基于AdaBoost算法的剩余电流分类方法

A residual current classification algorithm based on AdaBoost

针对当前剩余电流动作保护装置由于不能检测剩余电流中触电电流类型的问题,搭建了生物体触电实验平台,通过实验分别获取生物体触电电流。基于上述数据,提出一种利用AdaBoost算法的剩余电流分类方法,该方法首先通过提取实验获取不同类型剩余电流分量的特征分量,而后将这些分量特征映射到AdaBoost的算法之中,利用AdaBoost算法检测出总剩余电流中的触电电流分量类型。实验还对比了SVM、随机森林等方法,结果表明所提方法具有一定的优势,可为后续自适应型剩余电流动作保护装置的研制提供理论依据和支撑。

Current residual current action protection device cannot detect the type of residual current in the electric shock current.In this paper,an electric shock test platform is built and the residual current data is obtained by experiment.Based on the above data,this paper presents a residual current classification method based on the AdaBoost algorithm,which firstly obtains the characteristic components of different types of residual current components,and then maps these component features to the AdaBoost algorithm.The type of electric shock current in total residual current is detected by AdaBoost algorithm.Compared with SVM and random forests,the results show that the proposed method has some advantages,which can provide theoretical basis and support for the development of the subsequent adaptive residual current action protection device.