低压直流配电网强弱电弧特征及检测算法

doi:10.11930/j.issn.1004-9649.202302040

摘要/Abstract

摘要：

直流电弧没有过零点特性，难以自行熄灭，易引发电气火灾，威胁直流配用电安全。通过搭建电弧模拟发生实验平台，采集不同电压下电弧信号，研究电弧电阻、电压等特性，分析了不同电压下电弧特征的差异性。以GB/T 35727—2017中低压直流配网标称电压优选值为依据，研究220 V、750 V、1500 V 3种不同电压等级电弧检测特征，对比傅里叶、小波、奇异值分解等频域特性，最大值、最小值、平均值、标准差等时域特性，得到不同电压等级下电弧检测的优质判据，证明了不同时频域特征适用于不同的电压等级。结合神经网络模型，设计了适用于750 V系统的电弧检测算法，算法检测精度为99.12%。

关键词: 直流配电网, 电弧, 小波包, 奇异值, 时频域特性

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

刘国强, 李国春, 郝亚楠, 李贵海, 吴中杰. 低压直流配电网强弱电弧特征及检测算法[J]. 中国电力, 2024, 57(2): 94-102.

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.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302040

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

图/表 12

图 1 实验平台设计

Fig.1 Experimental platform design

图 2 电弧电压及电弧电流信号

Fig.2 Arc voltage and arc current signal

图 3 不同电流下电弧特性随电源电压变化曲线

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

图 4 不同电流下电弧电压随间距变化曲线

Fig.4 Arc voltage versus distance curve under different currents

图 5 整体流程

Fig.5 Overall flow chart

图 6 小波系数相关法去噪流程

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

图 7 不同电压等级电弧信号FFT频谱特征

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

图 8 各小波包节点能量谱占比

Fig.8 Energy spectrum proportion of each wavelet packet node

图 9 不同电压下正常与电弧信号SVD极差

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

表 1 时域特征下电弧检测准确率

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

表 2 不同电压等级下电弧检测优质特征

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%）

图 10 检测算法输出结果

Fig.10 Output result of detection algorithm

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