Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability

Abstract

With the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is proposed in this paper. In this method, virtual synchronous generator (VSG) control strategy is introduced into the model predictive control (MPC), so that the reference value of the inner loop current can vary with the grid voltage and frequency. Using the radial basis function (RBF) neural network to adjust the VSG virtual inertia online can solve the large fluctuation of frequency and power caused by excessive load fluctuation. The simulation model was built based on MATLAB and compared and analyzed with the MPC control method. The simulation results show that: when the output power of the inverter changes, the model predictive control of the adaptive virtual synchronous generator can increase the inertia and stability of the power grid; by adjusting the moment of inertia, the system damping ratio is improved to effectively suppress the transient process overshoot and oscillation in medium power.

Keywords

• distributed power generation
• model predictive control
• radial basis function neural network
• virtual synchronous generator