Abstract：With the proportion of renewable energy sources increasing in the power system, and the penetration rate of power electronic devices continuing to rise, the inertia support and frequency regulation ability of the power system are greatly weakened. To compensate for the lack of inertia, virtual synchronous generator (VSG) control is proposed. In the traditional VSG control method, the inertia and damping coefficient usually remain constant, and the secondary frequency regulation ability is insufficient, which will lead to obvious frequency deviation when dealing with large load changes. A joint control strategy of inertia and damping coefficient self-adaptive control and variable weight model predictive control (MPC) is proposed to solve this problem. Firstly, based on the VSG small-signal model, the influence of inertia and damping coefficient on the system's output characteristics is analyzed, and an inertia and damping coefficient self-adaptive control strategy is designed to adjust the inertia and damping coefficient of VSG in real-time. The proposed control introduces $s(x)$ function, which can limit the range of inertia and damping, and avoid the parameters changing too fast or too big. Then, an MPC control method with a variable weight coefficient is proposed. Based on traditional MPC control, the weight function is constructed with angular frequency deviation and change rate as inputs and the weight coefficient of MPC objective function is optimized online. Utilizing the rolling optimization feature of MPC, the active power reference value of VSG is corrected. Finally, the effectiveness of the proposed control strategy is verified by simulation and hardware-in-the-loop experiments. The results show that the joint control strategy can quickly respond to load changes, suppress frequency fluctuation and reduce frequency deviation. According to the simulation analysis, the control proposed in this paper has the best suppression effect on frequency coefficient, and due to the adaptive adjustment of MPC weight coefficient, the inertia and damping coefficient can recover to stable values faster, which shows that better frequency regulation effect can be obtained through smaller parameters change. The following conclusions can be drawn from the analysis: (1) Through the combined control of the inertia and damping coefficient self-adaptive control and variable weight MPC control, the frequency regulation ability of VSG can be further strengthened. Compared with traditional MPC control, the inertia and damping coefficient in the state equation can be modified. Compared with the simple inertia and damping coefficient self-adaptive control, the active power reference value is modified in real-time. Multiple controls working together improves the frequency response characteristics of the system. (2) A self-adaptive control strategy of inertia and damping coefficient is proposed, which is an improvement on the traditional self-adaptive control. The introduction of $s(x)$ function can limit the range of inertia and damping coefficient, and at the same time, the adjustment of $k$ can control the speed of parameters change and avoid the parameters changing too fast or too big. (3) Adjusting the objective function's weight matrix at different stages of frequency change can speed up the frequency recovery. By establishing the relationship between the weight matrix and angular frequency deviation and change rate, variable weight MPC control is realized. (4) Simulation and hardware-in-the-loop experiments show that the proposed control strategy allows VSG to make rapid adjustments according to system conditions and load changes, and limits the frequency fluctuation within a safe range, which significantly improves the frequency stability of the system.
郭潇镁, 李永刚, 周一辰. 基于变权重自适应模型预测控制的虚拟同步发电机调频策略[J]. 电工技术学报, 2025, 40(21): 6956-6969.
Guo Xiaomei, Li Yonggang, Zhou Yichen. Virtual Synchronous Generator Frequency Regulation Control Strategy Based on Variable Weight Adaptive Model Predictive Control. Transactions of China Electrotechnical Society, 2025, 40(21): 6956-6969.
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