Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

Abstract

The fast and accurate series arc fault (SAF) identification method and its hardware implementation are the key to the development of arc fault circuit interrupter (AFCI) or arc fault detection device (AFDD). The SAF experiments under household multi-branch circuit conditions were conducted. And a novel SAF identification model based on lightweight one-dimensional (1-D) convolutional neural network was proposed. First, the main-circuit current signal was used as the input of the model. The 1-D convolutional layers and 1-D maximum pooling layers of the model were used to extract the features of the current signal. The fully connected neural network (FCNN) was used to identify whether or not there is a SAF in the circuit and determine the branch-circuit where the fault is located. Second, the second to fourth standard convolutional layers of the model were improved by using depthwise separable convolution, and the batch normalization layers were added to the model, so as to realize the optimal design of the model. Finally, the model was deployed to an embedded device and its performance was tested. When the sampling frequency is higher than 5 kHz, the accuracy of fault identification and fault line selection of the model in the embedded device is higher than 98.05% and 99.11%, respectively. The average runtime of single identification is 5.26 ms. It meets the technical requirements of household AFCI or AFDD.

Keywords

• Depthwise separable convolution
• fault diagnosis
• fault line selection
• lightweight design
• series arc fault