A Novel Series Arc Fault Detection Method Based on Mel-Frequency Cepstral Coefficients and Fully Connected Neural Network
نویسندگان
چکیده
Arc faults pose challenges to electric safety, which can cause serious fire hazards. However, the commonly used arc fault detection method is prone nuisance tripping. This paper proposed a hybrid based on improved Mel-Frequency Ceptral Coefficients (MFCC) for preprocessing and neural network model identification called ARC_MFCC. As per IEC 62606, twelve different loads/scenarios are considered this research. An tangent-based core filter employed improve MFCC enhance features within bandwidth of 3 kHz 7 kHz. A lightweight fully connected cascaded with MFCC-based preprocessing, distinguish normal operation under test conditions. verification result, ARC_ achieve an accuracy 99.34%. Moreover, implemented by Raspberry pie 4B. Test results show average running time about 4.2ms sample, Ensures that tripping meet 62606.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3206547