基于图卷积神经网络和格拉姆角场的电能质量扰动分类  

Power Quality Disturbance Classification Based on Graph Convolutional Neural Networks and Gramian Angular Field

作　　者：黄光磊 田启东 林志贤 郑炜楠 徐特 李冰然 HUANG Guanglei;TIAN Qidong;LIN Zhixian;ZHENG Weinan;XU Te;LI Bingran(Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen 310030,Guangdong,China;State Grid Jiangsu Electric Power Co.,Ltd.,Suzhou 215000,Jiangsu,China)

机构地区：[1]深圳供电局有限公司,广东深圳310030 [2]国家电网江苏省电力有限公司,江苏苏州215000

出　　处：《电气传动》2024年第3期84-90,共7页Electric Drive

基　　金：国网江苏省电力有限公司科技项目(J2019124)。

摘　　要：由于新能源系统的广泛加入,系统中的电能质量扰动数量和种类也相应增加,而传统电能质量扰动(PQD)分类方法存在准确率和效率不高的问题,难以适应现有包含高新能源渗透率的电力系统的电能质量管理。因此,提出了一种基于图卷积神经网络(GCNNs)和格拉姆角场(GAF)的电能质量扰动分类方法。首先,对原始的PQD信号进行归一化和极坐标转化处理;然后采用GAF对不同种类的PQD一维信号进行图形化转换,生成包含不同PQD特征的二维图片;最后,采用GCNNs对不同种类的PQD图片进行训练和分类,实现不同PQD的分类。实验部分采用IEEE-39节点系统仿真并模拟不同种类的PQD曲线,对所提方法进行验证。实验结果表明,所提方法可以自动地进行特征的提取和优化,满足PQD识别和分类的高效性和准确性。Due to the extensive addition of new energy systems,the number and types of power quality disturbances in the system are also increased accordingly.However,the traditional power quality disturbance(PQD)classification method has the problem of low accuracy and efficiency,and it is difficult to adapt to the existing power quality management of power systems with high new energy penetration.Therefore,a PQD classification method based on graph convolutional neural networks(GCNNs)and Gramian angular field(GAF)was proposed.First,the original PQD signal was normalized and polar coordinate transformation was processed,then GAF was used to graphically transform different kinds of PQD one-dimensional signals to generate twodimensional images containing different PQD features,and finally,GCNNs were used to train and classify the different kinds of PQD images to achieve the classification of different PQDs.In the experiment part,the IEEE-39 node system was used to simulate and simulate different types of PQD curves,and the method proposed was used for verification.The experiment results show that the proposed method can automatically extract and optimize the features,and meet the high efficiency and accuracy of PQD identification and classification.

关 键 词：电能质量扰动 图卷积神经网络 格拉姆角场 扰动分类 

分 类 号：TM346[电气工程—电机]