基于径向基神经网络的参数协同自适应VSG控制策略

袁涛; 杜振东

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DOI:10.3969/j.issn.2096-8299.2023.06.004

上海电力大学学报:2023,39(6):536-542

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基于径向基神经网络的参数协同自适应VSG控制策略

袁涛¹, 杜振东^1,2

(1.上海电力大学;2.浙江华云电力工程设计咨询有限公司)

VSG Parameter Cooperative Control Strategy Based on Radial Basis Function Neural Network

YUAN Tao¹, DU Zhendong^1,2

(1.Shanghai University of Electric Power, Shanghai 200090, China;2.Zhejiang Huayun Electric Power Engineering Design Consulting Co., Ltd., Hangzhou, Zhejiang 310014, China)

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投稿时间：2023-07-03

中文摘要: 采用虚拟同步发电机（VSG）控制策略的并网逆变器可为分布式能源提供必要的惯性阻尼特性，以支撑电网频率。VSG的动态响应特性与惯量和阻尼的设定密切相关。首先，建立VSG控制模型，分析惯量和阻尼对系统动态响应指标的影响，并给出了VSG参数的计算方法。其次，结合径向基神经网络提出了一种参数协同自适应VSG控制策略，自适应调节惯量和阻尼参数来应对系统功率变化、负荷扰动及频率偏移。最后，通过MATLAB/Simulink仿真，验证所提策略的有效性和优越性。

中文关键词: 虚拟同步发电机转动惯量阻尼系数神经网络

Abstract:Grid-connected inverters with a virtual synchronous generator (VSG) control strategy generate the inertial damping characteristics which is necessary for distributed energy to support the grid frequency.The dynamic response characteristics of VSG are closely related to the setting of inertia and damping.First, the VSG control model is established to analyze the influence of inertia and damping on the dynamic response index of the system, and the calculation method of VSG parameters is given.Combined with radial basis neural network, a parameter collaborative adaptive VSG control strategy is proposed.The inertia and damping parameters can be adjusted adaptively to cope with the power change, load disturbance and frequency offset.Finally, the effectiveness of the proposed strategy is verified by MATLAB/Simulink simulation.

keywords: virtual synchronous generator virtual inertia damping coefficient neural network

文章编号：20236004 中图分类号：TM712 文献标志码：

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作者	单位	E-mail
袁涛	上海电力大学	ytyuan_tao@163.com
杜振东	上海电力大学浙江华云电力工程设计咨询有限公司

Author Name	Affiliation	E-mail
YUAN Tao	Shanghai University of Electric Power, Shanghai 200090, China	ytyuan_tao@163.com
DU Zhendong	Shanghai University of Electric Power, Shanghai 200090, China Zhejiang Huayun Electric Power Engineering Design Consulting Co., Ltd., Hangzhou, Zhejiang 310014, China

引用文本：
袁涛,杜振东.基于径向基神经网络的参数协同自适应VSG控制策略[J].上海电力大学学报,2023,39(6):536-542.
YUAN Tao,DU Zhendong.VSG Parameter Cooperative Control Strategy Based on Radial Basis Function Neural Network[J].Journal of Shanghai University of Electric Power,2023,39(6):536-542.