A PINN-VSG frequency control method for microgrids with high penetration of renewable energy

SHEN Guangyu; DENG Wei; XIE Zijian; CUI Chenggang; XIAO Hao; YANG Yanhong; PEI Wei

doi:10.19783/j.cnki.pspc.260175

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A PINN-VSG frequency control method for microgrids with high penetration of renewable energy

更新时间：2026-09-28

- A PINN-VSG frequency control method for microgrids with high penetration of renewable energy
- Power System Protection and Control Vol. 54, Issue 19, (2026)
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- DOI：10.19783/j.cnki.pspc.260175
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- Published：2026
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SHEN Guangyu, DENG Wei, XIE Zijian, et al. A PINN-VSG frequency control method for microgrids with high penetration of renewable energy[J]. Power System Protection and Control, 2026, 54(19).
DOI：

SHEN Guangyu, DENG Wei, XIE Zijian, et al. A PINN-VSG frequency control method for microgrids with high penetration of renewable energy[J]. Power System Protection and Control, 2026, 54(19). DOI： 10.19783/j.cnki.pspc.260175.

摘要

虚拟同步发电机可为高比例可再生能源微网提供惯性和阻尼支撑，但传统固定参数控制在复杂工况下适应性不足。针对该问题，提出一种基于物理信息神经网络的控制参数自适应调节方法。首先，分析多台虚拟同步发电机的交互机理，选取连续时刻的角频率、角频率变化率和电磁功率构建时序输入，实现转动惯量、阻尼系数和二次调频系数的在线调整。接着，将转子运动方程嵌入网络训练，构建由数据损失和物理损失组成的联合损失函数，以增强网络输出的物理一致性。最后，通过仿真和硬件在环实验进行验证。结果表明，所提方法能够在不同负载扰动下较快且平稳地恢复系统频率，有效抑制超调和振荡，具有较好的适应性和泛化能力。

Abstract

Virtual synchronous generators can provide inertia and damping support for microgrids with high renewable energy penetration. However

traditional fixed-parameter control strategies have limited adaptability under complex operating conditions. To address this issue

a parameter adaptive control method based on a physics-informed neural network (PINN) is proposed. First

the interaction mechanism among multiple virtual synchronous generators is analyzed. Angular frequency

the rate of change of angular frequency

and electromagnetic power at consecutive time instants are selected to construct time-series inputs

thereby achieving the online adjustments of the moment of inertia

damping coefficients

and secondary frequency regulation coefficients. Subsequently

the rotor motion equation is embedded into network training

and a joint loss function comprising a data-loss term and a physics-loss term is constructed to enhance the physical consistency of the network outputs. Finally

simulations and hardware-in-the-loop experiments are conducted to validate the proposed method. The results show that the proposed method achieves fast and smooth frequency recovery under different load disturbances and effectively suppresses overshoots and oscillations

demonstrating good adaptability and generalization capability.

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references

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