Characteristics and Detection Algorithm of Strong and Weak Arc in Low Voltage DC Distribution Network

doi:10.11930/j.issn.1004-9649.202302040

Abstract

Abstract:

With no zero-crossing characteristic, DC arc is difficult to extinguish by itself, and is inclined to cause electrical fire, threatening the safety of DC power distribution. The differences of arc characteristics under different voltages are analyzed by building an experimental platform for arc simulation, collecting arc signals under different voltages, and studying the characteristics of arc resistance and voltage. Based on the preferred value of the nominal voltage of the medium and low voltage DC distribution network in GB/T 35727—2017, the differences of the detected arc characteristics at three different voltage levels of 220 V, 750 V and 1500 V are studied, with the frequency domain characteristics such as Fourier transform, wavelet transform and singular value decomposition, as well as the time domain characteristics such as maximum, minimum, average and standard deviation compared, to obtain high-quality criteria for arc detection at different voltage levels, which proves that the frequency domain characteristics at different times are applicable to varied voltage levels. Combined with the neural network model, the arc detection algorithm suitable for 750 V system is designed, and the detection accuracy of the algorithm is 99.12%.

Key words: DC distribution network, electric arc, wavelet packet, singular value, time-frequency domain characteristics

Guoqiang LIU, Guochun LI, Yanan HAO, Guihai LI, Zhongjie WU. Characteristics and Detection Algorithm of Strong and Weak Arc in Low Voltage DC Distribution Network[J]. Electric Power, 2024, 57(2): 94-102.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202302040

https://www.electricpower.com.cn/EN/Y2024/V57/I2/94

Figures/Tables 12

Fig.1 Experimental platform design

Fig.2 Arc voltage and arc current signal

Fig.3 Curve of variations of arc characteristics with power supply voltage under different currents

Fig.4 Arc voltage versus distance curve under different currents

Fig.5 Overall flow chart

Fig.6 Flow chart of denoising via wavelet coefficient correlation method

Fig.7 FFT characteristics of arc signals at different voltage levels

Fig.8 Energy spectrum proportion of each wavelet packet node

Fig.9 Extreme difference of SVD between normal and arc signals at different voltage levels

Table 1 Arc detection accuracy under time domain characteristics

时域特征	准确率/%
时域特征	220 V	750 V	1500 V
最大值	36.79	32.12	28.33
最小值	50.21	48.63	56.74
平均值	51.14	55.46	48.61
极差	55.06	60.84	57.14
中位数	50.83	48.31	51.37
方差	59.86	64.87	67.16
标准差	66.53	71.17	65.94

Table 2 High quality criterion of arc detection at different voltage levels

电压等级/V	特征1	特征2	特征3
220	第1节点小波分解能量谱（88.26%）	SVD极差（75.45%）	标准差（66.53%）
750	第1节点小波分解能量谱（83.64%）	SVD极差（75.63%）	标准差（71.17%）
1500	第4节点小波分解能量谱（90.37%）	SVD极差（76.84%）	方差（67.16%）

Fig.10 Output result of detection algorithm

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