Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network

• Published in: Energies (Basel) 2024-07, Vol.17 (13), p.3227
• Main Authors: Zheng, Chen, Li, Qionglin, Liu, Shuming, Dai, Shuangyin, Zhang, Bo, Liu, Yajuan
• Format: Article
• Language: English
• Subjects: Classification; convolutional neural networks; Electricity distribution; feature fusion strategy; Fourier transforms; Identification; Neural networks; Power; power quality disturbance; Signal processing; transformer network; transient signal; Wavelet transforms
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17133227

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.