摘要
针对电力故障信号为突变、非平稳信号的特点,利用消噪阈值法对故障信号进行预处理,通过小波变换对故障信号进行特征提取,然后将小波变换和信息熵相结合,并应用到故障检测与定位中,为奇异信号的检测提供了新思路和新手段。通过仿真分析,得到以下结论:小波能量熵WEE可以很好地检测故障突变点,小波能量熵随时间的变化规律,反映了电流或电压在时域的能量分布特征并表征信号的频率变化,可以用作分类所需的特征。
In view of power fault signal is catastrophe and non-stationary, fault signal is pre-processed using de-noising threshold method, fault signal feature is extracted through wavelet transform, and the wavelet transform and entropy are combined, which is applied to fault detection and location, and it provides a new idea and method for singular signal. Through simulation analysis, some conclusions were as follows： catastrophe point of fault signal is detected well through wavelet energy entropy WEE, if only the left side of data window is coincidence with the signal catastrophe point. WEE curve started to flatten, the moment correspond to signal catastrophe point.
出处
《煤矿机械》
北大核心
2010年第11期257-260,共4页
Coal Mine Machinery
关键词
故障信号
小波能量熵
小波变换
阈值法
fault signal
wavelet energy entropy
wavelet transform
threshold method
