Fault Diagnosis of Power Transformer Based on 1D-CNN-PSO-SVM
Aiming at the problem of poor generalization and low diagnostic accuracy of artificial feature extraction in the process of transformer fault diagnosis,a fault diagnosis model based on one-dimensional convolutional neural network(1D-CNN)and particle swarm optimization support vector machine(PSO-SVM)is proposed.Firstly,a 1D-CNN was constructed as a feature extractor,the original data of dissolved gas in transformer oil was used as input for training,and the deep abstract features with higher correlation with fault types were adaptively learned layer by layer.After the training was completed,the PSO-SVM with better classification performance was used to replace the Softmax classifier in the traditional 1D-CNN to realize the identification of transformer fault types.The simulation results show that after extracting features by 1D-CNN,the samples of different fault types have high discrimination.Using PSO-SVM to classify and recognize the extracted features,compared with using Softmax classifier,the diagnostic accuracy has been further improved,which verifies the effectiveness of the method proposed in this paper.
NETL
NSTL
万方数据
