基于机器学习方法的直流电弧故障检测

为了解决传统分析方法在直流供电系统中电弧故障检测的精确度不足及过程繁琐的问题,将直流电弧故障检测归为二分类问题,引入机器学习方法,通过直流电弧实验得到正常状态和电弧状态的数据,从时域中提取电流均值等4个特征,从频域中提取高频分量标准差等3个特征.利用提取到的特征对支持向量机(SVM)进行训练,利用求解得到的模型对测试数据集进行分类,分类准确率为94.483%.结果证明:所提方法能有效检测直流电弧故障,提高故障检测精度,且步骤精简,易于推广.

DC arc fault detection based on machine learning method

In order to solve the problems that in direct current(DC)power supply system,accuracy of arc fault detection is insufficient and the process is tedious with the traditional analysis method. The DC arc fault detection is classified into two classification problems while the machine learning method is used. The data of normal state and arc fault state are obtained by DC arc experiment. Extract four features from time domain,including the average current and so on. At the same time,extract three characteristics from frequency domain,such as standard deviation of high frequency component,etc. By training support vector machine(SVM)using the extracted features above,classification model is obtained. The accuracy of classification of the test data set by the model is 94. 483%,the result proves that this method can be used to detect DC arc fault effectively,improve detection precision,and can be popularized easily because of simple steps.