A Novel Series Arc Fault Detection Method Based on Mel-Frequency Cepstral Coefficients and Fully Connected Neural Network

Abstract

Arc faults pose challenges to electric safety, which can cause serious fire hazards. However, the commonly used arc fault detection method is prone to nuisance tripping. This paper proposed a hybrid arc fault detection method based on the improved Mel-Frequency Ceptral Coefficients (MFCC) for preprocessing and a neural network model for arc identification called ARC_MFCC. As per the IEC 62606, twelve different loads/scenarios are considered for this research. An arc tangent-based core filter is employed to improve the MFCC to enhance the arc features within a bandwidth of 3 kHz to 7 kHz. A lightweight neural network model of fully connected cascaded with the MFCC-based preprocessing, which can distinguish the arc fault with normal operation under different test conditions. As a verification result, the ARC_ MFCC can achieve an accuracy of 99.34%. Moreover, the proposed method is implemented by Raspberry pie 4B. Test results show an average running time of about 4.2 ms per sample, which Ensures that the tripping time can meet IEC 62606.

Keywords

• Series arc fault protection
• improved Mel-Frequency Ceptral Coefficients (MFCC)
• artificial intelligence
• fully connected neural network