Parameter adaptive model predictive control strategy of NPC three-level virtual synchronous generator

Frontiers in Energy Research

Energy Research

Jiane Zhao¹  Nan Jin²  Xiaoliang Yang²  Yihao Li²  Rui Wang²  Yuyue Cui² 

[1] College of Electrical and Electronic Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China;College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China;Henan Key Lab of Information Based Electrical Appliances, Zhengzhou, China;

关键词: virtual synchronous generator (VSG);    neutral point clamped (NPC);    finite control set model predictive control (FCS-MPC);    parameter adaptive;    frequency regulation;   

DOI  :  10.3389/fenrg.2023.1236646

 received in 2023-06-08, accepted in 2023-08-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

The Virtual Synchronous Generator (VSG) emulates the characteristics of a synchronous generator to provide inertia and damping for renewable energy systems. In the case of using the NPC three-level converter structure, traditional control methods require complex dual-loop control and internal PI parameter tuning. Furthermore, although fixed-parameter VSG control can provide inertia and damping when a significant power load is switched in an islanded microgrid, it cannot guarantee frequency regulation performance. To address these issues, this paper proposes an NPC three-level VSG parameter adaptive finite control set model predictive control strategy. This method eliminates the need for dual-loop control and PI parameter tuning. By incorporating angular velocity deviation and its rate of change into adaptive adjustment, a Tracking-Differentiator (TD) is designed to calculate the rate of change of angular velocity. This approach avoids frequent fluctuation of adaptive parameters during load power switching and improves the frequency stability of the microgrid. The effectiveness of the proposed strategy is validated through simulation and experimental verification.

【 授权许可】

Unknown   
Copyright © 2023 Yang, Wang, Jin, Zhao, Cui and Li.