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OA

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.