dc.contributor | Univ Malaya | |
dc.contributor | Univ Kuala Lumpur | |
dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.contributor | Natl Cheng Kung Univ | |
dc.date.accessioned | 2018-11-26T17:45:06Z | |
dc.date.available | 2018-11-26T17:45:06Z | |
dc.date.created | 2018-11-26T17:45:06Z | |
dc.date.issued | 2018-02-27 | |
dc.identifier | Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 12, n. 4, p. 976-986, 2018. | |
dc.identifier | 1751-8687 | |
dc.identifier | http://hdl.handle.net/11449/163822 | |
dc.identifier | 10.1049/iet-gtd.2017.1134 | |
dc.identifier | WOS:000424423500021 | |
dc.identifier | WOS000424423500021.pdf | |
dc.description.abstract | 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. | |
dc.language | eng | |
dc.publisher | Inst Engineering Technology-iet | |
dc.relation | Iet Generation Transmission & Distribution | |
dc.relation | 0,907 | |
dc.rights | Acesso aberto | |
dc.source | Web of Science | |
dc.subject | evolutionary computation | |
dc.subject | particle swarm optimisation | |
dc.subject | combinatorial mathematics | |
dc.subject | database management systems | |
dc.subject | mathematics computing | |
dc.subject | distribution networks | |
dc.subject | power engineering computing | |
dc.subject | integrated database approach | |
dc.subject | multiobjective network reconfiguration | |
dc.subject | distribution system performance enhancement | |
dc.subject | complex combinatorial process | |
dc.subject | global optimum solutions | |
dc.subject | optimal network configurations | |
dc.subject | nonradiality network solution elimination | |
dc.subject | 33-bus distribution systems | |
dc.subject | 118-bus distribution systems | |
dc.subject | switching actions | |
dc.subject | voltage deviation | |
dc.subject | power loss minimization | |
dc.subject | discrete evolutionary particle swarm optimisation techniques | |
dc.subject | discrete evolutionary programming | |
dc.subject | network reconfiguration optimisation | |
dc.subject | pre-determined network radiality solutions | |
dc.title | Integrated database approach in multi-objective network reconfiguration for distribution system using discrete optimisation techniques | |
dc.type | Artículos de revistas | |