AFCIs—From a Standards Perspective

Lippert, Kevin J.; Domitrovich, Thomas A.

doi:10.1109/tia.2013.2272670

Cited by 14 publications

(3 citation statements)

References 1 publication

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“…Arc faults can raise the local temperature up to 5000 °C through gas discharge, resulting in severe electrical fires. In order to avoid arc fault, arc fault circuit breakers were required to be used in residential electrical installations in the United States in 2002 [2]. In 2013, the International Electrotechnical Commission (IEC) also proposed a protection technology called arc fault detection device (AFDD) [3].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Sheng

et al. 2022

IEEE Access

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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 4.2ms per sample, which Ensures that the tripping time can meet IEC 62606.INDEX TERMS Series arc fault protection, improved Mel-Frequency Ceptral Coefficients (MFCC), artificial intelligence, fully connected neural network

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Section: Introductionmentioning

confidence: 99%

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

Wang

Sheng

et al. 2022

IEEE Access

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“…These cases are equivalent to short circuit faults and the breaker is competent in protecting the circuit in time. However, the effective current value in the line is less than that during normal operations when arc faults occur, which are hard to identify [6,7]. Therefore, how to effectively identify series arc faults in the line is an urgent problem needing to be solved, which is of vital practical significance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Arc Fault Detection Method Integrated Random Forest, Improved Multi-scale Permutation Entropy and Wavelet Packet Transform

et al. 2019

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Arc faults are one of the important causes of electric fires. In order to solve the problem of randomness, diversity, the concealment of series arc faults and to improve the detection accuracy, a novel arc fault detection method integrated random forest (RF), improved multi-scale permutation entropy (IMPE) and wavelet packet transform (WPT) are designed. Firstly, singular value decomposition (SVD) was applied to filter the current signal and then the high-dimensional fault features were constructed by extracting IMPE, the wavelet packet energy and the wavelet packet energy-entropy. Afterward, the high-dimensional fault features were employed to train the RF to realize the arc fault detection of different load types and the experimental results verify the effectiveness of the arc fault detection method designed in this paper. Finally, the comparative experiments demonstrates that the RF shows better performance in arc fault detection compared to the back-propagation neural network (BPNN) and least squares support vector machines (LSSVM), and that the experiments of transient events indicate that RF is able to effectively avoid incorrectly detecting different load types during the start operations and stop operations.

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“…So, the arc fault circuit interrupters (AFCIs) became concerned for home fires caused by damage in the power cords [2]. The National Electrical Code (NEC) required installation of AFCIs on indoor low voltage power lines [3]. The AFCI is a safety device for low-voltage power lines to prevent fire accidents from arc faults.…”

Section: Introductionmentioning

confidence: 99%

Smart detection technology of serial arc fault on low-voltage indoor power lines

Liu

2015

International Journal of Electrical Power & Energy Systems

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AFCIs—From a Standards Perspective

Cited by 14 publications

References 1 publication

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

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

A Novel Arc Fault Detection Method Integrated Random Forest, Improved Multi-scale Permutation Entropy and Wavelet Packet Transform

Smart detection technology of serial arc fault on low-voltage indoor power lines

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