基于SMSST-CDSSI的风电并网系统次同步振荡模式辨识

王长江; 代笑笑; 刘博涵; 周勤勇; 姜懿郞; 王庆

doi:doi:10.19912/j.0254-0096.tynxb.2024-2216

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基于SMSST-CDSSI的风电并网系统次同步振荡模式辨识

更新时间：2026-07-22

- 基于SMSST-CDSSI的风电并网系统次同步振荡模式辨识
- Vol. 47, Issue 4, Pages: 350-359(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2216
  CLC： TM712
- Online First：08 May 2026，
  
  Published：2026
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王长江, 代笑笑, 刘博涵, et al. 基于SMSST-CDSSI的风电并网系统次同步振荡模式辨识[J]. 2026, 47(4): 350-359.
DOI：

王长江, 代笑笑, 刘博涵, et al. 基于SMSST-CDSSI的风电并网系统次同步振荡模式辨识[J]. 2026, 47(4): 350-359. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2216.

摘要

为实现风电并网系统次同步振荡（SSO）特征参数的精确辨识

该文提出一种基于二阶多重同步压缩变换（SMSST）和组合确定性随机子空间（CDSSI）结合的SSO辨识方法。首先

为准确追踪SSO信号的非线性时变特性

利用SMSST对SSO信号进行分解并构建时频系数矩阵

以提升信号的时频能量聚集能力;然后

在时频域中采用脊线提取方法对各振荡模式进行最佳时频轨迹搜索

借助最佳时频轨迹重构各振荡分量的时域SSO分量;进一步

利用CDSSI方法辨识SSO信号的频率和阻尼比等振荡特征参数;最后

通过自合成SSO信号、风电并网系统算例仿真信号和实际系统SSO的监测数据验证了所提方法的有效性与准确性。

Abstract

The increasing integration of large-scale wind power into the grid poses risks of sub-synchronous oscillation （SSO）

posing a significant threat to the stable operation of the power system. This paper proposes a novel SSO identification methodology integrating second-order multi-synchrosqueezing transform （SMSST） with combined deterministic-stochastic subspace identification （CDSSI） to achieve precise parameter extraction in wind-integrated power systems. Firstly

to accurately track the nonlinear and time-varying characteristics of SSO signals

SMSST is employed to decompose the SSO signal and construct a time-frequency coefficient matrix

enhancing the energy concentration in the time-frequency domain. Then

ridge extraction method is applied in the time-frequency domain to perform optimal time-frequency trajectory searching for each SSO mode. Using the optimal time-frequency trajectory

the time-domain SSO components of each oscillation mode are reconstructed. Subsequently

the CDSSI method is utilized to identify the oscillatory characteristic parameters of the SSO signal

such as frequency and damping ratio. Finally

the effectiveness and accuracy of the proposed method are validated through synthetic SSO signals

simulation signals from a wind power integrated system

and monitoring data of actual system SSO events.

关键词

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references

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