【摘要】 时频分析方法是变压器局部放电特征提取的有效手段。讨论利用小波变换方法分析非平稳信号的局限性,介绍希尔伯特–黄变换(Hilbert-Huang transform,HHT)的理论体系,并分别应用2种时频分析方法得到不同类型局放信号的幅值谱,从中提取了时频熵向量作为用于模式识别的特征量,模糊聚类结果显示,由希尔伯特–黄变换得到的时频熵向量具有优于小波变换的聚类特性。因此,希尔伯特–黄变换是目前变压器局放信号特征提取的有效方法。更多还原

【Abstract】 Time-frequency analysis is an efficient method in feature extraction for transformer partial discharge.The shortcomings of wavelet transform were discussed firstly as approaches to analyze non-stationary signals;then the theoretical framework of Hilbert-Huang transform was introduced;finally,the time-frequency spectrums were obtained through the two methods mentioned above and time-frequency entropy vectors were extracted from them as the features for pattern recognition.The result of fuzzy clustering shows that the clustering characteristic of HHT entropy vectors are better than those of wavelet transform,so HHT is a more efficient way to extract features of partial discharge pulses for transformers.更多还原