摘要
大型发电机局部放电信号的极性对于局部放电类型的识别有重要意义。数字化的局部放电在线监测设备受现场强干扰和采样频率的限制,采集的局部放电信号存在大量噪声和局放信息丢失,给信号极性的提取带来了很大困难。小波变换是时间和频率的局部转换,通过伸缩和平移运算对信号进行多尺度细化分析,是去除信号噪声的有力工具。采用FFT作出所采集信号的频域图,确定局部放电信号的频率段,取出该频率段的信号。再利用与局部放电脉冲十分接近的Meyer小波对取出的信号去噪。然后,对去噪后的信号统计一定放电电压范围的正负极性脉冲,并根据局部放电信号极性与放电类型之间的关系,确定局部放电的类型。
The polarity of large-scale generator partial discharge signal has importance to the partial discharge type recognition. Digital partial discharge on - line monitoring facilities are restricted by the strong disturb and the sampling frequency, so collected partial discharge signals have a lot of noises and lose some information about partial discharge, This brings big difficulty to polarity signal extraction, Wavelet transformation, which is time and frequency part changing through expansion and shrinkage and peace calculation, carries out much dimension refining analysis on the signal, and it is a good tool to remove signal noises. Firstly, we made up the frequency domain of the collected signal by FFT, and fixed the frequency range about partial discharge signal, and then took out the signal in the frequency range. Secondly, removed noises for extracted signal making use of Meyer wavelet that is very close to partial dis- charge impulse. At last, we counted the positive and negative polarity impulse in the fixed discharge voltage range for denoised signal, and confirmed partial discharge type based on the relations between the partial discharge signal polarity and the discharge type.
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2008年第3期1-5,23,共6页
Journal of North China Electric Power University：Natural Science Edition
基金
国家自然科学基金资助项目(50677017)
