The proposed control scheme in this paper is based on the design of an optimal load distribution of reactive power for High-Voltage Direct-Current systems (HVDCs). The purpose is to monitor and implement appropriate operations to create a balance in the power and eliminate the voltage or current that has exceeded the permissible limit. Two-terminal HVDC transmission lines are of the most important elements of the power system. The optimization of reactive power distribution for power systems is defined based on the minimization of power loss under equal and unequal constraints. The problem of optimizing the reactive power distribution in integrated AC-DC power systems in the form of HVDC lines is generalized with regard to the control specifications for power transmission. In this paper, the problem is solved using Genetic Optimization Algorithm (GA). The validation, efficiency and the effectiveness of the proposed method are measured by comparing the results of the GA and those obtained by previous researches. Results indicate the effectiveness of the proposed method in the optimization of power distribution and the design of HVDC lines.
Mohammadi, A. (2022). The Optimal Design of HVDC Transmission Lines for the Improvement of Reactive Power Distribution Using Genetic Algorithm. Engineering Management and Soft Computing, 8(1), 197-213. doi: 10.22091/jemsc.2019.1326
MLA
Ali Mohammadi. "The Optimal Design of HVDC Transmission Lines for the Improvement of Reactive Power Distribution Using Genetic Algorithm". Engineering Management and Soft Computing, 8, 1, 2022, 197-213. doi: 10.22091/jemsc.2019.1326
HARVARD
Mohammadi, A. (2022). 'The Optimal Design of HVDC Transmission Lines for the Improvement of Reactive Power Distribution Using Genetic Algorithm', Engineering Management and Soft Computing, 8(1), pp. 197-213. doi: 10.22091/jemsc.2019.1326
VANCOUVER
Mohammadi, A. The Optimal Design of HVDC Transmission Lines for the Improvement of Reactive Power Distribution Using Genetic Algorithm. Engineering Management and Soft Computing, 2022; 8(1): 197-213. doi: 10.22091/jemsc.2019.1326
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