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

Xuhong Yang, Hui Li (), Wei Jia, Zhongxin Liu, Yu Pan and Fengwei Qian
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Xuhong Yang: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Hui Li: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Wei Jia: Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China
Zhongxin Liu: Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China
Yu Pan: Shanghai Institute of Space Power-Sources/State Key Laboratory of Space Power-Sources Technology, Shanghai 200245, China
Fengwei Qian: Shanghai Solar Energy Engineering Technology Research Center, Shanghai 200245, China

Energies, 2022, vol. 15, issue 22, 1-13

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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