基于相空间重构和卷积神经网络的电能质量扰动分类

为了提高电能质量扰动信号分类的准确率,首先利用相空间重构将一维时间序列电能质量扰动信号重构到多维相空间,获得扰动信号轨迹并投影到二维相平面,形成二维轨迹图像。然后对该图像进行二值化处理,减少信号的数据量,凸显轨迹轮廓。最后通过卷积神经网络对处理后的轨迹图像进行特征提取,并对相应的扰动信号进行分类识别。在卷积神经网络框架Caffe下进行仿真实验,仿真结果表明该方法具有很高的识别准确率和良好的抗噪能力。

Classification for power quality disturbance based on phase-space reconstruction and convolution neural network

In order to improve the accuracy of the power quality disturbance signals classification, firstly, the one-dimensional time series power quality disturbance signals are reconstructed into the multidimensional phase space by phase-space reconstruction theory, thereby obtaining the disturbance signals trajectory and projecting to the two-dimensional plane, and forming a two-dimensional images. Then these images are binarized to reduce the amount of data of the signal and highlight the trajectory profile. Finally, the convolution neural network is used to extract the features of trajectory images and achieve the classification and identification of the corresponding disturbance signals. Simulation experiments are carried out under the Caffe framework of convolution neural network, showing that the method has high recognition accuracy and good ability to resist noise. This work is supported by National Natural Science Foundation of China (No. 51767017 and No. 51267012).