摘要
二元树复小波变换(DT-CWT)在时域和频域都具有表征信号局部特征的能力,且二元树复小波还具备近似平移不变、多方向选择、完全重构和高效计算等优点。而基于小波的信息熵能反映信号统计分布特征,突出系统信号中短暂的异常信号,达到及早发现可能故障的目的。笔者对4种典型绝缘缺陷产生的局部放电脉冲波形进行二元树复小波分解,将提取每层分解系数上的能量特征和小波能量熵测度作为模式识别的特征量。通过大量的试验获得放电样本,用构建的BP神经网络作为分类器,对4种典型绝缘缺陷产生的局部放电进行了有效识别,结果表明:采用此特征量的神经网络识别方法简单、有效、实用,为局部放电信号的识别提供了有效的参考。
The dual-tree complex wavelet transform （DT-CWT） has the ability to characterize local feature of signal in both time-domain and frequency-domain, and the dual-tree complex wavelet has such merits as approximate shift invariance, good directional selectivity, and high computational efficiency. The information entropy based on wavelet can reveal statistic distribution properties of signal and find transient abnormal signal in system signal, so that some faults can be found early. In this paper, the partial discharge （PD） pulse waveforms which are generated by 4 typical insulation defects are transformed by DT-CWT, and then the energy and wavelet energy entropy in every coefficient are extracted as the features for pattern recognition. Discharge samples are obtained through large number of experiments, and a BP neural network, which plays the role of classifier, is established to recognize the PD signals generated by 4 typical insulation defects. The results show that the PD signals can be easily and sufficiently recognized by the neural network method using the features.
出处
《高压电器》
CAS
CSCD
北大核心
2009年第6期44-48,共5页
High Voltage Apparatus
关键词
局部放电
二元树复小波
小波能量熵测度
特征提取
模式识别
partial discharge
dual-tree complex wavelet transform（DT-CWT）
wavelet energy entropy
feature extraction
pattern recognition
