976-986, 2018.
1751-8687
http://hdl.handle.net/11449/163822
10.1049/iet-gtd.2017.1134
WOS:000424423500021
WOS000424423500021.pdf
url http://dx.doi.org/10.1049/iet-gtd.2017.1134
http://hdl.handle.net/11449/163822
identifier_str_mv Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 12, n. 4, p. 976-986, 2018.
1751-8687
10.1049/iet-gtd.2017.1134
WOS:000424423500021
WOS000424423500021.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Iet Generation Transmission & Distribution
0,907
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 976-986
application/pdf
dc.publisher.none.fl_str_mv Inst Engineering Technology-iet
publisher.none.fl_str_mv Inst Engineering Technology-iet
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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