A lightweight power quality disturbance recognition model based on CNN and Transformer - ZHANG Bide; QIU Jie; LOU Guangxin; ZHOU Can; LUO Qingqing; LI Tianqian (电力工程技术)
A lightweight power quality disturbances (PQDs) recognition model that integrates convolutional neural network (CNN) and Transformer (CaT) is proposed to address the high number of parameters and computational complexity in existing deep learning-based models. Depthwise separable convolutions are first employed to extract local features from the disturbance signals. An efficient softthreshold block is then introduced to reduce noise and redundant features without significantly increasing the model′s parameters or complexity. The Transformer model is used to capture global features of the disturbance signals. Finally, pooling layers, fully connected layers, and Softmax are applied to complete the recognition PQDs. Simulation experiments demonstrate that the CaT model effectively recognizes PQDs with fewer parameters and floating point operations, achieving high accuracy and strong noise robustness. Its lightweight, end-to-end design also results in shorter inference times compared to other deep learning models.