Parameter Identification of PMSG-Based Wind Turbine Based on Sensitivity Analysis and Improved Gray Wolf Optimization

Bingjie Zhai, Kaijian Ou, Yuhong Wang, Tian Cao, Huaqing Dai and Zongsheng Zheng ()
Additional contact information
Bingjie Zhai: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Kaijian Ou: Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System, Guangzhou 510663, China
Yuhong Wang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Tian Cao: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Huaqing Dai: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Zongsheng Zheng: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2024, vol. 17, issue 17, 1-15

Abstract: With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions.

Keywords: improved gray wolf optimization; parameter identification; sensitivity analysis; wind turbine (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/17/4361/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/17/4361/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:17:p:4361-:d:1468615

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().