基于卷积深度置信网络的配电网故障分类方法

Fault classification method based on CDBN for distribution network

提出一种基于卷积深度置信网络(CDBN)实现配电网故障分类的方法,利用离散小波包变换(DWPT)分解主变低压侧进线电流和母线电压等电量信号并构造时频矩阵,将时频矩阵转换成时频谱图的像素矩阵后作为CDBN的输入,经CDBN自主提取故障特征量,最终完成配电网故障分类识别。应用典型结构配电网的故障仿真数据与故障实验样本进行故障识别测试,结果表明,所提方法不但具有提取故障特征明显、故障分类正确率较高的特点,并且在系统中性点运行方式及网络结构调整、故障起动检测延迟、分布式电源接入等情况下,均有良好的应用适应性。

A novel fault classification method based on CDBN(Convolutional Deep Belief Network)for distribution network is proposed.The DWPT(Discrete Wavelet Packet Transform)is adopted to decompose signals of the main transformer including current of low-voltage inlet line,bus voltage,and so on,to construct time-frequency matrices.Then the time-frequency matrices are transformed into the pixel matrices of the time-frequency spectrum map,which is used as the input of CDBN.Then the fault features are autonomously extracted by CDBN,and the fault classification and recognition of distribution network is completed.Fault classification test is carried out with simulative data and experimental samples of a typical structure distribution network.The testing results show that the proposed method not only can extract obvious fault characteristics with high classification accuracy,but also adapts well to the change of neutral-point grounding mode and system network structure,fault detection delay and connection of distributed generation.