基于特征图像组合与改进ResNet-18的电能质量扰动识别方法OA北大核心CSTPCD

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.