Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control

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Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control

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[1]WANG Mingdong,YANG Aodi,LI Longhao,et al.Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):127-133.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 005]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 127-133 Column: Public date: 2024-04-20

Title:: Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control

Author(s):: WANG Mingdong¹; YANG Aodi¹; LI Longhao²; LI Zhongwen¹; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. XJ Electric Co. ,Ltd. , Xuchang 461000,China

Keywords:: new energy power system; VSG; droop control; neural network; small signal analysis model

CLC:: TM712TP13

DOI:: 10. 13705/ j. issn. 1671-6833. 2024. 03. 005

Abstract:: Aiming at the problems of poor dynamic performance of traditional VSG technology and difficulty to determine the optimal values of important parameters J and D, a VSG control and parameter optimization strategy based on droop control and neural network prediction was proposed to realize dynamic adjustment of key parameters J and D in VSG technology. The proposed strategy applied the active power-frequency droop control to the control algorithm of VSG. Then, simulated the rotor motion equation and the voltage and reactive power control characteristics of synchronous generator, the small signal analysis model of VSG was established, and the initial setting of key parameters rotational inertia and damping coefficient were completed. Finally, an artificial neural network was established for analysis learning and network training, and the weight was adjusted to change the VSG moment of inertia and damping coefficient. The error between the output and the input was compared by the error function, and the parameter reached the expected value after multiple learning and training. The neural network optimization algorithm was combined with the droop control strategy to optimize the VSG control strategy. Traditional VSG control, constant parameter droop control and adaptive parameter droop control based on neural network optimization were used to simulate a numerical example, and the results showed that, compared with traditional VSG control, the proposed adaptive parameter droop control based on neural network optimization reduced the maximum frequency variation by 26.7%, and the frequency stabilization time by 0.25 s. The strategy was effective.

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Last Update: 2024-04-29