A multi-parameter coordinated adaptive control strategy for energy storage VSG based on Adam-RBF neural network

YANG Sen; TIAN Guizhen; LIU Guangchen; SUN Leng

doi:10.19783/j.cnki.pspc.251342

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A multi-parameter coordinated adaptive control strategy for energy storage VSG based on Adam-RBF neural network

更新时间：2026-05-14

- A multi-parameter coordinated adaptive control strategy for energy storage VSG based on Adam-RBF neural network
- Power System Protection and Control Vol. 54, Issue 10, (2026)
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- DOI：10.19783/j.cnki.pspc.251342
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- Published：2026
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YANG Sen, TIAN Guizhen, LIU Guangchen, et al. A multi-parameter coordinated adaptive control strategy for energy storage VSG based on Adam-RBF neural network[J]. Power System Protection and Control, 2026, 54(10).
DOI：

YANG Sen, TIAN Guizhen, LIU Guangchen, et al. A multi-parameter coordinated adaptive control strategy for energy storage VSG based on Adam-RBF neural network[J]. Power System Protection and Control, 2026, 54(10). DOI： 10.19783/j.cnki.pspc.251342.

摘要

为了提升储能虚拟同步发电机 (virtual synchronous generator

VSG) 控制的频率支撑性能，提出了基于适应性矩估计算法的径向基函数 (adaptive moment estimation-radial basis function

Adam-RBF) 神经网络的储能 VSG 多参数协同自适应控制策略。首先，建立风 - 储 - 火联合系统的调频响应模型，推导计及火电和储能 VSG 控制的频率传递函数，定量分析 VSG 的转动惯量、阻尼系数和调频系数对一次调频性能的影响。然后，研究储能 VSG 多参数协调控制策略。该控制策略利用 RBF 神经网络算法来拟合转动惯量、阻尼系数以及调频系数三者之间的非线性关系。同时引入 Adam 算法，显著加快了系统一次调频的恢复速度，减少了权值的迭代次数，降低了对初始参数的依赖性。最后，通过仿真和实验结果验证了所提控制策略能够减小系统频率波动，使系统频率更快达到稳定状态。

Abstract

To enhance the frequency support performance of energy storage systems with virtual synchronous generator (VSG) control

a multi-parameter coordinated adaptive control strategy based on an adaptive moment estimation-radial basis function (Adam-RBF) neural network is proposed. First

a frequency regulation model of a wind-storage-thermal power integrated system is established. The frequency transfer function considering both thermal power units and VSG-controlled energy storage is derived

and the impacts of VSG virtual inertia

damping coefficient

and frequency regulation coefficient on primary frequency regulation performance are quantitatively analyzed. Then

a multi-parameter coordination strategy for energy storage VSG is developed. The strategy employs an RBF neural network to approximate the nonlinear relationships among virtual inertia

damping coefficient

and frequency regulation coefficient. Meanwhile

the Adam algorithm is incorporated to significantly accelerate frequency recovery in primary regulation

reduce the number of weight iterations

and decrease dependence on initial parameter settings. Finally

simulation and experimental results demonstrate that the proposed control strategy effectively suppresses system frequency fluctuations and facilitates faster frequency stabilization.

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Keywords

references

YANG Zhe, WANG Hongyi, LIAO Wenlong, et al. Protection challenges and solutions for AC systems with renewable energy sources: a review[J]. Protection and Control of Modern Power Systems, 2025, 10(1): 18-39.

YAO Fengjun, ZHAO Jinbin, LI Xiangjun, et al. RBF neural network based virtual synchronous generator control with improved frequency stability[J]. IEEE Transactions on Industrial Informatics, 2021, 17(6): 4014-4024.

KING M A, JIMMY L. Adam: a method for stochastic optimization[C] // 3rd International Conference on Learning Representations, May 7-9, 2015, San Diego, USA: 1-13.

REDDI S J, KALE S, KUMAR S. On the convergence of Adam and beyond[C] // Proceedings of the International Conference on Learning Representations, April 30-May 3, 2019, Vancouver Canada: 1-23.

SHI Qingxin, LI Fangxing, CUI Hantao. Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6355-6367.

全国电力储能标准化技术委员会(SAC/TC 550). 电化学储能系统储能变流器技术要求: GB/T 34120—2023[S]. 北京: 中国标准出版社, 2023.

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