基于小波熵和小波熵权的电能质量扰动识别

电力系统中电能质量扰动信号的分类和识别一直是国内外众多学者研究的热点问题。小波分析是具有时频局部化特性的时频分析方法,在此基础上定义的小波熵具有较好的定量特征提取能力。基于此,在给出小波熵、小波相对熵和小波熵权的基本原理和定义的基础上,文章提出利用小波熵和熵权两种测度来分类和识别电能质量扰动信号,建立了各种扰动的仿真模型,对电压突降、突升、中断,振荡暂态、脉冲暂态、电压尖峰、缺口、谐波等扰动类型进行了系统的仿真分析。结果表明,不同类型扰动信号的小波熵及熵权具有不同的定性规律,小波熵及小波熵权对电能质量扰动具有一定的分类识别能力。

Recognition of Power Quality Disturbance Signals Based on Wavelet Entropy and Wavelet Entropy Weight

Wavelet analysis is a time-frequency analysis method which possesses localized characteristic, and wavelet entropy is better for feature extraction. Wavelet energy entropy, wavelet energy relative entropy and entropy weight are presented based on wavelet analysis. Signals of power quality disturbance are recognized using wavelet entropy and wavelet entropy weight measure in this paper. The simulated models of the disturbance signals including voltage sag, voltage swell, interruption, oscillatory transients, impulse transients, spike, notch and harmonics are built and analyzed. The result shows that entropy and entropy weight can qualitatively describe kinds of signals, and be able to classify and recognize power quality disturbance signals.