A Series DC Arc Fault Detection Method Based on Steady Pattern of High-Frequency Electromagnetic Radiation

2019 ◽  
Vol 47 (9) ◽  
pp. 4370-4377 ◽  
Author(s):  
Shuangle Zhao ◽  
Yao Wang ◽  
Feng Niu ◽  
Chen Zhu ◽  
Youxin Xu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Electromagnetic Radiation ◽  
High Frequency ◽  
Detection Method ◽  
Dc Arc

A Novel DC Arc Fault Detection Method Based on Electromagnetic Radiation Signal

2017 ◽  
Vol 45 (3) ◽  
pp. 472-478 ◽  
Author(s):  
Qing Xiong ◽  
Shengchang Ji ◽  
Lingyu Zhu ◽  
Lipeng Zhong ◽  
Yuan Liu
Keyword(s):  
Fault Detection ◽  
Electromagnetic Radiation ◽  
Detection Method ◽  
Dc Arc

Series DC Arc Fault Detection Method

2019 ◽  
Author(s):  
Kerim Kaya ◽  
Okan Ozgonenel ◽  
Ataberk Najafi
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Dc Arc

scholarly journals A Method of DC Arc Detection in All-Electric Aircraft

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4190
Author(s):  
Teng Li ◽  
Zhijie Jiao ◽  
Lina Wang ◽  
Yong Mu
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Power Distribution ◽  
Distribution System ◽  
Detection Method ◽  
Discrete Wavelet ◽  
Power Distribution System ◽  
Time Frequency ◽  
Intelligent Recognition ◽  
Dc Arc

Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.

Series DC Arc Fault Detection Method for PV Systems Employing Differential Power Processing Structure

2021 ◽  
pp. 1-1
Author(s):  
Hwa-Pyeong Park ◽  
Mina Kim ◽  
Jeehoon Jung ◽  
Suyong Chae
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Pv Systems ◽  
Dc Arc

The DC Arc Fault Detection Method Taken Advantage of WT and MFE

2019 ◽  
Author(s):  
Zhendong Yin ◽  
Li Wang ◽  
Yaojia Zhang ◽  
Yang Gao ◽  
Shanshui Yang
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Dc Arc
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