南方电网技术2025，Vol.19Issue(2)：48-56,114,10.DOI:10.13648/j.cnki.issn1674-0629.2025.02.006
基于递归图和预训练迁移学习的电能质量扰动分类
Power Quality Disturbance Classification Based on Recursive Graph and Pre-Trained Transfer Learning
摘要
Abstract
Applications of deep learning methods in the classification task of power quality disturbances(PQDs)are becoming increas-ingly popular.Aiming at the limited availability of measured label data compared to the ability to generate simulated data in large batches,a disturbance classification method is proposed based on recursive graph theory and pre-trained transfer learning.Firstly,the PQDs signals are transformed into two-dimensional recursive images using the recursive graph algorithm.Then,a VGG-16 deep learning network is pre-trained using a large amount of simulated data,and the model's weight parameters are saved.Finally,through transfer learning method,the fully connected layer of the model is fine-tuned using a limited amount of measured data,enabling deep feature extraction and classification of PQDs signals under the constraint of limited training samples.Simulation and measured data validation demonstrate that the proposed method achieves high classification accuracy even with limited labeled data.关键词
电能质量扰动分类/递归图/预训练/迁移学习/时间序列分类/深度学习Key words
classification of power quality disturbances/recursive graph/pre-trained/transfer learning/time series classification/deep learning分类
动力与电气工程引用本文复制引用
王继东,王泽平,张迪..基于递归图和预训练迁移学习的电能质量扰动分类[J].南方电网技术,2025,19(2):48-56,114,10.基金项目
国家重点研发计划资助项目(2022YFB4200703). Supported by the National Key Research and Development Program of China(2022YFB4200703). （2022YFB4200703）
