中国电机工程学报2026，Vol.46Issue(8)：3118-3129,中插5,13.DOI:10.13334/j.0258-8013.pcsee.242877
融合时频自适应小波卷积网络的半监督电能质量扰动识别框架
Semi-supervised Power Quality Disturbance Identification Framework Integrated With Time-frequency Adaptive Wavelet Convolutional Network
徐玉珍 1高子奥 1刘宇龙 2陈煌滨 1金涛1
作者信息
- 1. 福州大学电气工程与自动化学院,福建省 福州市 350108
- 2. 北京大学能源研究院,北京市 海淀区 100871
- 折叠
摘要
Abstract
Traditional power quality disturbances(PQD)classification methods rely on a large amount of labeled data and perform poorly under multiple disturbance coupling and strong noise interference.To solve this problem,this paper proposes a semi-supervised PQD recognition framework that integrates a time-frequency adaptive wavelet network(TFAWNet).First,a teacher network TFAWNet based on wavelet convolution and multi-level attention mechanism is constructed and trained with a small amount of labeled data to obtain the optimal model.Subsequently,the trained and optimized teacher model is used to perform deep reasoning on unlabeled data to generate high-confidence pseudo labels,and the labeled data and pseudo-label data are combined to jointly train a lightweight student network EfficientNet.The experimental results show that with only 50 labeled samples for each type of disturbance,the student network has a test accuracy of 93.27%on the simulation data.After the model is deployed on the edge computing platform,it is verified by measured data that the accuracy is as high as 99.83%,and the average inference time is only 10 ms.These performances further verify the superior performance of the framework in simulation and practice,highlighting the robustness,practicality and efficiency of the model.关键词
半监督学习/电能质量扰动/小波卷积/时频特征融合/边缘计算Key words
semi-supervised learning/power quality disturbance/wavelet convolution/time-frequency feature fusion/edge computing分类
信息技术与安全科学引用本文复制引用
徐玉珍,高子奥,刘宇龙,陈煌滨,金涛..融合时频自适应小波卷积网络的半监督电能质量扰动识别框架[J].中国电机工程学报,2026,46(8):3118-3129,中插5,13.
