| 引用本文: | 陈添富,高 伟,郭谋发,等.基于顺序变分模态分解和改进两步法的宽频振荡频率动态估计[J].电力系统保护与控制,2026,54(04):89-100.[点击复制] |
| CHEN Tianfu,GAO Wei,GUO Moufa,et al.Dynamic frequency estimation of wideband oscillations based on successive variational mode decomposition and an improved two-step method[J].Power System Protection and Control,2026,54(04):89-100[点击复制] |
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| 摘要: |
| 高比例新能源并网加剧了电网宽频振荡问题，可能引发机组脱网等后果。其宽频域、多模态等特性导致信号呈现混叠性和非平稳性，给频率精确估计带来严峻挑战。实时精准的频率估计有助于抑制频率振荡。为此提出一种基于顺序变分模态分解(successive variational mode decomposition, SVMD)与改进两步法(improved two-step, ITS)的宽频振荡信号频率动态估计法。首先，通过引入鲸鱼优化算法(whale optimization algorithm, WOA)自适应确定SVMD的最大惩罚因子，然后对信号进行SVMD，得到各个固有模态分量(intrinsic mode function, IMF)，有效避免了对先验知识的依赖。其次，结合两步法(two-step method, TS)与多重同步压缩变换(multisynchrosqueezing transform, MSST)，通过相位解调技术和时频谱优化，提高频率估计的分辨率与抗噪性。实验表明，所提方法分解的IMF波形与原信号振荡分量波形高度相似，频率动态估计的精确度相比TS、希尔伯特变换等方法显著提高，在仿真和实测信号中均能实现对振荡信号瞬时频率的动态追踪。 |
| 关键词: 宽频振荡 动态估计 鲸鱼优化算法 顺序变分模态分解 多重同步压缩变换 |
| DOI：10.19783/j.cnki.pspc.250641 |
| 投稿时间：2025-06-12修订日期：2025-11-19 |
| 基金项目:福建省自然科学基金项目资助(2021J01633) |
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| Dynamic frequency estimation of wideband oscillations based on successive variational mode decomposition and an improved two-step method |
| CHEN Tianfu1,GAO Wei1,2,GUO Moufa1,2,YANG Gengjie1 |
| (1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; 2. Department of
Electrical Engineering, Fuzhou University Zhicheng College, Fuzhou 350002, China) |
| Abstract: |
| The high penetration of renewable energy integration has exacerbated wideband oscillation problems in power systems, potentially leading to consequences such as generator tripping. The wideband and multimodal characteristics of such oscillations result in signal aliasing and non-stationarity, posing significant challenges to accurate frequency estimation. Real-time and precise frequency estimation is crucial for suppressing frequency oscillations. To address this problem, this paper proposes a dynamic frequency estimation method for wideband oscillation signals based on successive variational mode decomposition (SVMD) and an improved two-step (ITS) approach. First, the whale optimization algorithm (WOA) is introduced to adaptively determine the maximum penalty factor of SVMD, and the oscillation signal is then decomposed via SVMD into intrinsic mode functions (IMFs), effectively avoiding reliance on prior knowledge. Second, by integrating the two-step (TS) method with multi-synchrosqueezing transform (MSST), the resolution and noise immunity of frequency estimation are enhanced through phase demodulation and time-frequency spectrum refinement. Experimental results demonstrate that the IMFs decomposed by the proposed method closely resemble the oscillation components of the original signal. Furthermore, the accuracy of dynamic frequency estimation is significantly improved compared to traditional methods such as TS and Hilbert Transform, enabling effective dynamic tracking of the instantaneous frequency of oscillation signals in both simulated and measured data. |
| Key words: wideband oscillation dynamic estimation whale optimization algorithm successive variational mode decomposition multi-synchrosqueezing transform |
