郑州大学学报（工学版）2024，Vol.45Issue(3)：127-133,7.DOI:10.13705/j.issn.1671-6833.2024.03.005
基于VSG下垂优化控制的新能源电力系统惯性提升
Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control
摘要
Abstract
Aiming at the problems of poor dynamic performance of traditional VSG technology and difficulty to de-termine 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 characteris-tics 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 es-tablished for analysis learning and network training,and the weight was adjusted to change the VSG moment of in-ertia 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 algo-rithm 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 pro-posed adaptive parameter droop control based on neural network optimization reduced the maximum frequency varia-tion by 26.7%,and the frequency stabilization time by 0.25 s.The strategy was effective.关键词
新能源电力系统/VSG/下垂控制/神经网络/小信号分析模型Key words
new energy power system/VSG/droop control/neural network/small signal analysis model分类
信息技术与安全科学引用本文复制引用
王明东,杨岙迪,李龙好,李忠文..基于VSG下垂优化控制的新能源电力系统惯性提升[J].郑州大学学报（工学版）,2024,45(3):127-133,7.基金项目
国家自然科学基金资助项目(62273312) （62273312）
河南省自然科学基金资助项目(212300410406) （212300410406）
