基于自适应特征增强分组卷积网络的电能质量扰动分类  

Power Quality Disturbances Classification Based on Grouping Convolutional Network With Adaptive Feature Enhanced Network

作　　者：张锐[1] 张闯 高辉 程政铎 ZHANG Rui;ZHANG Chuang;GAO Hui;CHENG Zhengduo(School of Automation,Harbin University of Science and Technology,Harbin 150080,Heilongjiang Province,China;School of Electrical and Information Engineering,Heilongjiang Institute of Technology,Harbin 150050,Heilongjiang Province,China)

机构地区：[1]哈尔滨理工大学自动化学院,黑龙江省哈尔滨市150080 [2]黑龙江工程学院电气与信息工程学院,黑龙江省哈尔滨市150050

出　　处：《中国电机工程学报》2023年第15期5808-5817,共10页Proceedings of the CSEE

摘　　要：分布式电源在接入电网时会产生复杂的电能质量扰动(power quality disturbances,PQDs),为提高对PQDs信号分类识别的准确率,构建了自适应特征增强分组卷积神经网络(grouping convolutional neural network with adaptive feature enhanced network,GCNN-AFEN)。GCNN-AFEN模型的核心:首先,对PQDs信号进行S变换形成时频矩阵图像,利用CNN与结构稀疏的GCNN相结合作为特征学习的基础框架以减少模型参数,进而提高运算速度;然后,AEFN模块通过通道注意力机制、频域特征增强和软阈值去噪环节,自适应学习扰动类型与对应特征图的相关性,增加信噪比,突出能够代表扰动类别的深层特征;最后,通过全连接层(fully connected layers,FC)和Softmax分类器进行分类识别。仿真实验表明,提出的模型对于电能质量扰动信号具有较高的分类识别准确率和噪声鲁棒性,能够用于电能质量扰动的快速识别和分类。Distributed power supply can generate complex power quality disturbances(PQDs)when it is connected to the power grid.In order to improve the accuracy of PQDs signal classification and identification,the Grouping Convolutional Neural Network with Adaptive Feature Enhanced Network(GCNN-AFEN)is constructed.The core of GCNN-AFEN model is as follows:First,S transformation is performed on PQDs signals to form time-frequency matrix images,and CNN is combined with sparse GCNN as the basic framework of feature learning to reduce model parameters and improve the computing speed.Then,through the channel attention mechanism,frequency domain feature enhancement and soft threshold denoising,the AEFN module adaptively learns the correlation between disturbance types and corresponding feature graphs,increases the signal-to-noise ratio,and highlights the deep features that can represent disturbance categories.Finally,full connection layer(FC)and Softmax classifier are used for classification recognition.Simulation results show that the proposed model has high classification accuracy and noise robustness for all kinds of power quality disturbance signals,and can be used for fast identification and classification of power quality disturbances.

关 键 词：电能质量扰动 分组卷积 混洗卷积 通道注意力机制 自适应特征增强网络 

分 类 号：TM761[电气工程—电力系统及自动化]