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Hybrid Time Varying Particle Swarm Optimization and Genetic Algorithm to Solve Optimal Reactive Power Dispatch Problem
Kanata S.a, Suwarnoa, Sianipar G.H.a, Maulidevi N.U.a
a School of Electrical and Informatics, Bandung Institute of Technology, 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.Optimal reactive power dispatch (ORPD) is the determination of the control variable (discrete continuous combination) which affects the power supply / reactive power absorption. The aim is to reduce real power losses without exceeding the limits of their capabilities in small-scale power systems. Hybrid time varying nonlinear particle swarm optimization and genetic algorithm (HTVNLPSOGA) is applied to solve ORPD problems. The parameters of inertia weight and acceleration factors are made to change nonlinearly so that the speed of particles in the process of exploration and exploitation is more adaptive. Several comparisons with previous studies to see the capabilities of the proposed algorithm. The IEEE 14-bus system has been used to examine and test the methods presented. The simulation results show that the HTVNLPSOGA method when finding the optimal solution is capable of convergence with the number of search iterations below 10 iterations. In addition, this method is able to minimize real power losses of 12.3569 MW. The proposed method is able to reduce power losses better than previous studies on small-scale IEEE 14-bus power systems.[/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]Acceleration factors,Control variable,Exploration and exploitation,Inertia weight,Optimal reactive power dispatch,Optimal solutions,Power absorption,Real power loss[/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,particle swarm optimization,reactif power dispatch,time varying[/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 thank the Republic of Indonesia Ministry of Research, Technology and Higher Education for funding studies on doctoral programs 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/ICEEI47359.2019.8988864[/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]