SF6局部放电分解组分光声检测信号交叉响应处理技术  

Crossover Response Processing Technology of Photoacoustic Spectroscopy Signal of SF6 Decomposition Components Under Partial Discharge

作　　者：唐炬[1] 范敏[1,2] 谭志红[1] 孙才新[1] 

机构地区：[1]重庆大学输变电装备及系统安全与新技术国家重点实验室,重庆400030 [2]湖南省电力公司科学研究院,长沙410007

出　　处：《高电压技术》2013年第2期257-264,共8页High Voltage Engineering

基　　金：国家重点基础研究发展计划(973计划)(2009CB724506);优秀博士论文基金(200749)~~

摘　　要：SF6局部放电(PD)时,某些特征分解组分气体的红外吸收频带存在重叠部分,利用光声检测法检测其成分时会有严重的交叉响应,影响检测准确度。为此,将主成分分析(PCA)与径向基函数(RBF)神经网络相结合,构建了一种能降低交叉响应的PCA-RBF神经网络,应用于光声检测法输出信号阵列的处理,以解决传统RBF神经网络在输入空间严重自相关时检测准确度的下降,实现对SO2、CO2、CF4混合气体中各组分气体体积分数的准确检测。结果表明:PCA-RBF神经网络有效地消除了样本之间的相关性,提高了神经网络对混合气体中各组分气体体积分数的检测准确度(平均相对误差<3%),为将光声检测法应用于SF6局部放电分解组分气体的检测提供了有效的数据处理手段。Overlap exists in the infrared absorption band of some SF6 decomposition components under partial discharges（PD）,so there would be serious crossover response and precision decline when photoacoustic technology is used to detect these components.Thus,we combined principal components analysis（PCA） with radial basis function（RBF） neural network to construct a PCA-RBF neural network which suppresses crossover responses.The newly constructed neural network is applied in processing the output signal array of photoacoustic detection to solve the problem of precision decline for using tradition RBF neural network in the case of serious correlated input space,and to achieve accurate detection of concentrations of gas components in a gas mixture of SO2,CO2,and CF4.The results show that the PCA-RBF neural network is effective in eliminating correlation between samples,and it raises the detection precision of neural networks to concentration of various components in gas mixture（the average relative error of this method drop to less than 3%）.The research provides an effective method of data processing for using photoacoustic spectroscopy in detection of SF6 decomposition components under partial discharge.

关 键 词：SF6 局部放电(PD) 交叉响应 主成分分析(PCA) 径向基函数(RBF) 神经网络 相关性 检测准确度 

分 类 号：TM855[电气工程—高电压与绝缘技术]