【目的】虚拟同步控制的引入使得双馈风机(DFIG)与线路补偿装置间的次同步振荡更为复杂。针对此问题,提出了一种基于模型预测控制(MPC)的虚拟同步双馈风机并网系统次同步振荡抑制策略。【方法】首先，从虚拟同步发电机(VSG)阻抗模型中得到虚拟惯量和阻尼二阶表达式，从阻抗特性的角度探究参数变化对并网系统次同步振荡的影响；其次，利用三相两电平含开关函数的电压方程推导换流器输出有功和无功功率的预测函数，建立基于MPC的直接功率预测内环控制，实现功率波动最小的最优控制；最后，利用扫频法对MPC_VSG控制策略进行分析。【结果】通过基于RT-LAB的硬件在环测试对所提MPC_VSG控制策略进行验证，结果表明在不同串补度、不同风速下，MPC_VSG控制策略均能够在0.5 s内抑制次同步振荡,且具有良好的鲁棒性。【结论】本文设计的MPC_VSG控制策略，通过以最小功率波动为目标选取最优开关状态，实现了对次同步振荡的有效抑制。

[Objective] The introduction of virtual synchronous control complicates the sub-synchronous oscillation between double-fed induction generator (DFIG) and line compensation devices. To address this issue, a sub-synchronous oscillation suppression strategy based on model predictive control (MPC) for a virtual synchronous DFIG grid-connected system is proposed. [Method] First, the second-order expressions of virtual inertia and damping were derived from the virtual synchronous generator (VSG) impedance model, and the impact of parameter variations on sub-synchronous oscillations in the grid-connected system was investigated from the perspective of impedance characteristics. Second, using a three-phase two-level voltage equation with switching functions, the prediction functions of active and reactive power output from the converter were derived. A direct power predictive inner-loop control based on MPC was established to achieve optimal control with minimum power fluctuation. Finally, the MPC_VSG control strategy was analyzed using the frequency sweep method. [Results] The proposed MPC_VSG control strategy was verified by hardware-in-loop experiment based on RT-LAB .The results demonstrated that, under different series compensation levels and wind speeds, the MPC_VSG control strategy can suppress sub-synchronous oscillations within 0.5 seconds, and exhibits strong robustness. [Conclusion] The MPC_VSG control strategy designed in this paper selects the optimal switching state by targeting minimal power fluctuations, achieving the subsynchronous oscillation effective suppression.

国家重点研发计划项目(2022YFB2703500);云南省基础研究计划资助项目(202301AS070055)