中国电机工程学报2024，Vol.44Issue(7)：2531-2544,后插3,15.DOI:10.13334/j.0258-8013.pcsee.222644
基于特征图像组合与改进ResNet-18的电能质量扰动识别方法
Power Quality Disturbance Recognition Method Based on Feature Image Combination and Modified ResNet-18
摘要
Abstract
Aiming at the problems of limited single image feature information and insufficient algorithm recognition ability in traditional power quality disturbance(PQD)recognition schemes,a PQD recognition method based on feature image combination and modified ResNet-18 is proposed according to the idea of feature fusion.First,a series of intrinsic mode functions(IMFs)and residual components are obtained by variational mode decomposition(VMD)of PQD signals.Then,the IMFs,residual components,original disturbance signals and Subtract components are longitudinally spliced into component matrix,and the signal-image conversion method is used to generate the feature component color map.Meanwhile,continuous wavelet transform(CWT)is performed on the original disturbance signal to generate the wavelet time-frequency diagram.Finally,the feature component color map and wavelet time-frequency diagram are combinatorically input into the modified six-channel ResNet-18 training and the learning on how to recognize the PQD.The PQD recognition method is analyzed through simulation and compared with the commonly used recognition system.The results show that the proposed method has good anti-noise performance and can better extract the PQD feature information to achieve higher recognition accuracy.关键词
电能质量扰动/变分模态分解/特征分量彩色图/小波时-频图/残差网络Key words
power quality disturbance/variational mode decomposition/feature component color map/wavelet time-frequency diagram/ResNet分类
信息技术与安全科学引用本文复制引用
张逸,欧杰宇,金涛,毕贵红..基于特征图像组合与改进ResNet-18的电能质量扰动识别方法[J].中国电机工程学报,2024,44(7):2531-2544,后插3,15.基金项目
国家自然科学基金项目(51977039).Project Supported by National Natural Science Foundation of China(51977039). （51977039）
