Fault Line Selection Method Based on Transfer Learning Depthwise Separable Convolutional Neural Network

Haixia Zhang,Wenao Cheng,Yang Li

doi:10.1155/2021/9979634

Abstract

With the continuous development of artificial intelligence technology, the value of massive power data has been widely considered. Aiming at the problem of single-phase-to-ground fault line selection in resonant grounding system, a fault line selection method based on transfer learning depthwise separable convolutional neural network (DSCNN) is proposed. The proposed method uses two pixel-level image fusions to transform the three-phase current of each feeder into the RGB color image, which is used as the input of DSCNN. After DSCNN self-feature extraction, the fault line selection is completed. With the consideration that not all of power distribution systems can obtain a large amount of data in practical applications, the transfer learning strategy is adopted to transplant the trained line selection model. The smaller number of DSCNN parameters increases the portability of the model. The test results show that not only does the proposed method extracts obvious features, but also the line selection accuracy can reach 99.76%. It also has good adaptability under different sampling frequencies, different noise environments, and different distribution network topologies; the line selection accuracy can reach more than 97.43%.

Highlights

A series of fault detection methods are proposed for single-phase-to-ground faults, which are mainly divided into three categories: (1) fault transient signal based methods [7, 8], (2) fault steady-state signal based approaches [9,10,11], and (3) methods based on artificial intelligence technology [7, 12]. e first two methods have certain adoption limitations such as network structure, fault type, and signal noise
In [17], an approach of decomposing the three-phase fault current waveform by discrete wavelet transform, extracting features such as standard deviation and energy value, and using a naive Bayes classifier for fault identification is proposed. e features are extracted from the obtained voltage and current signals by wavelet transform or wavelet packet transform. e artificial neural network is trained by a large amount of data to achieve the goal of effectively identifying the fault feeder
In [21], a novel deep learning framework based on the graph convolutional neural networks (GCNs) was presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes

Summary

Propose the Method of Fault Line Selection

It can be seen from the above formula that the reduction of DSCNN calculation is related to the number of output channels k and the size of the convolution kernel. In view of the problem of single-phase-to-ground line selection of resonant grounding system, the characteristics of the fusion image obtained by the different power distribution systems learned by the DSCNN model are similar, but the task of line selection is different. E feature map extracted by the separable convolutional pooling layer in the DSCNN model represents the existence of common concepts in the image. E specific implementation steps are as follows. e classification module in the pretrained DSCNN network is replaced with a new classification module, which includes flatten layer, dense layer, and output layer. e parameters of the separable convolutional pool layer are frozen, which include four layers of SeparableConv2D and MaxPooling2D layers. e new classification modules are trained on small data sets to adapt them to new line selection tasks

System Modeling with Data Preparation

Results

Method

Conclusions