| 引用本文: | 郭 成,代剑波,杨灵睿,何 觅,杨发宇.基于ISGMD-DHT的电压暂降特征提取方法研究[J].电力系统保护与控制,2024,52(7):70-79.[点击复制] |
| GUO Cheng,DAI Jianbo,YANG Lingrui,HE Mi,YANG Fayu.A voltage sag feature extraction method based on ISGMD-DHT[J].Power System Protection and Control,2024,52(7):70-79[点击复制] |
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| 摘要: |
| 针对电压暂降特征信号在谐波、噪声环境下的准确提取问题，提出了一种基于迭代辛几何模态分解-差值希尔伯特变换(iteration symplectic geometry mode decomposition-difference Hilbert transform, ISGMD-DHT)的提取方法。首先，基于哈密顿矩阵与辛QR分解构造重构轨迹矩阵，结合辛几何相似变换得到初始辛几何分量。其次，根据相似度准则拟合初始辛几何分量并计算残余分量，再根据残余分量构造轨迹矩阵。然后，重复上述操作直至满足迭代终止条件获得最终相互独立的辛几何分量。最后，通过差值希尔伯特变换提取暂降特征量。仿真和实测数据的分析结果表明，该方法能在严重噪声、谐波扰动情况下准确提取暂降特征量。 |
| 关键词: 辛几何模态分解 电压暂降 特征提取 差值希尔伯特变换 辛几何分量 |
| DOI：10.19783/j.cnki.pspc.231305 |
| 投稿时间：2023-10-09修订日期：2023-12-25 |
| 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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| A voltage sag feature extraction method based on ISGMD-DHT |
| GUO Cheng1,DAI Jianbo1,YANG Lingrui1,HE Mi2,YANG Fayu2 |
| (1. Faculty of Power Engineering, Kunming University of Science and Technology, Kunming 650500, China;
2. Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650000, China) |
| Abstract: |
| Aiming at the accurate extraction of voltage sag characteristic signals in harmonic and noisy environments, an extraction method based on iteration symplectic geometry mode decomposition-difference Hilbert transform (ISGMD-DHT) is proposed. First, the reconstruction trajectory matrix is constructed based on the Hamiltonian matrix and symplectic QR decomposition, and the initial symplectic geometric component is obtained by combining with the symplectic geometric similarity transformation. Secondly, the initial symplectic geometric component is fitted according to the similarity criterion and the residual component is calculated, and then the trajectory matrix is constructed according to the residual component. Next, the above operations are repeated until the iteration termination condition is satisfied to obtain the final independent symplectic geometric components. Finally, the sag characteristic quantities are extracted by the difference Hilbert transform. The analysis of simulation and measured data show that the proposed method can accurately extract the sag feature quantity under severe noise and harmonic disturbance. |
| Key words: symplectic geometric mode decomposition voltage sag feature extraction difference Hilbert transform symplectic geometric components |
