No abstract

No abstract

Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. In order to meet the data dimension requirements of neural networks, a color domain transformation method is used to transform the feature matrix into an image. A regularization method based on gamma transform is proposed for small sample data sets. The results showed that the proposed regularization method improved the validation set accuracy of ResNet50 from 66.67% to 96.53%. The overfitting problem of neural network was solved. In addition, this method fused fault features of 64 different scales, and provided a valuable manually labeled arc fault dataset. Compared with the threshold detection method, this method was more objective. The use of image features increased intuitiveness and generality. Compared with other typical lightweight networks, this method had the best detection performance.

Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. We use HSV color index to map the coefficient matrix to the phase space image. Random gamma transform and random rotation are applied to data enhancement. Finally, typical deep residual network (ResNet) is established for image recognition. Training results show that this method can detect faults in real time. The accuracy of ResNet50 is 96.53% by using the data set in this paper.

No abstract