【摘要】 为了对模拟气体绝缘组合电器(gas insulated switchgear,GIS)的4种绝缘缺陷产生超高频(ultra high frequency,UHF)局部放电(partial discharge,PD)数据和波形进行识别,用复小波变换对UHF PD信号进行分解,利用均值、方差、偏斜度、陡峭度、能量共5个统计参量对复小波变换的各尺度系数进行量化,构造出能够描述UHF PD信号特征的候选特征子集,引入衡量特征分类能力的分离度指标J作为特征量降维的评判指标,从60个特征量中选取了5个具有较高分类能力的最佳特征量,作为径向基神经网络识别放电类型的输入有效向量,识别结果表明:db系列复小波系数的实部和虚部信息共同描述了PD信号的特征,从中提取的最佳特征量具有较高的分类能力,其中db4复小波的分类效果最好。更多还原

【Abstract】 In order to classify the ultra-high-frequency(UHF) partial discharge(PD) signals resulting from four types of insulation defects in gas insulated switchgear(GIS),the complex wavelet transform is applied to extract features of UHF PD signals.Five statistical parameters including mean,variance,kurtosis,skewness and energy are used to quantize the scaling coefficients of the complex wavelet transform and describe the feature subsets of UHF PD signals.A critical index J is defined to select features according to their classification performance.Using the J criterion,five optimal features are selected from sixty UHF PD features and taken as the input of radial basis function neural network.The classification results show that the information of real part and image part of complex wavelet coefficients indicates the characteristics of UHF PD singles and the recognition effect is pretty good.To use db4 complex wavelet can get the best classification performance.更多还原