Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping

Jiachen Liu; Zhongguan Wang; Xiaodi Zang; Xialin Li; Li Guo; Qinglin Meng; Chengshan Wang

doi:10.17775/CSEEJPES.2023.02430

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Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping

Regular Papers | 更新时间：2026-02-06

- Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping
- Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第3期页码：1018-1029
- 作者机构：
  
  School of Electrical and Information Engineering, Tianjin University,Tianjin,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.02430
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Jiachen Liu, Zhongguan Wang, Xiaodi Zang, 等. Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(3):1018-1029.

Jiachen Liu, Zhongguan Wang, Xiaodi Zang, et al. Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping[J]. CSEE Journal of Power and Energy Systems, 2025, 11(3): 1018-1029.
Jiachen Liu, Zhongguan Wang, Xiaodi Zang, 等. Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(3):1018-1029. DOI： 10.17775/CSEEJPES.2023.02430.

Jiachen Liu, Zhongguan Wang, Xiaodi Zang, et al. Data-Driven Dynamic Assessment of Wind Farm Frequency Characteristics Based on State Space Mapping[J]. CSEE Journal of Power and Energy Systems, 2025, 11(3): 1018-1029. DOI： 10.17775/CSEEJPES.2023.02430.

摘要

Abstract

With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces

power systems are suffering from increasingly serious frequency stability risks. Due to the large number of WTs and their complex dynamic characteristics

operators encounter challenges in coordinating single WTs to provide frequency support directly

and it is necessary to assess the primacy frequency regulation (PFR) capability of wind farms. To cope with the problems of solving complexity and incomplete parameters

a data-driven state space mappingbased linear model for wind farms is developed in this paper to assess the maximum PFR capability. With Koopman operator theory (KOT)

the proposed method transforms wind farm PFR nonlinear dynamics into a linear lift-dimension algebraic model

which can assess the maximum PFR capability of wind farms based on historical data in real-time. The simulation results demonstrate that the proposed method has the advantages of fast solving

independence on model parameters

and lower training data requirements.

关键词

Keywords

references

4. Australian Energy Market Commission. ( 2019, Dec. ). “Mechanisms to enhance resilience in the power system-review of the South Australian black system event. [Online]. Available: https://api.semanticscholar.org/CorpusID:204877979.

6. The UK National Grid. ( 2016, Nov. ). System operability framework 2016 [Online]. Available: https://www.nationalgrideso.com/document/63481/download.

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