电测与仪表2025，Vol.62Issue(11)：111-119,9.DOI:10.19753/j.issn1001-1390.2025.11.013
面向新型电力系统的电能质量扰动分类研究
Research on classification of power quality disturbance in novel power system
摘要
Abstract
Aiming at the problems of complex signal types and heterogeneous data of power quality disturbance in novel power system,a power quality disturbance classification method using Federated learning and prototype learn-ing is proposed.This method includes two types of work nodes:server and client.The server collects the local pro-totype output from the local model of clients.The local prototype cannot be reverse reconstructed to get the original data.Instead,the server aggregates the local prototype to get the global prototype and sends it back to the client to regularize the local model training.Compared with the convolutional neural network model,this method does not require a lot of training data,and the model is not vulnerable to slight heterogeneous data disturbance,and has strong robustness to unknown disturbance signals.The simulation experimental results show that,compared with ex-isting methods,the proposed method is suitable for small-scale power quality disturbance samples,with a classifica-tion accuracy of 0.998 3,which has high application value in the new distributed power grid environment.关键词
联邦学习/原型学习/电能质量扰动分类/异构数据Key words
Federated learning/prototype learning/classification of power quality disturbances/heterogeneous data分类
动力与电气工程引用本文复制引用
李琮琮,王清,荆臻,张志,王平欣,杨林林..面向新型电力系统的电能质量扰动分类研究[J].电测与仪表,2025,62(11):111-119,9.基金项目
国网山东省电力公司科技项目(520633220001) （520633220001）
