计量学报2025，Vol.46Issue(5)：629-637,9.DOI:10.3969/j.issn.1000-1158.2025.05.02
基于Wave-ViT的改进多通道深度残差网络的电能质量扰动分类
Power Quality Disturbance Classification Based on Wave-ViT Improved Multi-channel Depth Residual Network
摘要
Abstract
A power quality disturbance classification method based on Wave-ViT improved multi-channel depth residual network is proposed.Firstly,the one-dimensional time series power quality disturbances(PQDS)signal is used as the input of channel one.Then,the one-dimensional PQDS signal is mapped into a two-dimensional image through the Gramian angular field(GAF)as the input of channel two,and the two-dimensional GAF image information is deeply mined by using the wave vision transformer(Wave-ViT)module as the input of channel three,and then the deep feature extraction of the three channels is carried out respectively,and a multi-channel network framework suitable for PQDS classification is constructed.Through ablation experiments,it is confirmed that multi-channel has complementary effect on the convergence speed and classification accuracy of the network.Further noise experiments and comparative experiments show that this method has strong feature extraction ability,less iterations,and good anti noise performance.The recognition rates of 16 kinds of disturbances in random noise and no noise environment can reach 99.81%and 99.19%respectively,which provides a new idea for power quality disturbance recognition.关键词
电磁计量/电能质量扰动/Wave-ViT/深度残差网络/消融实验/噪声实验/扰动识别Key words
electromagnetic metrology/power quality disturbance/Wave-ViT/depth residual network/ablation experiment/noise experiment/disturbance identification引用本文复制引用
刘大鹏,罗嘉宾,刘勇,穆勇,董彪,张淑清..基于Wave-ViT的改进多通道深度残差网络的电能质量扰动分类[J].计量学报,2025,46(5):629-637,9.基金项目
国家自然科学基金(52275067) （52275067）
河北省自然科学基金重点项目(F2020203058) （F2020203058）
国网冀北电力有限公司唐山供电公司项目(B30103230016) （B30103230016）
