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Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System
Kanata S.a, Suwarnoa, Sianipar G.H.a, Ulfa Maulidevi N.a
a Bandung Institute of Technology, School of Electrical and Informatics, Bandung, 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]© 2019 IEEE.The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).[/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]Enhanced particle swarm optimization,IEEE 57-bus,Multi objective,Multi objective particle swarm optimization,Multi-objective optimization problem,Non- dominated sorting genetic algorithms,Non-dominated Sorting,Optimal reactive power dispatch[/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,IEEE 57-bus,multi-objective,non-dominated sorting,optimal reactive power dispatch[/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]ACKNOWLEDGMENT The authors would like to say a great thank to the Ministry of Research, Technology and Higher Education of the Republic of Indonesia for funding doctoral studies 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]https://doi.org/10.1109/ICHVEPS47643.2019.9011144[/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]