Power quality disturbance classification based on Res-CA model
Power quality perturbations(PQDs)can cause power equipment failures,resulting in wasted energy.Using traditional machine learning methods to identify various types of PQDs,it is often necessary to manually design different feature extractors or classifiers,which is time-consuming and laborious.The deep learning model can deal with multiple types of perturbations at the same time and has strong adaptability.In this paper,a Res-CA model for PQDs classification task is constructed by combining deep residu-al neural network ResNet18 with coordinate attention(CA)module.Firstly,the time embedding feature is extracted through ResNet18 backbone network.Then,CA module is used to capture more important deep temporal features.Finally,the PQDs signal type is recognized by Softmax classifier.The experimental results show that the Res-CA network can effectively classify PQDs signals under two SNR conditions of 20 dB and 30 dB,and the recognition accuracy is 99.41%and 99.78%respectively.
power quality perturbationsattention mechanismdeep residual neural networks
NETL
NSTL
万方数据
