基于深度学习的PRPD数据特征提取方法

Feature extraction method of PRPD data based on deep learning

气体绝缘金属封闭开关设备(Gas Insulated Switchgear,GIS)局部放电(Partial Discharge,PD)的传统特征提取具有依赖专家经验、盲目性高、识别率低的缺点,文中将局部放电PRPD数据转变为灰度图,利用卷积神经网络强大的特征自适应提取能力提取灰度图的辨识特征,并将特征应用于经典分类器如SVM、随机森林,BP神经网络等,实现深度学习方法和传统机器学习方法的有效融合。实验表明,该方法提取的特征具有更高的辨识度,可以有效提升局部放电模式识别的准确率。

The traditional feature extraction method of partial discharge of gas insulated switchgear(GIS)has the disadvantages of relying on expert experience,high blindness and low recognition accuracy.This paper will convert PRPD data of partial discharge into gray-scale maps,and the identify features of which were extracted by the convolutional neural network with the powerful adaptive feature extraction ability.The extracted features were applied to classical classifiers such as SVM,random forest,and BP neural network,to realize the effective integration of deep learning methods and traditional machine learning methods.The experimental results show that the features extracted by this method have higher differentiation degrees,which can effectively improve the accuracy of partial discharge pattern recognition.