Joint structure and parameter identification of DFIG wind farm frequency response model

LU Zongxiang; QIAO Ying; JIANG Jiheng; LIU Jingbo; XIE Jianfeng; ZHANG Shiyu

doi:10.19783/j.cnki.pspc.260390

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Joint structure and parameter identification of DFIG wind farm frequency response model

更新时间：2026-09-23

- Joint structure and parameter identification of DFIG wind farm frequency response model
- Power System Protection and Control Vol. 54, Issue 18, (2026)
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- DOI：10.19783/j.cnki.pspc.260390
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- Published：2026
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LU Zongxiang, QIAO Ying, JIANG Jiheng, et al. Joint structure and parameter identification of DFIG wind farm frequency response model[J]. Power System Protection and Control, 2026, 54(18).
DOI：

LU Zongxiang, QIAO Ying, JIANG Jiheng, et al. Joint structure and parameter identification of DFIG wind farm frequency response model[J]. Power System Protection and Control, 2026, 54(18). DOI： 10.19783/j.cnki.pspc.260390.

摘要

双馈风力发电机组可通过附加有功控制向电力系统提供虚拟惯量与下垂调节服务。然而，机组频率响应模型还受转速控制、桨距角控制影响，借用同步机惯量-阻尼模型辨识将产生较大误差。提出了基于增广UD分解(augmented UD decomposition identification

AUDI)的双馈风电场频率响应模型辨识方法。首先论证了频率响应模型在桨距角闭锁和切入时分别呈现5阶、7阶等高阶特征变化。其次，考虑到调度难以获取场站机组内部状态参数导致全环节动态模型无法实施，搭建了双馈风场频率响应的多项式离散系统模型，仅采用场站并网点PMU频率、有功采样数据输入即可完成辨识。推导了AUDI方法的递推形式，并结合F检验理论提出了模型最优阶次选择方法，实现了微扰动工况下的风电场频率响应模型的结构与参数辨识。最后，基于5机11节点仿真表明，所提策略能精准识别变风速工况下的模型阶次、辨识结果接近无偏估计，稳定性优于最小二乘类方法。

Abstract

Doubly-fed induction generator (DFIG) wind turbines can provide virtual inertia and active droop control to power systems through supplementary active power control. However

the frequency response modeling of DFIG-based wind farms are also affected by rotor speed control and pitch angle control. Therefore

directly applying the inertia-damping model commonly used for synchronous generators can result in significant errors. This paper proposes a frequency response model identification method for DFIG-based wind farms based on augmented UD decomposition identification (AUDI). First

it is demonstrated that the frequency response model exhibits high-order characteristics

with the effective model order changing to fifth and seventh order under pitch-angle control locking and activation

respectively. Second

considering that the internal states and control parameters of wind farm units are generally inaccessible to system operators

making a full-order dynamic model impractical for implementation

a polynomial discrete-time model is established for the frequency response of DFIG-based wind farms. The proposed model requires only PMU frequency measurements and active power sampling data at the point of common coupling. An AUDI recursive formulation is derived

and an optimal model-order selection method is developed based on the F-test theory

enabling joint structure and parameter identification of the wind farm frequency response model under small-disturbance conditions. Finally

simulations on a 5-machine

11-bus system demonstrate that the proposed method can accurately identify the model order under varying wind-speed conditions. The identification results are close to unbiased estimates

and the proposed method exhibits better numerical stability than least-squares-based methods.

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references

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