基于多维度特征提取的电弧故障检测方法

Arc fault detection based on multi-dimension feature extraction

针对当前含多种电气故障的复杂电路电弧故障识别率低、训练速度慢的问题,提出一种窗口划分结合小波分解与经验模态分解(empirical mode decomposition,EMD)分别从时域、频域及时间尺度等多个维度提取电流特征量,利用机器学习分类模型进行电弧故障识别的方法。首先,利用搭建的电气故障实验平台采集故障及正常电流数据,并将电流数据进行窗口分段,然后分别使用小波变换与EMD方法对电流信号进行分解并计算不同维度上的特征量,将该特征信息作为分类算法的输入进行电弧故障诊断。经实验验证,该特征提取方法在梯度提升决策树(gradient boosting decision tree,GBDT)上的电弧故障检测准确率高达98%,相比电流不分段的方式分类准确率提升了1.87%,能有效获取电弧故障特征,实现对电弧故障高效率与高准确率检测。

Aiming at the problem of low accuracy and slow training speed in complex circuits with multiple electrical faults,a method of window division combined with wavelet decomposition and empirical mode decomposition(EMD)is proposed to extract current characteristic quantities respectively from multiple dimensions in time domain.,frequency domain and time scale,identifying arc fault by using machine learning classification models.Firstly,the fault and normal current data are collected by the electrical fault experimental platform,and the current data is segmented by window.Then,the wavelet transforming and EMD methods are used to decompose the current signal and calculate the characteristic quantities in different dimensions.The characteristic information collected is used as the input of the classification algorithm for arc fault diagnosis.The experimental results show that the arc fault detection accuracy of the feature extraction method on the gradient boosting decision tree(GBDT)is as high as 98%,which is 1.87%higher than that of the current without segmentation.It can effectively obtain the arc fault characteristics and realize the detection of arc fault with high efficiency and high accuracy.