基于小波包和改进BP神经网络的变压器励磁涌流识别方法  

Transformer inrush current identification method based on wavelet packet and improved BP neural network

作　　者：公茂法[1] 李美蓉[1] 殷凡姣 王中刚[1] 刘丙乾[1] 邵群[1] 李杰[1] 

机构地区：[1]山东科技大学电气与自动化工程学院,山东青岛266590

出　　处：《电测与仪表》2015年第6期124-128,共5页Electrical Measurement & Instrumentation

基　　金：国家级大学生创新创业训练计划资助项目(201210424046);山东省自然科学基金资助项目(ZR2012EEM021);山东科技大学研究生科技创新基金资助项目(YC140338)

摘　　要：根据励磁涌流和内部故障电流的波形特征存在巨大差异,提出一种基于小波包和改进BP网络的识别励磁涌流的新算法。利用小波包对励磁涌流和故障电流信号进行分解和重构,提取小波包重构系数,计算各频段的能量并进行归一化处理,构造能量特征向量,作为BP网络的输入样本,进行训练和测试,提出保护判据。经过PSCAD/EMTDC和MATLAB软件对大量样本进行仿真验证,证明该方案能够快速准确地识别励磁涌流和内部故障电流。According to the great difference in the waveform characteristics between inrush current and internal fault current,the paper proposed a new method to identify inrush current based on wavelet packet and improved BP network.The inrush current and fault current signal are decomposed and reconstructed by using wavelet packet to extract wavelet packet reconstruction coefficients,calculate the energy of each band and normalize the process and construct energy feature vectors as a sample for BP network training and testing,and finally propose the protection criterion.Through a large number of samples simulation by PSCAD / EMTDC and MATLAB software,it proves that the program can quickly and accurately identify inrush current and internal fault current.

关 键 词：小波包 改进BP神经网络 励磁涌流 变压器 

分 类 号：TM93[电气工程—电力电子与电力传动]