| 引用本文: | 郭 成,杨宣铭,杨灵睿,等.基于改进SOBI-SGMD算法的次同步振荡模态辨识研究[J].电力系统保护与控制,2025,53(14):100-110.[点击复制] |
| GUO Cheng,YANG Xuanming,YANG Lingrui,et al.Research on subsynchronous oscillation mode identification based on improved SOBI-SGMD algorithm[J].Power System Protection and Control,2025,53(14):100-110[点击复制] |
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| 摘要: |
| 针对次同步振荡(sub-synchronous oscillation, SSO)信号的准确辨识问题，提出了一种基于动态时间规整 (dynamic time warping, DTW)算法改进的辛几何模态分解(symplectic geometry mode decomposition, SGMD)与二阶盲辨识(second order blind identification, SOBI)相结合的多通道次同步振荡辨识预警方法。首先，对SSO信号进行SGMD，经对角平均化与自适应重构后分解为初始辛几何模态分量(initial symplectic geometric mode components, ISGMCs)，通过DTW算法计算ISGMCs间的最优距离值以度量序列的相似性，自适应筛选出具有独立模态的辛几何分量(symplectic geometry components, SGCs)。其次，将主导的SGCs作为观测信号输入SOBI算法矩阵中，并对观测矩阵联合近似对角化逼近，得到完整的SSO源估计信号，引入最小二乘法改进SOBI算法直接辨识SSO的振荡频率、衰减因子。最后，通过对理想算例与仿真算例的对比分析，验证了所提算法能够精确高效地辨识多通道次同步振荡信号。 |
| 关键词: 辛几何模态分解 二阶盲辨识 次同步振荡 多通道辨识 动态时间规整算法 |
| DOI：10.19783/j.cnki.pspc.241215 |
| 投稿时间：2024-09-08修订日期：2024-12-04 |
| 基金项目:国家自然科学基金项目资助(52367002)；云南省科技厅联合基金重点项目资助(202201BE070001-15) |
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| Research on subsynchronous oscillation mode identification based on improved SOBI-SGMD algorithm |
| GUO Cheng1,YANG Xuanming1,YANG Lingrui1,XI Xinze2 |
| (1. Faculty of Power Engineering, Kunming University of Science and Technology, Kunming 650500, China;
2. Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China) |
| Abstract: |
| Aiming at the problem of accurate identification of subsynchronous oscillation (SSO) signals, a multi-channel SSO identification and early warning method is proposed by combining symplectic geometry mode decomposition (SGMD) improved with dynamic time warping (DTW) and second-order blind identification (SOBI). First, SGMD is applied to the SSO signal, which is then subjected to diagonal averaging and adaptive reconstruction to obtain the initial symplectic geometric mode components (ISGMCs). The optimal distance between ISGMCs is calculated by DTW algorithm to measure the similarity of sequences, and the symplectic geometry components (SGCs) with independent modes are adaptively selected. Next, the dominant SGCs are used as observation signals and input into the SOBI algorithm. By performing joint approximate diagonalization on the observation matrix, the complete SSO source estimation signals are obtained. The least square method is introduced to improve the SOBI algorithm, enabling direct identification of the SSO oscillation frequency and attenuation factor. Finally, through comparative analysis of ideal and simulation examples, it is verified that the proposed algorithm can accurately and efficiently identify multi-channel subsynchronous oscillation signals. |
| Key words: symplectic geometry mode decomposition second order blind identification subsynchronous oscillation multi-channel identification dynamic time warping algorithm |
