| 引用本文: | 陶佳兰,喻 敏,陈贵词,王 斌.基于SWT的电力系统基波检测[J].电力系统保护与控制,2022,50(18):39-49.[点击复制] |
| TAO Jialan,YU Min,CHEN Guici,WANG Bin.Fundamental detection for a power system based on SWT[J].Power System Protection and Control,2022,50(18):39-49[点击复制] |
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| 摘要: |
| 在噪声混入含有基波的信号时，传统的时频分析方法在基波提取过程中易出现模态混叠。为了准确检测出基波分量，利用时频分析精度较高的同步挤压小波变换(Synchrosqueezing Wavelet Transform, SWT)实现基波检测。首先，采用SWT将含有基波的信号分解为一组内蕴模态类函数(Intrinsic Mode Type functions, IMTs)，第一个分量IMT1即代表基波。然后，该分量经Hilbert变换实现基波频率和幅值的测量。在谐波幅值瞬变、噪声混入、基波频率波动、间谐波频率靠近基波和谐波的情境下进行算法验证。实验结果表明，SWT能够准确提取基波，频率精度最高可达108量级，具有较强的抗噪性，且SWT的基波提取能力强于谐波和间谐波。 |
| 关键词: 基波检测 模态混叠 同步挤压小波变换 希尔伯特变换 抗噪性 |
| DOI：DOI: 10.19783/j.cnki.pspc.211660 |
| 投稿时间：2021-12-06修订日期：2022-03-08 |
| 基金项目:国家自然科学基金项目资助(51877161，61671338)；冶金工业过程系统科学湖北省重点实验室基金重点项目资助(Y202007，Z201901) |
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| Fundamental detection for a power system based on SWT |
| TAO Jialan,YU Min,CHEN Guici,WANG Bin |
| (1. Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology,
Wuhan 430065, China; 2. College of Science, Wuhan University of Science and Technology, Wuhan 430065, China;
3. College of Information and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China) |
| Abstract: |
| When noise is mixed into a signal containing the fundamental component, the traditional time-frequency analysis method is prone to modal aliasing in the fundamental component extraction process. To accurately detect the fundamental component, synchrosqueezing wavelet transform (SWT) with high accuracy of time-frequency analysis is applied to realize the fundamental detection. First, the signal containing the fundamental component is decomposed into a set of intrinsic mode type functions (IMTs) by SWT. The first component IMT1 represents the fundamental. Then, the fundamental frequency and amplitude are measured by Hilbert Transform. Finally, the algorithm is verified in the situation of harmonic amplitude transient, noise mixing, fundamental frequency fluctuation and interharmonic frequencies near the fundamental and harmonic frequencies. The experimental results show that SWT can accurately extract the fundamental component, and the frequency accuracy can reach 108 orders of magnitude. The method has strong noise resistance and is better at extracting the fundamental component than harmonics and interharmonics.
This work is supported by the National Natural Science Foundation of China (No. 51877161 and No. 61671338). |
| Key words: fundamental detection mode aliasing SWT Hilbert transform noise resistance |
