| 引用本文: | 黄建明,李晓明.结合短时傅里叶变换和谱峭度的电力系统谐波检测方法[J].电力系统保护与控制,2017,45(7):43-50. |
| HUANG Jianming,LI Xiaoming.Detection of harmonic in power system based on short-time Fourier transform and spectral kurtosis[J].Power System Protection and Control,2017,45(7):43-50 |
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| 摘要: |
| 针对电能质量分析中的谐波检测问题，提出一种结合短时傅里叶变换和谱峭度的电力系统谐波检测方法。采用短时傅里叶变换作为时频分析工具对采样信号进行时频分解，同时引入频谱标准差和基于短时傅里叶变换的谱峭度作为辅助分析。通过频谱标准差和谱峭度对谐波模态进行识别，并根据识别结果从频谱矩阵中提取出相应的谐波分量，然后采用基于奇异值分解的扰动定位方法对暂态谐波的起止时刻进行准确定位。仿真实验结果验证了算法的有效性，在低信噪比的情况下仍具有较高的检测精度，具有较好的抗噪性和鲁棒性。 |
| 关键词: 短时傅里叶变换 谱峭度 电力系统 谐波检测 奇异值分解 |
| DOI：10.7667/PSPC160506 |
| 分类号: |
| 基金项目:国家自然科学基金项目(51277134) |
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| Detection of harmonic in power system based on short-time Fourier transform and spectral kurtosis |
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HUANG Jianming,LI Xiaoming
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(School of Electrical Engineering, Wuhan University, Wuhan 430072, China)
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| Abstract: |
| Aiming at the problem of harmonic detection in power quality analysis, a new method for harmonic detection in power system based on short-time Fourier transform and spectral kurtosis is proposed. As the time-frequency analysis tool, short-time Fourier transform is used to decompose the sampled signal. Meanwhile, the spectrum of standard deviation and spectral kurtosis based on short-time Fourier transform are introduced as auxiliary analysis. The harmonic modal is identified through the spectrum of standard deviation and spectral kurtosis, and the corresponding harmonic components are extracted by the spectrum matrix according to the result of recognition. Furthermore, a method for disturbances location based on singular value decomposition is presented to locate the transient harmonic signals. Results of a simulation verify the efficiency of the proposed method, indicating that it still has high detection accuracy under the condition of low signal noise ratio (SNR), and has better noise immunity and robustness. This work is supported by National Natural Science Foundation of China (No. 51277134). |
| Key words: short-time Fourier transform spectral kurtosis power system harmonic detection singular value decomposition |
