希尔伯特-黄变换(HHT)在复合电能质量扰动分析中的应用
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摘要
为准确分辨出电能信号中的扰动成份,进行电能质量治理,笔者通过对暂降+谐波、暂升+谐波、脉冲+谐波、暂升+波动、暂降+波动、脉冲+波动等6种双重扰动信号进行EMD分解,希尔伯特-黄变换,然后着重对信号的前2阶IMF,第1阶IMF的瞬时频率,希尔伯特谱进行分析。结果显示,EMD分解能分解出复合的扰动信号成份,第一阶的IMF分量的瞬时频率及希尔伯特谱能对扰动发生时刻、谐波成份进行较准确的分析。
In order to accurately distinguish the disturbance components in the power signal, the power quality control is carried out. In this paper, EMD decomposition and Hilbert-Huang transform are carried out for six kinds of double disturbance signals, such as sag+harmonic, swell+harmonic, pulse+harmonic, swell+fluctuation, sag+fluctuation and pulse+fluctuation, then the instantaneous frequency and Hilbert spectrum of the first two order IMF are analyzed. The results show that EMD decomposition can decompose the composite disturbance signal components, the instantaneous frequency and Hilbert spectrum of the first order IMF components can accurately analyze the disturbance occurrence time and harmonic components.
In order to accurately distinguish the disturbance components in the power signal, the power quality control is carried out. In this paper, EMD decomposition and Hilbert-Huang transform are carried out for six kinds of double disturbance signals, such as sag+harmonic, swell+harmonic, pulse+harmonic, swell+fluctuation, sag+fluctuation and pulse+fluctuation, then the instantaneous frequency and Hilbert spectrum of the first two order IMF are analyzed. The results show that EMD decomposition can decompose the composite disturbance signal components, the instantaneous frequency and Hilbert spectrum of the first order IMF components can accurately analyze the disturbance occurrence time and harmonic components.
引文
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[2] 潘从茂,李凤婷.基于小波变换的暂态电能质量的检测与识别[J].电测与仪表,2013(11):69-72.
[3] 王海元,李开成,张献.基于S变换的电能质量扰动识别[J].湖南电力,2014(4):17-21.
[4] 刘晓华,谢志文,詹清华.基于S变换的电能质量扰动异常数据识别[J].电气应用,2014(11):78-79.
[5] DONNELLY D.The Fast Fourier and Hilbert-Huang Transforms:A Comparison[Z].IEEE,2006,1:84-88.
[6] HAJIAN M,AKBARI FOROUD A.A new hybrid pattern recognition scheme for automatic discrimination of power quality disturbances[J].Measurement,2014,51:265-280.
[7] 陈华丰.电能质量扰动信号识别方法研究[D].成都:西南交通大学,2013.