LAPSE:2023.33050
Published Article
LAPSE:2023.33050
Improved Virtual Inertia of PMSG-Based Wind Turbines Based on Multi-Objective Model-Predictive Control
Shiyao Qin, Yuyang Chang, Zhen Xie, Shaolin Li
April 20, 2023
Abstract
In the case of a high penetration rate of wind energy conversion systems, the conventional virtual inertia control of permanent magnet synchronous generators (PMSG) has an insufficient support capability for system frequency, leading to an unstable system frequency and a slower response. Considering the finite control set model predictive control has multi-objective regulation capabilities and efficient tracking capabilities, and an improved multi-objective model-predictive control is proposed in this paper for PMSG-based wind turbines with virtual inertia based on its mathematical model. With the prediction model, the optimal control of the current and the frequency of the PMSG-based wind turbines can be obtained. Since the shaft torque changes rapidly under high virtual inertia, shaft oscillation may occur under this scenario. To address this problem, the electromagnetic torque is set as an additional optimization objective, which effectively suppresses the oscillation. Furthermore, based on accurate short-term wind speed forecasting, a dynamic weight coefficient strategy is proposed, which can reasonably distribute the weight coefficients according to the working conditions. Finally, simulations are carried out on a 2 MW PMSG-based wind turbine platform, and the effectiveness of the proposed control strategies is verified.
Keywords
dynamic weight coefficient, multi-objective model predictive control, permanent magnet synchronous generators, virtual inertia
Suggested Citation
Qin S, Chang Y, Xie Z, Li S. Improved Virtual Inertia of PMSG-Based Wind Turbines Based on Multi-Objective Model-Predictive Control. (2023). LAPSE:2023.33050
Author Affiliations
Qin S: School of Electrical Engineering, Southeast University, Nanjing 210096, China; China Electric Power Research Institute, Beijing 100192, China
Chang Y: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Xie Z: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Li S: China Electric Power Research Institute, Beijing 100192, China
Journal Name
Energies
Volume
14
Issue
12
First Page
3612
Year
2021
Publication Date
2021-06-17
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14123612, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.33050
This Record
External Link

https://doi.org/10.3390/en14123612
Publisher Version
Download
Files
Apr 20, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
217
Version History
[v1] (Original Submission)
Apr 20, 2023
 
Verified by curator on
Apr 20, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.33050
 
Record Owner
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version