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Multi-objective optimal reactive power dispatch using hybrid time varying particle swarm optimization and genetic algorithm
Kanata S.a, Suwarnoa, Sianipar G.H.a, Maulidevi N.U.a
a School of Electrical and Informatis, Bandung Institute of Technology, Bandung, West Java, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2005 – ongoing JATIT & LLS.The hybrid time varying particle swarm optimization and genetic algorithm method (TVPSOGA) was introduced to solve multi-objective reactive power dispatch (MORPD) problems. MORPD as a non-linear multi-objective optimization problem that has the characteristics of non-convex, multi-constraint, and multi-variable which consists of a mixture of solutions that have discrete and continuous variables. The feasibility of the proposed method was tested on the IEEE 57-bus and IEEE 118-bus power systems. Comparison of simulation results shows the efficacy of the proposed optimization method compared to methods such as multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO) for the case of IEEE 57-bus power system. As for the case of the IEEE 118-bus power system, this method shows better efficacy compared to biogeography based optimization (BBO), the particle swarm optimization method with an aging leader and challengers (ALC-PSO), the enhanced gaussian bare-bones water cycle algorithm (NGBWCA) and PSO with a gravitational search algorithm (PSOGSA).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Genetic algorithm,Multi-objective reactive power dispatch,The real power losses,The total voltage deviation,Time varying particle swarm optimization[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors would like to say a great thank to The Ministry of Research, Technology and Higher Education of the Republic Indonesia for funding doctoral studies and researches at Bandung Institute of Technology.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]