基于卷积神经网络的电能质量扰动分类

Classification of power quality disturbances using convolutional neural network

为了减小在设计电能质量扰动分类器时人工选取扰动特征存在的困难,基于对样本数据进行深度学习提出一种采用卷积神经网络（Convolutional Neural Network,CNN）的电能质量扰动分类算法,利用CNN从原始扰动图像中自适应地提取特征并加以分类.首先将一维电能质量扰动数据映射为二维灰度图,并分析各类扰动在二维灰度图形式下的特征;然后以LeNet-5为基础构建适用于电能质量扰动分类的CNN网络结构,并采用随机梯度下降法训练分类网络对电能质量扰动进行分类.仿真结果表明卷积神经网络能有效收敛,且在不同噪声情况下能准确地进行扰动分类,具有较好的噪声鲁棒性,是电能质量扰动分类的一种可行方法.

In order to reduce the difficulty of designing power quality disturbance（PQD）classifier and make full use of the existing power quality data,basing on deep learning,a novel classification algorithm,which applies convolutional neural network（CNN）is proposed to learn disturbance features adaptively and classify the unknown instances.Firstly,the one-dimensional power quality waves are mapped into two-dimensional grayscale images and the features of perturbation in 2 Dgrayscale form.Then,based on LeNet-5,build CNN architecture which suitable for classification of power quality disturbance,and use stochastic gradient descent to train the CNN’s network parameters.The simulation results show that the proposed algorithm can effectively recognize the power quality disturbances under different noise conditions,and it is a feasible method with noise robustness.