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

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。

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

According to huge 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. Decompose and reconstruct inrush current and fault current signal using wavelet packet, extracted wavelet packet reconstruction coefficients, calculate the energy of each band and normalized to construct energy feature vectors as input sample for BP network training and testing, and finally propose protection criterion. Through a large number of samples simulation using PSCAD / EMTDC and MATLAB software, it proves that the program can quickly and accurately identify inrush current and internal fault current.