Series arc fault identification based on complete ensemble empirical mode decomposition with adaptive noise and convolutional neural network

Abstract

The effective identification of series arc faults is of considerable significance for preventing fires in residential buildings. Series arc fault currents and load currents have a similar waveform, and the fault features and nonfault features are superimposed on the current signal. Fault features are deeply hidden, making it difficult to identify them. This work proposes a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing and a one-dimensional convolutional neural network (1DCNN). The CEEMDAN algorithm is used to decompose the collected current signals. Then, the intrinsic mode function (IMF) components with no representational significance are eliminated by calculating the Spearman correlation coefficient before inputting it into the 1DCNN. The experimental results showed that the accuracy of the method for the measured load is 99.3%. Compared with the method that directly uses original current signals as model inputs, the recognition accuracy of the algorithm was significantly improved. Therefore, the proposed algorithm can be used for series arc fault identification in residential building power distribution systems.