基于GAF与卷积神经网络的电能质量扰动分类

针对在设计电能质量扰动(Power Quality Disturbance, PQD)分类器时人工选取特征过程繁琐并且不够精确的问题,提出一种基于格拉姆角场(Gramian Angular Field, GAF)和卷积神经网络(Convolutional Neural Network, CNN)的PQD分类方法。首先将一维PQD信号映射为二维图像,接着在已有的神经网络基础上构造适用于PQD分类的网络框架。最后将二维图像作为输入,CNN将自动从海量的扰动样本中提取特征并加以分类。仿真结果表明该方法在噪声数据中具有良好的分类性能,是一种行之有效的PQD分类方法。

Power quality disturbance classification based on GAF and a convolutional neural network

Given that the manual feature selection process is cumbersome and not sufficiently accurate, a classification method of Power Quality Disturbance (PQD) based on a Gramian Angular Field (GAF) and a Convolutional Neural Network (CNN) is proposed when designing the power quality disturbance classifier. First, one-dimensional power quality disturbance signals are mapped to two-dimensional images. Then a network framework suitable for power quality disturbance classification is constructed based on the existing neural network. Finally, two-dimensional images are taken as input, and the CNN will automatically extract features from the massive disturbance samples and classify them. Simulation results show that this method has good classification performance in noisy data, and it is an effective power quality disturbance classification method. This work is supported by the National Natural Science Foundation of China (No. 61871133).