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Article type: Research Article
Authors: Yin, Jintiana; b | Liu, Lia; b; * | Peng, Zhihuaa; b | Chen, Rihenga; b
Affiliations: [a] School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan, China | [b] Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, Hunan, China
Correspondence: [*] Corresponding author: Li Liu, School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China. E-mail: liuli470@163.com.
Abstract: With the development of science and technology, new energy technology has increasingly become a hot spot of academic research. Today, solar, nuclear, tidal and wind power generation is gradually replacing thermal power generation. Aiming at the configuration problem of traditional wind power generation, this research will optimize the control of wind turbine generator sets, so as to realize the configuration optimization of the entire distribution network. In this study, a MOG (MOG) algorithm control optimization method was introduced. First, each scenario is constructed, and components such as centralized wind power plants are configured and controlled through the internal power flow program; then, decision variables are defined, and three objective functions with trade-off relationships are constructed. Finally, the MOG algorithm is used for iterative solution, and the point with the lowest violation cost is selected as the optimal solution. Simulation experiments including three different configuration scenarios were conducted in MATLAB environment for comparing the influence of different connection modes on distribution network. According to experimental results, the effectiveness of the proposed method is verified and it is proved that the proposed method can be applied to complex distribution networks.
Keywords: MOG algorithm, distribution network, CWT, objective function, optimum solution
DOI: 10.3233/JCM226582
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 2, pp. 1053-1068, 2023
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