基于RBF的VSG转动惯量和阻尼系数自适应控制策略

doi:10.12204/j.issn.1000-7229.2022.09.014

电力建设 ›› 2022, Vol. 43 ›› Issue (9): 132-139.doi: 10.12204/j.issn.1000-7229.2022.09.014

基于RBF的VSG转动惯量和阻尼系数自适应控制策略

高子轩¹(), 赵晋斌²(), 杨旭红¹(), 姚凤军³(), 方剑峰¹()

1.上海电力大学自动化工程学院,上海市 200090
2.上海电力大学电气工程学院,上海市 200090
3.国网浙江省电力有限公司绍兴供电公司,浙江省绍兴市 312099

收稿日期:2022-01-18 出版日期:2022-09-01 发布日期:2022-08-31
通讯作者: 赵晋斌 E-mail:tokui0114@163.com;zhaojinbin@shiep.edu.cn;yangxuhon.sh@163.com;yaofengjunde@163.com;664879930@qq.com
作者简介:高子轩(1998),男,硕士研究生,主要研究方向为微网控制技术,E-mail: tokui0114@163.com;
杨旭红(1969),女,博士,教授,研究方向为智能电网控制技术、新能源发电及储能技术,E-mail: yangxuhon.sh@163.com;
姚凤军(1994),男,硕士,研究方向为微电网控制技术,E-mail: yaofengjunde@163.com;
方剑峰(1996),男,硕士研究生,研究方向为智能电网控制技术,E-mail: 664879930@qq.com。
基金资助:
国家自然科学基金项目(52177184)

RBF-Based Adaptive Control Strategy of Rotational Inertia and Damping Coefficient for VSG

GAO Zixuan¹(), ZHAO Jinbin²(), YANG Xuhong¹(), YAO Fengjun³(), FANG Jianfeng¹()

1. School of Automatic Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
3. Shaoxing Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd.,Shaoxing 312099, Zhejiang Province,China

Received:2022-01-18 Online:2022-09-01 Published:2022-08-31
Contact: ZHAO Jinbin E-mail:tokui0114@163.com;zhaojinbin@shiep.edu.cn;yangxuhon.sh@163.com;yaofengjunde@163.com;664879930@qq.com
Supported by:
National Natural Science Foundation of China(52177184)

摘要/Abstract

摘要：

虚拟同步发电机(virtual synchronous generator,VSG)通过控制策略使电力电子变换器具备同步发电机的转动惯量和阻尼系数,但这两个参数在调节过程中与频率为非线性关系,传统方法都将其作为线性关系来设计控制策略。该类控制策略只能粗略调整两个参数,且调节频率过高。针对这一问题,文章提出了一种基于径向基函数(radial basis function, RBF)神经网络的VSG转动惯量和阻尼系数协同控制策略,通过人工智能算法改进传统控制策略。从VSG数学模型、输出特性和小信号模型三个角度对转动惯量和阻尼系数控制方法进行了分析,给出了对应参数的取值范围;并针对VSG特有的非线性关系建立了双输入双输出的RBF神经网络控制策略;最后,通过MATLAB/Simulink仿真比较传统策略与文章提出的控制策略的瞬态响应,验证所提控制策略的有效性。

关键词: 虚拟同步发电机, 频率响应, 转动惯量, 阻尼系数, 神经网络, 自适应控制策略

Abstract:

The virtual synchronous generator (VSG) control strategy enables the power electronic converter to have the moment of rotational inertia and damping coefficient of the synchronous generator, but the relationship between the two parameters and the frequency in the regulation process is a nonlinear function, which is regarded as a linear relationship in the traditional methods. The control strategy can only roughly adjust two parameters and the adjustment frequency is too high. Therefore, this paper proposes an adaptive control strategy of inertia and damping for VSG, which is based on Radial Basis Function (RBF), using the artificial intelligence algorithm to improve the control strategy. Firstly, this paper analyzes the control methods of moment of inertia and damping coefficient from the perspectives of VSG mathematical model, output characteristics and small signal model, and gives the value ranges of corresponding parameters. Secondly, according to the unique nonlinear relationship of VSG, a double-input and double-output RBF neural network control strategy is established. Finally, the transient response of the traditional control strategy and the control strategy proposed in this paper are compared by Matlab / Simulink simulation to verify the effectiveness of the proposed control strategy.

Key words: virtual synchronous generator, frequency response, rotational inertia, damping coefficient, neural network, adaptive control strategy

中图分类号:

TM711

高子轩, 赵晋斌, 杨旭红, 姚凤军, 方剑峰. 基于RBF的VSG转动惯量和阻尼系数自适应控制策略[J]. 电力建设, 2022, 43(9): 132-139.

GAO Zixuan, ZHAO Jinbin, YANG Xuhong, YAO Fengjun, FANG Jianfeng. RBF-Based Adaptive Control Strategy of Rotational Inertia and Damping Coefficient for VSG[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 132-139.

图/表 14

图1 基于RBF神经网络的VSG控制原理图

Fig.1 VSG control diagram based on RBF neural network

图2 VSG有功功率和无功功率控制回路

Fig.2 Active power and reactive power control loops of VSG

图3 VSG并网等效电路

Fig.3 Equivalent circuit of grid-connected VSG

图4 不同转动惯量和阻尼系数下的输出有功动态响应

Fig.4 Dynamic responses of active power output under different rotational inertias and damping cofficients

图5 不同转动惯量和阻尼系数下系统角频率的评价指标变化

Fig.5 Variation of evaluation index of system angular frequency under different rotational inertia and damping coefficient

图6 针对VSG控制的RBF神经网络结构图

Fig.6 RBF neural network structure diagram for VSG control

图7 RBF-JD控制流程图

Fig.7 Flowchart of RBF-JD control

表1 仿真模型主要参数

Table 1 Main parameters of the simulation model

表2 自适应线性控制策略主要参数

Table 2 Main parameters of the adaptive linear control strategy

表3 RBF-J算法主要参数

Table 3 Main parameters of RBF-J algorithm

图8 不同控制策略下的有功功率对比

Fig.8 Comparison of active power under different control strategies

表4 两个场景下不同控制策略的分析指标

Table 4 Analysis indicators of different control strategies in two scenarios

图9 不同控制策略下的角频率对比

Fig.9 Comparison of angular frequency under different control strategies

图10 不同控制策略下的转动惯量和阻尼系数变化对比

Fig.10 Comparison of rotational inertia and damping coefficient under different control strategies

参考文献 18

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基于RBF的VSG转动惯量和阻尼系数自适应控制策略

RBF-Based Adaptive Control Strategy of Rotational Inertia and Damping Coefficient for VSG

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